Add code-modernization plugin

Structured workflow (assess → map → extract-rules → reimagine → transform →
harden) and specialist agents (legacy-analyst, business-rules-extractor,
architecture-critic, security-auditor, test-engineer) for modernizing legacy
codebases into current stacks.

.claude-plugin/marketplace.json

@@ -71,6 +71,19 @@
       },
       "homepage": "https://github.com/AikidoSec/aikido-claude-plugin"
     },
+    {
+      "name": "alloydb",
+      "description": "Create, connect, and interact with an AlloyDB for PostgreSQL database and data.",
+      "author": {
+        "name": "Google LLC"
+      },
+      "category": "database",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/gemini-cli-extensions/alloydb.git"
+      },
+      "homepage": "https://cloud.google.com/alloydb"
+    },
     {
       "name": "amazon-location-service",
       "description": "Guide developers through adding maps, places search, geocoding, routing, and other geospatial features with Amazon Location Service, including authentication setup, SDK integration, and best practices.",
@@ -95,6 +108,19 @@
       "category": "monitoring",
       "homepage": "https://github.com/amplitude/mcp-marketplace"
     },
+    {
+      "name": "apollo",
+      "description": "Prospect, enrich leads, load outreach sequences, and query sales analytics with Apollo.io — one-click MCP server integration for Claude Code and Cowork.",
+      "author": {
+        "name": "Apollo.io"
+      },
+      "category": "productivity",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/apolloio/apollo-mcp-plugin.git"
+      },
+      "homepage": "https://www.apollo.io/"
+    },
     {
       "name": "asana",
       "description": "Asana project management integration. Create and manage tasks, search projects, update assignments, track progress, and integrate your development workflow with Asana's work management platform.",
@@ -133,6 +159,19 @@
       },
       "homepage": "https://github.com/atlassian/atlassian-mcp-server"
     },
+    {
+      "name": "atlassian-forge-skills",
+      "description": "Forge-focused skill bundle and MCP tooling for Atlassian Forge: scaffold apps, review before deploy, debug production issues, and stay current on Forge APIs and the Atlassian Design System.",
+      "author": {
+        "name": "Atlassian Labs"
+      },
+      "category": "development",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/atlassian/forge-skills.git"
+      },
+      "homepage": "https://developer.atlassian.com"
+    },
     {
       "name": "atomic-agents",
       "description": "Comprehensive development workflow for building AI agents with the Atomic Agents framework. Includes specialized agents for schema design, architecture planning, code review, and tool development. Features guided workflows, progressive-disclosure skills, and best practice validation.",
@@ -147,6 +186,21 @@
         "community-managed"
       ]
     },
+    {
+      "name": "auth0",
+      "description": "Add authentication to any app with Auth0. This plugin detects your framework, scaffolds the right Auth0 SDK integration, and guides you through login, logout, sessions, and protected routes — using current SDK patterns.",
+      "author": {
+        "name": "Auth0"
+      },
+      "category": "security",
+      "source": {
+        "source": "git-subdir",
+        "url": "https://github.com/auth0/agent-skills.git",
+        "path": "plugins/auth0",
+        "ref": "main"
+      },
+      "homepage": "https://auth0.com/docs/quickstart/agent-skills"
+    },
     {
       "name": "autofix-bot",
       "description": "Code review agent that detects security vulnerabilities, code quality issues, and hardcoded secrets. Combines 5,000+ static analyzers to scan your code and dependencies for CVEs.",
@@ -181,6 +235,16 @@
       },
       "homepage": "https://github.com/awslabs/agent-plugins"
     },
+    {
+      "name": "azure",
+      "description": "Transform Claude into an Azure expert. This plugin integrates the Azure MCP server and specialized Azure skills to move beyond generic advice. It enables Claude to perform real-world tasks: listing resources, validating deployments, diagnosing infrastructure issues, and optimizing costs across 50+ Azure services.",
+      "category": "deployment",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/microsoft/azure-skills.git"
+      },
+      "homepage": "https://github.com/microsoft/azure-skills"
+    },
     {
       "name": "azure-cosmos-db-assistant",
       "source": {
@@ -192,16 +256,6 @@
       "category": "database",
       "homepage": "https://github.com/AzureCosmosDB/cosmosdb-claude-code-plugin"
     },
-    {
-      "name": "azure-skills",
-      "description": "Microsoft Azure MCP integration for cloud resource management, deployments, and Azure services. Manage your Azure infrastructure, monitor applications, and deploy resources directly from Claude Code.",
-      "category": "deployment",
-      "source": {
-        "source": "url",
-        "url": "https://github.com/microsoft/azure-skills.git"
-      },
-      "homepage": "https://github.com/microsoft/azure-skills"
-    },
     {
       "name": "base44",
       "description": "Build and deploy Base44 full-stack apps with CLI project management and JavaScript/TypeScript SDK development skills",
@@ -334,6 +388,19 @@
       "category": "productivity",
       "homepage": "https://github.com/anthropics/claude-plugins-official/tree/main/plugins/claude-md-management"
     },
+    {
+      "name": "cloud-sql-postgresql",
+      "description": "Create, connect, and interact with a Cloud SQL for PostgreSQL database and data.",
+      "author": {
+        "name": "Google LLC"
+      },
+      "category": "database",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/gemini-cli-extensions/cloud-sql-postgresql.git"
+      },
+      "homepage": "https://cloud.google.com/sql"
+    },
     {
       "name": "cloudflare",
       "source": {
@@ -368,6 +435,17 @@
       },
       "homepage": "https://github.com/cockroachdb/claude-plugin"
     },
+    {
+      "name": "code-modernization",
+      "description": "Modernize legacy codebases (COBOL, legacy Java/C++, monolith web apps) with a structured assess / map / extract-rules / reimagine / transform / harden workflow and specialist review agents",
+      "author": {
+        "name": "Anthropic",
+        "email": "support@anthropic.com"
+      },
+      "source": "./plugins/code-modernization",
+      "category": "development",
+      "homepage": "https://github.com/anthropics/claude-plugins-official/tree/main/plugins/code-modernization"
+    },
     {
       "name": "code-review",
       "description": "Automated code review for pull requests using multiple specialized agents with confidence-based scoring to filter false positives",
@@ -452,6 +530,19 @@
       },
       "homepage": "https://github.com/astronomer/agents"
     },
+    {
+      "name": "data-agent-kit-starter-pack",
+      "description": "Specialized suite of skills for data engineers on Google Cloud — architect data pipelines, transform data with dbt, write Spark and BigQuery SQL notebooks, and orchestrate end-to-end workflows across BigQuery, Spanner, BigLake, and Dataproc.",
+      "author": {
+        "name": "Google LLC"
+      },
+      "category": "development",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack.git"
+      },
+      "homepage": "https://cloud.google.com/bigquery"
+    },
     {
       "name": "data-engineering",
       "description": "Data engineering plugin - warehouse exploration, pipeline authoring, Airflow integration",
@@ -527,6 +618,20 @@
       },
       "homepage": "https://elixir-lsp.github.io/elixir-ls/"
     },
+    {
+      "name": "exa",
+      "description": "Exa AI web search, deep research, and content extraction. Provides MCP tools and research skills for comprehensive web search, people discovery, company research, academic papers, and more.",
+      "author": {
+        "name": "Exa"
+      },
+      "category": "productivity",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/exa-labs/exa-mcp-server.git",
+        "sha": "bd2ccdd52ca7a35fbc2207ad266bb2a961c0e793"
+      },
+      "homepage": "https://exa.ai/docs/reference/exa-mcp"
+    },
     {
       "name": "explanatory-output-style",
       "description": "Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)",
@@ -1247,6 +1352,33 @@
       },
       "homepage": "https://github.com/qodo-ai/qodo-skills.git"
     },
+    {
+      "name": "qt-development-skills",
+      "description": "Agentic engineering skills for Qt software development — Qt C++/QML code review, QML coding, and Qt C++/QML code documentation.",
+      "author": {
+        "name": "Qt Group"
+      },
+      "category": "development",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/TheQtCompanyRnD/agent-skills.git",
+        "sha": "62a98e2339e6eefcff108cfc3fe9db8a7301856c"
+      },
+      "homepage": "https://www.qt.io/"
+    },
+    {
+      "name": "quarkus-agent",
+      "description": "MCP server for AI coding agents to create, manage, and interact with Quarkus applications. Provides tools for project scaffolding, dev mode lifecycle, extension skills, Dev MCP proxy, and documentation search.",
+      "author": {
+        "name": "Quarkus"
+      },
+      "category": "development",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/quarkusio/quarkus-agent-mcp.git"
+      },
+      "homepage": "https://quarkus.io"
+    },
     {
       "name": "railway",
       "description": "Deploy and manage apps, databases, and infrastructure on Railway. Covers project setup, deploys, environment configuration, networking, troubleshooting, and monitoring.",
@@ -1688,6 +1820,20 @@
       },
       "homepage": "https://github.com/UI5/plugins-claude"
     },
+    {
+      "name": "vanta-mcp-plugin",
+      "description": "The Vanta plugin connects Claude Code to Vanta's security and compliance platform through the Vanta MCP server. It combines Vanta's test-specific remediation intelligence with your local repository context to help you fix compliance failures faster.",
+      "author": {
+        "name": "Vanta"
+      },
+      "category": "security",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/VantaInc/vanta-mcp-plugin.git",
+        "sha": "46e5bebf0484f08fc4a3c4054437cf5ec06298c9"
+      },
+      "homepage": "https://help.vanta.com/en/articles/14094979-connecting-to-vanta-mcp#h_887ce3f337"
+    },
     {
       "name": "vercel",
       "description": "Vercel deployment platform integration. Manage deployments, check build status, access logs, configure domains, and control your frontend infrastructure directly from Claude Code.",
@@ -1698,6 +1844,20 @@
       },
       "homepage": "https://github.com/vercel/vercel-plugin"
     },
+    {
+      "name": "versori-skills",
+      "description": "Skills for building data integrations using the Versori platform and versori-run SDK. Claude can bootstrap projects, configure systems and connections, generate type-safe TypeScript workflows, run local validation via Deno, and deploy to production — with a research-first approach that grounds code generation in gathered API documentation.",
+      "author": {
+        "name": "Versori"
+      },
+      "category": "development",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/versori/cli.git",
+        "sha": "134cf334c3065509eee39a5361fd0bcf969dc867"
+      },
+      "homepage": "https://docs.versori.com/latest/ai-tooling/overview"
+    },
     {
       "name": "voila-api",
       "description": "Definitive guide for the Voila API. Covers shipment creation (Manual/Smart Shipping), real-time tracking, detailed history, manifesting, collections, webhooks, and third-party integrations (Sorted, Peoplevox, Mintsoft, Veeqo, JD).",
@@ -1708,6 +1868,20 @@
       },
       "homepage": "https://github.com/TSedmanDC/Voila-API-Skill"
     },
+    {
+      "name": "windsor-ai",
+      "description": "Connect Claude Code to 325+ business data sources via Windsor.ai. Query marketing, sales, CRM, ecommerce, finance, and analytics data from Google Ads, Meta, HubSpot, Salesforce, Shopify, Stripe, and hundreds more — directly from your terminal.",
+      "author": {
+        "name": "Windsor.ai"
+      },
+      "category": "productivity",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/windsor-ai/claude-windsor-ai-plugin.git",
+        "sha": "248a6994b15b410cc025b105bb4ed5558e9b1af9"
+      },
+      "homepage": "https://windsor.ai"
+    },
     {
       "name": "wix",
       "description": "Build, manage, and deploy Wix sites and apps. CLI development skills for dashboard extensions, backend APIs, site widgets, and service plugins with the Wix Design System, plus MCP server for site management.",
@@ -1751,6 +1925,19 @@
         "url": "https://github.com/zoom/zoom-plugin.git"
       },
       "homepage": "https://developers.zoom.us/"
+    },
+    {
+      "name": "zscaler",
+      "description": "Manage Zscaler cloud security platform including ZPA (private access), ZIA (internet access), ZDX (digital experience), ZCC (client connector), EASM (attack surface), and Z-Insights (analytics). Create and manage policies, troubleshoot connectivity, audit security configurations, and investigate incidents across the full Zscaler ecosystem.",
+      "author": {
+        "name": "Zscaler"
+      },
+      "category": "security",
+      "source": {
+        "source": "url",
+        "url": "https://github.com/zscaler/zscaler-mcp-server.git"
+      },
+      "homepage": "https://github.com/zscaler/zscaler-mcp-server"
     }
   ]
 }

