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