external_plugins/telegram/server.ts

@@ -284,6 +284,19 @@ function gate(ctx: Context): GateResult {
   return { action: 'drop' }
 }
 
+// Like gate() but for bot commands: no pairing side effects, just allow/drop.
+function dmCommandGate(ctx: Context): { access: Access; senderId: string } | null {
+  if (ctx.chat?.type !== 'private') return null
+  if (!ctx.from) return null
+  const senderId = String(ctx.from.id)
+  const access = loadAccess()
+  const pruned = pruneExpired(access)
+  if (pruned) saveAccess(access)
+  if (access.dmPolicy === 'disabled') return null
+  if (access.dmPolicy === 'allowlist' && !access.allowFrom.includes(senderId)) return null
+  return { access, senderId }
+}
+
 function isMentioned(ctx: Context, extraPatterns?: string[]): boolean {
   const entities = ctx.message?.entities ?? ctx.message?.caption_entities ?? []
   const text = ctx.message?.text ?? ctx.message?.caption ?? ''
@@ -669,12 +682,7 @@ setInterval(() => {
 // the gate's behavior for unrecognized groups.
 
 bot.command('start', async ctx => {
-  if (ctx.chat?.type !== 'private') return
-  const access = loadAccess()
-  if (access.dmPolicy === 'disabled') {
-    await ctx.reply(`This bot isn't accepting new connections.`)
-    return
-  }
+  if (!dmCommandGate(ctx)) return
   await ctx.reply(
     `This bot bridges Telegram to a Claude Code session.\n\n` +
     `To pair:\n` +
@@ -685,7 +693,7 @@ bot.command('start', async ctx => {
 })
 
 bot.command('help', async ctx => {
-  if (ctx.chat?.type !== 'private') return
+  if (!dmCommandGate(ctx)) return
   await ctx.reply(
     `Messages you send here route to a paired Claude Code session. ` +
     `Text and photos are forwarded; replies and reactions come back.\n\n` +
@@ -695,14 +703,12 @@ bot.command('help', async ctx => {
 })
 
 bot.command('status', async ctx => {
-  if (ctx.chat?.type !== 'private') return
-  const from = ctx.from
-  if (!from) return
-  const senderId = String(from.id)
-  const access = loadAccess()
+  const gated = dmCommandGate(ctx)
+  if (!gated) return
+  const { access, senderId } = gated
 
   if (access.allowFrom.includes(senderId)) {
-    const name = from.username ? `@${from.username}` : senderId
+    const name = ctx.from!.username ? `@${ctx.from!.username}` : senderId
     await ctx.reply(`Paired as ${name}.`)
     return
   }

plugins/code-modernization/.claude-plugin/plugin.json

@@ -0,0 +1,8 @@
+{
+  "name": "code-modernization",
+  "description": "Modernize legacy codebases (COBOL, legacy Java/C++, monolith web apps) with a structured assess → map → extract-rules → reimagine → transform → harden workflow and specialist review agents",
+  "author": {
+    "name": "Anthropic",
+    "email": "support@anthropic.com"
+  }
+}

plugins/code-modernization/LICENSE

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

plugins/code-modernization/README.md

@@ -0,0 +1,107 @@
+# Code Modernization Plugin
+
+A structured workflow and set of specialist agents for modernizing legacy codebases — COBOL, legacy Java/C++, monolith web apps — into current stacks while preserving behavior.
+
+## Overview
+
+Legacy modernization fails most often not because the target technology is wrong, but because teams skip steps: they transform code before understanding it, reimagine architecture before extracting business rules, or ship without a harness that would catch behavior drift. This plugin enforces a sequence:
+
+```
+assess → map → extract-rules → reimagine → transform → harden
+```
+
+Each step has a dedicated slash command. Specialist agents (legacy analyst, business rules extractor, architecture critic, security auditor, test engineer) are invoked from within those commands — or directly — to keep the work honest.
+
+## Commands
+
+The commands are designed to be run in order, but each produces a standalone artifact so you can stop, review, and resume.
+
+### `/modernize-brief`
+Capture the modernization brief: what's being modernized, why now, constraints (regulatory, data, runtime), non-goals, and success criteria. Produces `analysis/brief.md`. Run this first.
+
+### `/modernize-assess`
+Inventory the legacy codebase: languages, line counts, module boundaries, external integrations, build system, test coverage, known pain points. Produces `analysis/assessment.md`. Uses the `legacy-analyst` agent for deep reads on unfamiliar dialects.
+
+### `/modernize-map`
+Map the legacy structure onto a target architecture: which legacy modules become which target services/packages, data-flow diagrams, migration sequencing. Produces `analysis/map.md`. Uses the `architecture-critic` agent to pressure-test the design.
+
+### `/modernize-extract-rules`
+Extract business rules from the legacy code — the rules that are encoded in procedural logic, COBOL copybooks, stored procedures, or config files — into human-readable form with citations back to source. Produces `analysis/rules.md`. Uses the `business-rules-extractor` agent.
+
+### `/modernize-reimagine`
+Propose the target design: APIs, data model, runtime. Explicitly list what changes from legacy and what stays identical. Produces `analysis/design.md`. Uses the `architecture-critic` agent to challenge over-engineering.
+
+### `/modernize-transform`
+Do the actual code transformation — module by module. Writes to `modernized/`. Pairs each transformed module with a test suite that pins the pre-transform behavior.
+
+### `/modernize-harden`
+Post-transform review pass: security audit, test coverage, error handling, observability. Uses `security-auditor` and `test-engineer` agents. Produces a findings report ranked Blocker / High / Medium / Nit.
+
+## Agents
+
+- **`legacy-analyst`** — Reads legacy code (COBOL, legacy Java/C++, procedural PHP, classic ASP) and produces structured summaries. Good at spotting implicit dependencies, copybook inheritance, and "JOBOL" patterns (procedural code wearing a modern syntax).
+- **`business-rules-extractor`** — Extracts business rules from procedural code with source citations. Each rule includes: what, where it's implemented, which conditions fire it, and any corner cases hidden in data.
+- **`architecture-critic`** — Adversarial reviewer for target architectures and transformed code. Default stance is skeptical: asks "do we actually need this?" Flags microservices-for-the-resume, ceremonial error handling, abstractions with one implementation.
+- **`security-auditor`** — Reviews transformed code for auth, input validation, secret handling, and dependency CVEs. Tuned for the kinds of issues that appear when translating security primitives across stacks (e.g., session handling from servlet to stateless JWT).
+- **`test-engineer`** — Audits test suites for behavior-pinning vs. coverage-theater. Flags tests that exercise code paths without asserting outcomes.
+
+## Installation
+
+```
+/plugin install code-modernization@claude-plugins-official
+```
+
+## Recommended Workspace Setup
+
+This plugin ships commands and agents, but modernization projects benefit from a workspace permission layout that enforces the "never touch legacy, freely edit modernized" rule. A starting-point `.claude/settings.json` for the project directory you're modernizing:
+
+```json
+{
+  "permissions": {
+    "allow": [
+      "Bash(git diff:*)",
+      "Bash(git log:*)",
+      "Bash(git status:*)",
+      "Read(**)",
+      "Write(analysis/**)",
+      "Write(modernized/**)",
+      "Edit(analysis/**)",
+      "Edit(modernized/**)"
+    ],
+    "deny": [
+      "Edit(legacy/**)"
+    ]
+  }
+}
+```
+
+Adjust `legacy/` and `modernized/` to match your actual layout. The key invariants: `Edit` under `legacy/` is denied, and writes are scoped to `analysis/` (for documents) and `modernized/` (for the new code).
+
+## Typical Workflow
+
+```bash
+# 1. Write the brief — what are we modernizing and why?
+/modernize-brief
+
+# 2. Inventory the legacy code
+/modernize-assess
+
+# 3. Extract business rules before touching the code
+/modernize-extract-rules
+
+# 4. Map legacy structure to target
+/modernize-map
+
+# 5. Propose the target design and review it
+/modernize-reimagine
+
+# 6. Transform module by module
+/modernize-transform
+
+# 7. Harden: security, tests, observability
+/modernize-harden
+```
+
+## License
+
+Apache 2.0. See `LICENSE`.

plugins/code-modernization/agents/architecture-critic.md

@@ -0,0 +1,36 @@
+---
+name: architecture-critic
+description: Reviews proposed target architectures and transformed code against modern best practice. Adversarial — looks for over-engineering, missed requirements, and simpler alternatives.
+tools: Read, Glob, Grep, Bash
+---
+
+You are a principal engineer reviewing a modernization design or a freshly
+transformed module. Your default stance is **skeptical**. The team is excited
+about the new shiny; your job is to ask "do we actually need this?"
+
+## Review lens
+
+For **architecture proposals**:
+- Does every service boundary correspond to a real domain seam, or is this
+  microservices-for-the-resume?
+- What's the simplest design that meets the stated requirements? How does
+  the proposal compare?
+- Which non-functional requirements (latency, throughput, consistency) are
+  unstated, and does the design accidentally violate them?
+- What's the data migration story? "We'll figure it out" is a finding.
+- What happens when service X is down? Trace one failure mode end-to-end.
+
+For **transformed code**:
+- Is this idiomatic for the target stack, or is legacy structure leaking
+  through? (Flag "JOBOL" — procedural Java with COBOL variable names.)
+- Is error handling meaningful or ceremonial?
+- Are there abstractions with exactly one implementation and no second use
+  case in sight?
+- Does the test suite actually pin behavior, or just exercise code paths?
+- What would the on-call engineer need at 3am that isn't here?
+
+## Output
+
+Findings ranked **Blocker / High / Medium / Nit**. Each with: what, where,
+why it matters, and a concrete suggested change. End with one paragraph:
+"If I could only change one thing, it would be ___."

plugins/code-modernization/agents/business-rules-extractor.md

@@ -0,0 +1,46 @@
+---
+name: business-rules-extractor
+description: Mines domain logic, calculations, validations, and policies from legacy code into testable Given/When/Then specifications. Use when you need to separate "what the business requires" from "how the old code happened to implement it."
+tools: Read, Glob, Grep, Bash
+---
+
+You are a business analyst who reads code. Your job is to find the **rules**
+hidden inside legacy systems — the calculations, thresholds, eligibility
+checks, and policies that define how the business actually operates — and
+express them in a form that survives the rewrite.
+
+## What counts as a business rule
+
+- **Calculations**: interest, fees, taxes, discounts, scores, aggregates
+- **Validations**: required fields, format checks, range limits, cross-field
+- **Eligibility / authorization**: who can do what, when, under which conditions
+- **State transitions**: status lifecycles, what triggers each transition
+- **Policies**: retention periods, retry limits, cutoff times, rounding rules
+
+## What does NOT count
+
+Infrastructure, logging, error handling, UI layout, technical retries,
+connection pooling. If a rule would be the same regardless of what language
+the system was written in, it's a business rule. If it only exists because
+of the technology, skip it.
+
+## Extraction discipline
+
+1. Find the rule in code. Record exact `file:line-line`.
+2. State it in plain English a non-engineer would recognize.
+3. Encode it as Given/When/Then with **concrete values**:
+   ```
+   Given an account with balance $1,250.00 and APR 18.5%
+   When the monthly interest batch runs
+   Then the interest charged is $19.27 (balance × APR ÷ 12, rounded half-up to cents)
+   ```
+4. List the parameters (rates, limits, magic numbers) with their current
+   hardcoded values — these often need to become configuration.
+5. Rate your confidence: **High** (logic is explicit), **Medium** (inferred
+   from structure/names), **Low** (ambiguous; needs SME).
+6. If confidence < High, write the exact question an SME must answer.
+
+## Output format
+
+One "Rule Card" per rule (see the format in the modernize:extract-rules
+command). Group by category. Lead with a summary table.

plugins/code-modernization/agents/legacy-analyst.md

@@ -0,0 +1,39 @@
+---
+name: legacy-analyst
+description: Deep-reads legacy codebases (COBOL, Java, .NET, Node, anything) to build structural and behavioral understanding. Use for discovery, dependency mapping, dead-code detection, and "what does this system actually do" questions.
+tools: Read, Glob, Grep, Bash
+---
+
+You are a senior legacy systems analyst with 20 years of experience reading
+code nobody else wants to read — COBOL, JCL, RPG, classic ASP, EJB 2,
+Struts 1, raw servlets, Perl CGI.
+
+Your job is **understanding, not judgment**. The code in front of you kept a
+business running for decades. Treat it with respect, figure out what it does,
+and explain it in terms a modern engineer can act on.
+
+## How you work
+
+- **Read before you grep.** Open the entry points (main programs, JCL jobs,
+  controllers, routes) and trace the actual flow. Pattern-matching on names
+  lies; control flow doesn't.
+- **Cite everything.** Every claim gets a `path/to/file:line` reference.
+  If you can't point to a line, you don't know it — say so.
+- **Distinguish "is" from "appears to be."** When you're inferring intent
+  from structure, flag it: "appears to handle X (inferred from variable
+  names; no comments confirm)."
+- **Use the right vocabulary for the stack.** COBOL has paragraphs,
+  copybooks, and FD entries. CICS has transactions and BMS maps. JCL has
+  steps and DD statements. Java has packages and beans. Use the native
+  terms so SMEs trust your output.
+- **Find the data first.** In legacy systems, the data structures (copybooks,
+  DDL, schemas) are usually more stable and truthful than the procedural
+  code. Map the data, then map who touches it.
+- **Note what's missing.** Unhandled error paths, TODO comments, commented-out
+  blocks, magic numbers — these are signals about history and risk.
+
+## Output format
+
+Default to structured markdown: tables for inventories, Mermaid for graphs,
+bullet lists for findings. Always include a "Confidence & Gaps" footer
+listing what you couldn't determine and what you'd ask an SME.

plugins/code-modernization/agents/security-auditor.md

@@ -0,0 +1,47 @@
+---
+name: security-auditor
+description: Adversarial security reviewer — OWASP Top 10, CWE, dependency CVEs, secrets, injection. Use for security debt scanning and pre-modernization hardening.
+tools: Read, Glob, Grep, Bash
+---
+
+You are an application security engineer performing an adversarial review.
+Assume the code is hostile until proven otherwise. Your job is to find
+vulnerabilities a real attacker would find — and explain them in terms an
+engineer can fix.
+
+## Coverage checklist
+
+Work through systematically:
+- **Injection** (SQL, NoSQL, OS command, LDAP, XPath, template) — trace every
+  user-controlled input to every sink
+- **Authentication / session** — hardcoded creds, weak session handling,
+  missing auth checks on sensitive routes
+- **Sensitive data exposure** — secrets in source, weak crypto, PII in logs
+- **Access control** — IDOR, missing ownership checks, privilege escalation paths
+- **XSS / CSRF** — unescaped output, missing tokens
+- **Insecure deserialization** — pickle/yaml.load/ObjectInputStream on
+  untrusted data
+- **Vulnerable dependencies** — run `npm audit` / `pip-audit` /
+  read manifests and flag versions with known CVEs
+- **SSRF / path traversal / open redirect**
+- **Security misconfiguration** — debug mode, verbose errors, default creds
+
+## Tooling
+
+Use available SAST where it helps (npm audit, pip-audit, grep for known-bad
+patterns) but **read the code** — tools miss logic flaws. Show tool output
+verbatim, then add your manual findings.
+
+## Reporting standard
+
+For each finding:
+| Field | Content |
+|---|---|
+| **ID** | SEC-NNN |
+| **CWE** | CWE-XXX with name |
+| **Severity** | Critical / High / Medium / Low (CVSS-ish reasoning) |
+| **Location** | `file:line` |
+| **Exploit scenario** | One sentence: how an attacker uses this |
+| **Fix** | Concrete code-level remediation |
+
+No hand-waving. If you can't write the exploit scenario, downgrade severity.

plugins/code-modernization/agents/test-engineer.md

@@ -0,0 +1,36 @@
+---
+name: test-engineer
+description: Writes characterization, contract, and equivalence tests that pin down legacy behavior so transformation can be proven correct. Use before any rewrite.
+tools: Read, Write, Edit, Glob, Grep, Bash
+---
+
+You are a test engineer specializing in **characterization testing** —
+writing tests that capture what legacy code *actually does* (not what
+someone thinks it should do) so that a rewrite can be proven equivalent.
+
+## Principles
+
+- **The legacy code is the oracle.** If the legacy computes 19.27 and the
+  spec says 19.28, the test asserts 19.27 and you flag the discrepancy
+  separately. We're proving equivalence first; fixing bugs is a separate
+  decision.
+- **Concrete over abstract.** Every test has literal input values and literal
+  expected outputs. No "should calculate correctly" — instead "given balance
+  1250.00 and APR 18.5%, returns 19.27".
+- **Cover the edges the legacy covers.** Read the legacy code's branches.
+  Every IF/EVALUATE/switch arm gets at least one test case. Boundary values
+  (zero, negative, max, empty) get explicit cases.
+- **Tests must run against BOTH.** Structure tests so the same inputs can be
+  fed to the legacy implementation (or a recorded trace of it) and the modern
+  one. The test harness compares.
+- **Executable, not aspirational.** Tests compile and run from day one.
+  Behaviors not yet implemented in the target are marked
+  `@Disabled("pending RULE-NNN")` / `@pytest.mark.skip` / `it.todo()` — never
+  deleted.
+
+## Output
+
+Idiomatic tests for the requested target stack (JUnit 5 / pytest / Vitest /
+xUnit), one test class/file per legacy module, test method names that read
+as specifications. Include a `README.md` in the test directory explaining
+how to run them and how to add a new case.

plugins/code-modernization/commands/modernize-assess.md

@@ -0,0 +1,142 @@
+---
+description: Full discovery & portfolio analysis of a legacy system — inventory, complexity, debt, effort estimation
+argument-hint: <system-dir> | --portfolio <parent-dir>
+---
+
+**Mode select.** If `$ARGUMENTS` starts with `--portfolio`, run **Portfolio
+mode** against the directory that follows. Otherwise run **Single-system
+mode** against `legacy/$1`.
+
+---
+
+# Portfolio mode (`--portfolio <parent-dir>`)
+
+Sweep every immediate subdirectory of the parent dir and produce a
+heat-map a steering committee can use to sequence a multi-year program.
+
+## Step P1 — Per-system metrics
+
+For each subdirectory `<sys>`:
+
+```bash
+cloc --quiet --csv <parent>/<sys>          # LOC by language
+lizard -s cyclomatic_complexity <parent>/<sys> 2>/dev/null | tail -1
+```
+
+Capture: total SLOC, dominant language, file count, mean & max
+cyclomatic complexity (CCN). For dependency freshness, locate the
+manifest (`package.json`, `pom.xml`, `*.csproj`, `requirements*.txt`,
+copybook dir) and note its age / pinned-version count.
+
+## Step P2 — COCOMO-II effort
+
+Compute person-months per system using COCOMO-II basic:
+`PM = 2.94 × (KSLOC)^1.10` (nominal scale factors). Show the formula and
+inputs so the figure is defensible, not a guess.
+
+## Step P3 — Documentation coverage
+
+For each system, count source files with vs without a header comment
+block, and list architecture docs present (`README`, `docs/`, ADRs).
+Report coverage % and the top undocumented subsystems.
+
+## Step P4 — Render the heat-map
+
+Write `analysis/portfolio.html` (dark `#1e1e1e` bg, `#d4d4d4` text,
+`#cc785c` accent, system-ui font, all CSS inline). One row per system;
+columns: **System · Lang · KSLOC · Files · Mean CCN · Max CCN · Dep
+Freshness · Doc Coverage % · COCOMO PM · Risk**. Color-grade the PM and
+Risk cells (green→amber→red). Below the table, a 2-3 sentence
+sequencing recommendation: which system first and why.
+
+Then stop. Tell the user to open `analysis/portfolio.html`.
+
+---
+
+# Single-system mode
+
+Perform a complete **modernization assessment** of `legacy/$1`.
+
+This is the discovery phase — the goal is a fact-grounded executive brief that
+a VP of Engineering could take into a budget meeting. Work in this order:
+
+## Step 1 — Quantitative inventory
+
+Run and show the output of:
+```bash
+scc legacy/$1
+```
+Then run `scc --by-file -s complexity legacy/$1 | head -25` to identify the
+highest-complexity files. Capture the COCOMO effort/cost estimate scc provides.
+
+## Step 2 — Technology fingerprint
+
+Identify, with file evidence:
+- Languages, frameworks, and runtime versions in use
+- Build system and dependency manifest locations
+- Data stores (schemas, copybooks, DDL, ORM configs)
+- Integration points (queues, APIs, batch interfaces, screen maps)
+- Test presence and approximate coverage signal
+
+## Step 3 — Parallel deep analysis
+
+Spawn three subagents **concurrently** using the Task tool:
+
+1. **legacy-analyst** — "Build a structural map of legacy/$1: what are the
+   5-10 major functional domains, which source files belong to each, and how
+   do they depend on each other? Return a markdown table + a Mermaid
+   `graph TD` of domain-level dependencies. Cite file paths."
+
+2. **legacy-analyst** — "Identify technical debt in legacy/$1: dead code,
+   deprecated APIs, copy-paste duplication, god objects/programs, missing
+   error handling, hardcoded config. Return the top 10 findings ranked by
+   remediation value, each with file:line evidence."
+
+3. **security-auditor** — "Scan legacy/$1 for security vulnerabilities:
+   injection, auth weaknesses, hardcoded secrets, vulnerable dependencies,
+   missing input validation. Return findings in CWE-tagged table form with
+   file:line evidence and severity."
+
+Wait for all three. Synthesize their findings.
+
+## Step 4 — Production runtime overlay (observability)
+
+If the system has batch jobs (e.g. JCL members under `app/jcl/`), call the
+`observability` MCP tool `get_batch_runtimes` for each business-relevant
+job name (interest, posting, statement, reporting). Use the returned
+p50/p95/p99 and 90-day series to:
+
+- Tag each functional domain from Step 3 with its production wall-clock
+  cost and **p99 variance** (p99/p50 ratio).
+- Flag the highest-variance domain as the highest operational risk —
+  this is telemetry-grounded, not a static-analysis opinion.
+
+Include a small **Batch Runtime** table (Job · Domain · p50 · p95 · p99 ·
+p99/p50) in the assessment.
+
+## Step 5 — Documentation gap analysis
+
+Compare what the code *does* against what README/docs/comments *say*. List
+the top 5 undocumented behaviors or subsystems that a new engineer would
+need explained.
+
+## Step 6 — Write the assessment
+
+Create `analysis/$1/ASSESSMENT.md` with these sections:
+- **Executive Summary** (3-4 sentences: what it is, how big, how risky, headline recommendation)
+- **System Inventory** (the scc table + tech fingerprint)
+- **Architecture-at-a-Glance** (the domain table; reference the diagram)
+- **Production Runtime Profile** (the batch-runtime table from Step 4, with the highest-variance domain called out)
+- **Technical Debt** (top 10, ranked)
+- **Security Findings** (CWE table)
+- **Documentation Gaps** (top 5)
+- **Effort Estimation** (COCOMO-derived person-months, ±range, key cost drivers)
+- **Recommended Modernization Pattern** (one of: Rehost / Replatform / Refactor / Rearchitect / Rebuild / Replace — with one-paragraph rationale)
+
+Also create `analysis/$1/ARCHITECTURE.mmd` containing the Mermaid domain
+dependency diagram from the legacy-analyst.
+
+## Step 7 — Present
+
+Tell the user the assessment is ready and suggest:
+`glow -p analysis/$1/ASSESSMENT.md`

plugins/code-modernization/commands/modernize-brief.md

@@ -0,0 +1,60 @@
+---
+description: Generate a phased Modernization Brief — the approved plan that transformation agents will execute against
+argument-hint: <system-dir> [target-stack]
+---
+
+Synthesize everything in `analysis/$1/` into a **Modernization Brief** — the
+single document a steering committee approves and engineering executes.
+
+Target stack: `$2` (if blank, recommend one based on the assessment findings).
+
+Read `analysis/$1/ASSESSMENT.md`, `TOPOLOGY.md`, and `BUSINESS_RULES.md` first.
+If any are missing, say so and stop.
+
+## The Brief
+
+Write `analysis/$1/MODERNIZATION_BRIEF.md`:
+
+### 1. Objective
+One paragraph: from what, to what, why now.
+
+### 2. Target Architecture
+Mermaid C4 Container diagram of the *end state*. Name every service, data
+store, and integration. Below it, a table mapping legacy component → target
+component(s).
+
+### 3. Phased Sequence
+Break the work into 3-6 phases using **strangler-fig ordering** — lowest-risk,
+fewest-dependencies first. For each phase:
+- Scope (which legacy modules, which target services)
+- Entry criteria (what must be true to start)
+- Exit criteria (what tests/metrics prove it's done)
+- Estimated effort (person-weeks, derived from COCOMO + complexity data)
+- Risk level + top 2 risks + mitigation
+
+Render the phases as a Mermaid `gantt` chart.
+
+### 4. Behavior Contract
+List the **P0 behaviors** from BUSINESS_RULES.md that MUST be proven
+equivalent before any phase ships. These become the regression suite.
+
+### 5. Validation Strategy
+State which combination applies: characterization tests, contract tests,
+parallel-run / dual-execution diff, property-based tests, manual UAT.
+Justify per phase.
+
+### 6. Open Questions
+Anything requiring human/SME decision before Phase 1 starts. Each as a
+checkbox the approver must tick.
+
+### 7. Approval Block
+```
+Approved by: ________________  Date: __________
+Approval covers: Phase 1 only | Full plan
+```
+
+## Present
+
+Enter **plan mode** and present a summary of the brief. Do NOT proceed to any
+transformation until the user explicitly approves. This gate is the
+human-in-the-loop control point.

plugins/code-modernization/commands/modernize-extract-rules.md

@@ -0,0 +1,68 @@
+---
+description: Mine business logic from legacy code into testable, human-readable rule specifications
+argument-hint: <system-dir> [module-pattern]
+---
+
+Extract the **business rules** embedded in `legacy/$1` into a structured,
+testable specification — the institutional knowledge that's currently locked
+in code and in the heads of engineers who are about to retire.
+
+Scope: if a module pattern was given (`$2`), focus there; otherwise cover the
+entire system. Either way, prioritize calculation, validation, eligibility,
+and state-transition logic over plumbing.
+
+## Method
+
+Spawn **three business-rules-extractor subagents in parallel**, each assigned
+a different lens. If `$2` is non-empty, include "focusing on files matching
+$2" in each prompt.
+
+1. **Calculations** — "Find every formula, rate, threshold, and computed value
+   in legacy/$1. For each: what does it compute, what are the inputs, what is
+   the exact formula/algorithm, where is it implemented (file:line), and what
+   edge cases does the code handle?"
+
+2. **Validations & eligibility** — "Find every business validation, eligibility
+   check, and guard condition in legacy/$1. For each: what is being checked,
+   what happens on pass/fail, where is it (file:line)?"
+
+3. **State & lifecycle** — "Find every status field, state machine, and
+   lifecycle transition in legacy/$1. For each entity: what states exist,
+   what triggers transitions, what side-effects fire?"
+
+## Synthesize
+
+Merge the three result sets. Deduplicate. For each distinct rule, write a
+**Rule Card** in this exact format:
+
+```
+### RULE-NNN: <plain-English name>
+**Category:** Calculation | Validation | Lifecycle | Policy
+**Source:** `path/to/file.ext:line-line`
+**Plain English:** One sentence a business analyst would recognize.
+**Specification:**
+  Given <precondition>
+  When  <trigger>
+  Then  <outcome>
+  [And  <additional outcome>]
+**Parameters:** <constants, rates, thresholds with their current values>
+**Edge cases handled:** <list>
+**Confidence:** High | Medium | Low — <why>
+```
+
+Write all rule cards to `analysis/$1/BUSINESS_RULES.md` with:
+- A summary table at top (ID, name, category, source, confidence)
+- Rule cards grouped by category
+- A final **"Rules requiring SME confirmation"** section listing every
+  Medium/Low confidence rule with the specific question a human needs to answer
+
+## Generate the DTO catalog
+
+As a companion, create `analysis/$1/DATA_OBJECTS.md` cataloging the core
+data transfer objects / records / entities: name, fields with types, which
+rules consume/produce them, source location.
+
+## Present
+
+Report: total rules found, breakdown by category, count needing SME review.
+Suggest: `glow -p analysis/$1/BUSINESS_RULES.md`

plugins/code-modernization/commands/modernize-harden.md

@@ -0,0 +1,46 @@
+---
+description: Security vulnerability scan + remediation — OWASP, CVE, secrets, injection
+argument-hint: <system-dir>
+---
+
+Run a **security hardening pass** on `legacy/$1`: find vulnerabilities, rank
+them, and fix the critical ones.
+
+## Scan
+
+Spawn the **security-auditor** subagent:
+
+"Adversarially audit legacy/$1 for security vulnerabilities. Cover:
+OWASP Top 10 (injection, broken auth, XSS, SSRF, etc.), hardcoded secrets,
+vulnerable dependency versions (check package manifests against known CVEs),
+missing input validation, insecure deserialization, path traversal.
+For each finding return: CWE ID, severity (Critical/High/Med/Low), file:line,
+one-sentence exploit scenario, and recommended fix. Also run any available
+SAST tooling (npm audit, pip-audit, OWASP dependency-check) and include
+its raw output."
+
+## Triage
+
+Write `analysis/$1/SECURITY_FINDINGS.md`:
+- Summary scorecard (count by severity, top CWE categories)
+- Findings table sorted by severity
+- Dependency CVE table (package, installed version, CVE, fixed version)
+
+## Remediate
+
+For each **Critical** and **High** finding, fix it directly in the source.
+Make minimal, targeted changes. After each fix, add a one-line entry under
+"Remediation Log" in SECURITY_FINDINGS.md: finding ID → commit-style summary
+of what changed.
+
+Show the cumulative diff:
+```bash
+git -C legacy/$1 diff
+```
+
+## Verify
+
+Re-run the security-auditor against the patched code to confirm the
+Critical/High findings are resolved. Update the scorecard with before/after.
+
+Suggest: `glow -p analysis/$1/SECURITY_FINDINGS.md`

plugins/code-modernization/commands/modernize-map.md

@@ -0,0 +1,66 @@
+---
+description: Dependency & topology mapping — call graphs, data lineage, batch flows, rendered as navigable diagrams
+argument-hint: <system-dir>
+---
+
+Build a **dependency and topology map** of `legacy/$1` and render it visually.
+
+The assessment gave us domains. Now go one level deeper: how do the *pieces*
+connect? This is the map an engineer needs before touching anything.
+
+## What to produce
+
+Write a one-off analysis script (Python or shell — your choice) that parses
+the source under `legacy/$1` and extracts:
+
+- **Program/module call graph** — who calls whom (for COBOL: `CALL` statements
+  and CICS `LINK`/`XCTL`; for Java: class-level imports/invocations; for Node:
+  `require`/`import`)
+- **Data dependency graph** — which programs read/write which data stores
+  (COBOL: copybooks + VSAM/DB2 in JCL DD statements; Java: JPA entities/tables;
+  Node: model files)
+- **Entry points** — batch jobs, transaction IDs, HTTP routes, CLI commands
+- **Dead-end candidates** — modules with no inbound edges (potential dead code)
+
+Save the script as `analysis/$1/extract_topology.py` (or `.sh`) so it can be
+re-run and audited. Run it. Show the raw output.
+
+## Render
+
+From the extracted data, generate **three Mermaid diagrams** and write them
+to `analysis/$1/TOPOLOGY.html` so the artifact pane renders them live.
+
+The HTML page must use: dark `#1e1e1e` background, `#d4d4d4` text,
+`#cc785c` for `<h2>`/accents, `system-ui` font, all CSS **inline** (no
+external stylesheets). Each diagram goes in a
+`<pre class="mermaid">...</pre>` block — the artifact server loads
+mermaid.js and renders client-side. Do **not** wrap diagrams in
+markdown ` ``` ` fences inside the HTML.
+
+1. **`graph TD` — Module call graph.** Cluster by domain (use `subgraph`).
+   Highlight entry points in a distinct style. Cap at ~40 nodes — if larger,
+   show domain-level with one expanded domain.
+
+2. **`graph LR` — Data lineage.** Programs → data stores.
+   Mark read vs write edges.
+
+3. **`flowchart TD` — Critical path.** Trace ONE end-to-end business flow
+   (e.g., "monthly billing run" or "process payment") through every program
+   and data store it touches, in execution order. If the `observability`
+   MCP server is connected, annotate each batch step with its p50/p99
+   wall-clock from `get_batch_runtimes`.
+
+Also export the three diagrams as standalone `.mmd` files for re-use:
+`analysis/$1/call-graph.mmd`, `analysis/$1/data-lineage.mmd`,
+`analysis/$1/critical-path.mmd`.
+
+## Annotate
+
+Below each `<pre class="mermaid">` block in TOPOLOGY.html, add a `<ul>`
+with 3-5 **architect observations**: tight coupling clusters, single
+points of failure, candidates for service extraction, data stores
+touched by too many writers.
+
+## Present
+
+Tell the user to open `analysis/$1/TOPOLOGY.html` in the artifact pane.

plugins/code-modernization/commands/modernize-reimagine.md

@@ -0,0 +1,82 @@
+---
+description: Multi-agent greenfield rebuild — extract specs from legacy, design AI-native, scaffold & validate with HITL
+argument-hint: <system-dir> <target-vision>
+---
+
+**Reimagine** `legacy/$1` as: $2
+
+This is not a port — it's a rebuild from extracted intent. The legacy system
+becomes the *specification source*, not the structural template. This command
+orchestrates a multi-agent team with explicit human checkpoints.
+
+## Phase A — Specification mining (parallel agents)
+
+Spawn concurrently and show the user that all three are running:
+
+1. **business-rules-extractor** — "Extract every business rule from legacy/$1
+   into Given/When/Then form. Output to a structured list I can parse."
+
+2. **legacy-analyst** — "Catalog every external interface of legacy/$1:
+   inbound (screens, APIs, batch triggers, queues) and outbound (reports,
+   files, downstream calls, DB writes). For each: name, direction, payload
+   shape, frequency/SLA if discernible."
+
+3. **legacy-analyst** — "Identify the core domain entities in legacy/$1 and
+   their relationships. Return as an entity list + Mermaid erDiagram."
+
+Collect results. Write `analysis/$1/AI_NATIVE_SPEC.md` containing:
+- **Capabilities** (what the system must do — derived from rules + interfaces)
+- **Domain Model** (entities + erDiagram)
+- **Interface Contracts** (each external interface as an OpenAPI fragment or
+  AsyncAPI fragment)
+- **Non-functional requirements** inferred from legacy (batch windows, volumes)
+- **Behavior Contract** (the Given/When/Then rules — these are the acceptance tests)
+
+## Phase B — HITL checkpoint #1
+
+Present the spec summary. Ask the user **one focused question**: "Which of
+these capabilities are P0 for the reimagined system, and are there any we
+should deliberately drop?" Wait for the answer. Record it in the spec.
+
+## Phase C — Architecture (single agent, then critique)
+
+Design the target architecture for "$2":
+- Mermaid C4 Container diagram
+- Service boundaries with rationale (which rules/entities live where)
+- Technology choices with one-line justification each
+- Data migration approach from legacy stores
+
+Then spawn **architecture-critic**: "Review this proposed architecture for
+$2 against the spec in analysis/$1/AI_NATIVE_SPEC.md. Identify over-engineering,
+missed requirements, scaling risks, and simpler alternatives." Incorporate
+the critique. Write the result to `analysis/$1/REIMAGINED_ARCHITECTURE.md`.
+
+## Phase D — HITL checkpoint #2
+
+Enter plan mode. Present the architecture. Wait for approval.
+
+## Phase E — Parallel scaffolding
+
+For each service in the approved architecture (cap at 3 for the demo), spawn
+a **general-purpose agent in parallel**:
+
+"Scaffold the <service-name> service per analysis/$1/REIMAGINED_ARCHITECTURE.md
+and AI_NATIVE_SPEC.md. Create: project skeleton, domain model, API stubs
+matching the interface contracts, and **executable acceptance tests** for every
+behavior-contract rule assigned to this service (mark unimplemented ones as
+expected-failure/skip with the rule ID). Write to modernized/$1-reimagined/<service-name>/."
+
+Show the agents' progress. When all complete, run the acceptance test suites
+and report: total tests, passing (scaffolded behavior), pending (rule IDs
+awaiting implementation).
+
+## Phase F — Knowledge graph handoff
+
+Write `modernized/$1-reimagined/CLAUDE.md` — the persistent context file for
+the new system, containing: architecture summary, service responsibilities,
+where the spec lives, how to run tests, and the legacy→modern traceability
+map. This file IS the knowledge graph that future agents and engineers will
+load.
+
+Report: services scaffolded, acceptance tests defined, % behaviors with a
+home, location of all artifacts.

plugins/code-modernization/commands/modernize-transform.md

@@ -0,0 +1,78 @@
+---
+description: Transform one legacy module to the target stack — idiomatic rewrite with behavior-equivalence tests
+argument-hint: <system-dir> <module> <target-stack>
+---
+
+Transform `legacy/$1` module **`$2`** into **$3**, with proof of behavioral
+equivalence.
+
+This is a surgical, single-module transformation — one vertical slice of the
+strangler fig. Output goes to `modernized/$1/$2/`.
+
+## Step 0 — Plan (HITL gate)
+
+Read the source module and any business rules in `analysis/$1/BUSINESS_RULES.md`
+that reference it. Then **enter plan mode** and present:
+- Which source files are in scope
+- The target module structure (packages/classes/files you'll create)
+- Which business rules / behaviors this module implements
+- How you'll prove equivalence (test strategy)
+- Anything ambiguous that needs a human decision NOW
+
+Wait for approval before writing any code.
+
+## Step 1 — Characterization tests FIRST
+
+Before writing target code, spawn the **test-engineer** subagent:
+
+"Write characterization tests for legacy/$1 module $2. Read the source,
+identify every observable behavior, and encode each as a test case with
+concrete input → expected output pairs derived from the legacy logic.
+Target framework: <appropriate for $3>. Write to
+`modernized/$1/$2/src/test/`. These tests define 'done' — the new code
+must pass all of them."
+
+Show the user the test file. Get a 👍 before proceeding.
+
+## Step 2 — Idiomatic transformation
+
+Write the target implementation in `modernized/$1/$2/src/main/`.
+
+**Critical:** Write code a senior $3 engineer would write from the
+*specification*, not from the legacy structure. Do NOT mirror COBOL paragraphs
+as methods, do NOT preserve legacy variable names like `WS-TEMP-AMT-X`.
+Use the target language's idioms: records/dataclasses, streams, dependency
+injection, proper error types, etc.
+
+Include: domain model, service logic, API surface (REST controller or
+equivalent), and configuration. Add concise Javadoc/docstrings linking each
+class back to the rule IDs it implements.
+
+## Step 3 — Prove it
+
+Run the characterization tests:
+```bash
+cd modernized/$1/$2 && <appropriate test command for $3>
+```
+Show the output. If anything fails, fix and re-run until green.
+
+## Step 4 — Side-by-side review
+
+Generate `modernized/$1/$2/TRANSFORMATION_NOTES.md`:
+- Mapping table: legacy file:lines → target file:lines, per behavior
+- Deliberate deviations from legacy behavior (with rationale)
+- What was NOT migrated (dead code, unreachable branches) and why
+- Follow-ups for the next module that depends on this one
+
+Then show a visual diff of one representative behavior, legacy vs modern:
+```bash
+delta --side-by-side <(sed -n '<lines>p' legacy/$1/<file>) modernized/$1/$2/src/main/<file>
+```
+
+## Step 5 — Architecture review
+
+Spawn the **architecture-critic** subagent to review the transformed code
+against $3 best practices. Apply any HIGH-severity feedback; list the rest
+in TRANSFORMATION_NOTES.md.
+
+Report: tests passing, lines of legacy retired, location of artifacts.

plugins/skill-creator/skills/skill-creator/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: skill-creator
-description: Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
+description: Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
 ---
 
 # Skill Creator
@@ -391,7 +391,7 @@ Use the model ID from your system prompt (the one powering the current session)
 
 While it runs, periodically tail the output to give the user updates on which iteration it's on and what the scores look like.
 
-This handles the full optimization loop automatically. It splits the eval set into 60% train and 40% held-out test, evaluates the current description (running each query 3 times to get a reliable trigger rate), then calls Claude with extended thinking to propose improvements based on what failed. It re-evaluates each new description on both train and test, iterating up to 5 times. When it's done, it opens an HTML report in the browser showing the results per iteration and returns JSON with `best_description` — selected by test score rather than train score to avoid overfitting.
+This handles the full optimization loop automatically. It splits the eval set into 60% train and 40% held-out test, evaluates the current description (running each query 3 times to get a reliable trigger rate), then calls Claude to propose improvements based on what failed. It re-evaluates each new description on both train and test, iterating up to 5 times. When it's done, it opens an HTML report in the browser showing the results per iteration and returns JSON with `best_description` — selected by test score rather than train score to avoid overfitting.
 
 ### How skill triggering works
 
@@ -435,6 +435,11 @@ In Claude.ai, the core workflow is the same (draft → test → review → impro
 
 **Packaging**: The `package_skill.py` script works anywhere with Python and a filesystem. On Claude.ai, you can run it and the user can download the resulting `.skill` file.
 
+**Updating an existing skill**: The user might be asking you to update an existing skill, not create a new one. In this case:
+- **Preserve the original name.** Note the skill's directory name and `name` frontmatter field -- use them unchanged. E.g., if the installed skill is `research-helper`, output `research-helper.skill` (not `research-helper-v2`).
+- **Copy to a writeable location before editing.** The installed skill path may be read-only. Copy to `/tmp/skill-name/`, edit there, and package from the copy.
+- **If packaging manually, stage in `/tmp/` first**, then copy to the output directory -- direct writes may fail due to permissions.
+
 ---
 
 ## Cowork-Specific Instructions
@@ -447,6 +452,7 @@ If you're in Cowork, the main things to know are:
 - Feedback works differently: since there's no running server, the viewer's "Submit All Reviews" button will download `feedback.json` as a file. You can then read it from there (you may have to request access first).
 - Packaging works — `package_skill.py` just needs Python and a filesystem.
 - Description optimization (`run_loop.py` / `run_eval.py`) should work in Cowork just fine since it uses `claude -p` via subprocess, not a browser, but please save it until you've fully finished making the skill and the user agrees it's in good shape.
+- **Updating an existing skill**: The user might be asking you to update an existing skill, not create a new one. Follow the update guidance in the claude.ai section above.
 
 ---
 

plugins/skill-creator/skills/skill-creator/scripts/improve_description.py

@@ -2,22 +2,52 @@
 """Improve a skill description based on eval results.
 
 Takes eval results (from run_eval.py) and generates an improved description
-using Claude with extended thinking.
+by calling `claude -p` as a subprocess (same auth pattern as run_eval.py —
+uses the session's Claude Code auth, no separate ANTHROPIC_API_KEY needed).
 """
 
 import argparse
 import json
+import os
 import re
+import subprocess
 import sys
 from pathlib import Path
 
-import anthropic
-
 from scripts.utils import parse_skill_md
 
 
+def _call_claude(prompt: str, model: str | None, timeout: int = 300) -> str:
+    """Run `claude -p` with the prompt on stdin and return the text response.
+
+    Prompt goes over stdin (not argv) because it embeds the full SKILL.md
+    body and can easily exceed comfortable argv length.
+    """
+    cmd = ["claude", "-p", "--output-format", "text"]
+    if model:
+        cmd.extend(["--model", model])
+
+    # Remove CLAUDECODE env var to allow nesting claude -p inside a
+    # Claude Code session. The guard is for interactive terminal conflicts;
+    # programmatic subprocess usage is safe. Same pattern as run_eval.py.
+    env = {k: v for k, v in os.environ.items() if k != "CLAUDECODE"}
+
+    result = subprocess.run(
+        cmd,
+        input=prompt,
+        capture_output=True,
+        text=True,
+        env=env,
+        timeout=timeout,
+    )
+    if result.returncode != 0:
+        raise RuntimeError(
+            f"claude -p exited {result.returncode}\nstderr: {result.stderr}"
+        )
+    return result.stdout
+
+
 def improve_description(
-    client: anthropic.Anthropic,
     skill_name: str,
     skill_content: str,
     current_description: str,
@@ -99,7 +129,7 @@ Based on the failures, write a new and improved description that is more likely
 1. Avoid overfitting
 2. The list might get loooong and it's injected into ALL queries and there might be a lot of skills, so we don't want to blow too much space on any given description.
 
-Concretely, your description should not be more than about 100-200 words, even if that comes at the cost of accuracy.
+Concretely, your description should not be more than about 100-200 words, even if that comes at the cost of accuracy. There is a hard limit of 1024 characters — descriptions over that will be truncated, so stay comfortably under it.
 
 Here are some tips that we've found to work well in writing these descriptions:
 - The skill should be phrased in the imperative -- "Use this skill for" rather than "this skill does"
@@ -111,70 +141,41 @@ I'd encourage you to be creative and mix up the style in different iterations si
 
 Please respond with only the new description text in <new_description> tags, nothing else."""
 
-    response = client.messages.create(
-        model=model,
-        max_tokens=16000,
-        thinking={
-            "type": "enabled",
-            "budget_tokens": 10000,
-        },
-        messages=[{"role": "user", "content": prompt}],
-    )
+    text = _call_claude(prompt, model)
 
-    # Extract thinking and text from response
-    thinking_text = ""
-    text = ""
-    for block in response.content:
-        if block.type == "thinking":
-            thinking_text = block.thinking
-        elif block.type == "text":
-            text = block.text
-
-    # Parse out the <new_description> tags
     match = re.search(r"<new_description>(.*?)</new_description>", text, re.DOTALL)
     description = match.group(1).strip().strip('"') if match else text.strip().strip('"')
 
-    # Log the transcript
     transcript: dict = {
         "iteration": iteration,
         "prompt": prompt,
-        "thinking": thinking_text,
         "response": text,
         "parsed_description": description,
         "char_count": len(description),
         "over_limit": len(description) > 1024,
     }
 
-    # If over 1024 chars, ask the model to shorten it
+    # Safety net: the prompt already states the 1024-char hard limit, but if
+    # the model blew past it anyway, make one fresh single-turn call that
+    # quotes the too-long version and asks for a shorter rewrite. (The old
+    # SDK path did this as a true multi-turn; `claude -p` is one-shot, so we
+    # inline the prior output into the new prompt instead.)
     if len(description) > 1024:
-        shorten_prompt = f"Your description is {len(description)} characters, which exceeds the hard 1024 character limit. Please rewrite it to be under 1024 characters while preserving the most important trigger words and intent coverage. Respond with only the new description in <new_description> tags."
-        shorten_response = client.messages.create(
-            model=model,
-            max_tokens=16000,
-            thinking={
-                "type": "enabled",
-                "budget_tokens": 10000,
-            },
-            messages=[
-                {"role": "user", "content": prompt},
-                {"role": "assistant", "content": text},
-                {"role": "user", "content": shorten_prompt},
-            ],
+        shorten_prompt = (
+            f"{prompt}\n\n"
+            f"---\n\n"
+            f"A previous attempt produced this description, which at "
+            f"{len(description)} characters is over the 1024-character hard limit:\n\n"
+            f'"{description}"\n\n'
+            f"Rewrite it to be under 1024 characters while keeping the most "
+            f"important trigger words and intent coverage. Respond with only "
+            f"the new description in <new_description> tags."
         )
-
-        shorten_thinking = ""
-        shorten_text = ""
-        for block in shorten_response.content:
-            if block.type == "thinking":
-                shorten_thinking = block.thinking
-            elif block.type == "text":
-                shorten_text = block.text
-
+        shorten_text = _call_claude(shorten_prompt, model)
         match = re.search(r"<new_description>(.*?)</new_description>", shorten_text, re.DOTALL)
         shortened = match.group(1).strip().strip('"') if match else shorten_text.strip().strip('"')
 
         transcript["rewrite_prompt"] = shorten_prompt
-        transcript["rewrite_thinking"] = shorten_thinking
         transcript["rewrite_response"] = shorten_text
         transcript["rewrite_description"] = shortened
         transcript["rewrite_char_count"] = len(shortened)
@@ -216,9 +217,7 @@ def main():
         print(f"Current: {current_description}", file=sys.stderr)
         print(f"Score: {eval_results['summary']['passed']}/{eval_results['summary']['total']}", file=sys.stderr)
 
-    client = anthropic.Anthropic()
     new_description = improve_description(
-        client=client,
         skill_name=name,
         skill_content=content,
         current_description=current_description,

plugins/skill-creator/skills/skill-creator/scripts/run_loop.py

@@ -15,8 +15,6 @@ import time
 import webbrowser
 from pathlib import Path
 
-import anthropic
-
 from scripts.generate_report import generate_html
 from scripts.improve_description import improve_description
 from scripts.run_eval import find_project_root, run_eval
@@ -75,7 +73,6 @@ def run_loop(
         train_set = eval_set
         test_set = []
 
-    client = anthropic.Anthropic()
     history = []
     exit_reason = "unknown"
 
@@ -200,7 +197,6 @@ def run_loop(
             for h in history
         ]
         new_description = improve_description(
-            client=client,
             skill_name=name,
             skill_content=content,
             current_description=current_description,