Legacy-project style inheritance skill. Use when the user types /inherit-legacy-style, or when onboarding an AI coding agent onto a hand-written legacy project and you need to prevent "style drift" (the model imposing its pretrained mainstream idioms onto the project). Language- and framework-agnost

Inherit Legacy Style Prevents AI code style drift in legacy projects by scanning the codebase for implicit conventions across 4 meta architecture dimensions, resolving conflicts with the user one at a time, and crystallizing the consensus into an enforceable .ai style rules.md . Fully language and framework agnostic. When to Activate User types /inherit legacy style User mentions onboarding AI onto a hand written legacy project User is worried about AI generated code "drifting" from existing project conventions User wants to extract and codify their project's implicit coding rules When to Use Use this skill when you need to preserve legacy project style and prevent AI generated style drift. See When to Activate above for trigger conditions. Prerequisites Git (recommended; non Git projects fall back to file timestamps for incremental mode) Read/Write access to the project root (generates .ai style rules.md and optionally CLAUDE.md ) Workflow Step 0 — Auto Detect Mode Silently check for .ai style rules.md at the project root: File exists? Mode No Branch A — First time Full Scan Yes Branch B — Incremental Sniff Announce the mode in one line and proceed — never ask the user to pick. Branch A — First time Full Scan 1. Measure scale, pick a scanning tier Tier Source files Strategy Small ≲ 50 Full close read every source Medium 50–500 Infra layer = full read; business layer = sample 2–3 per dimension Large ≳ 500 Strict sampling + budget cap; stat summary first, then targeted reads 2. Scan along 4 dimensions 1. File Anatomy — in file declaration order (imports → types → main logic → helpers → export) 2. State & Control Flow — naming conventions for async state, pagination, flags 3. Infrastructure — where cross cutting utils live (interceptors, formatters, middleware) 4. Error Handling — try/catch vs global interceptor vs Result return; null check habits 3. Apply signal threshold noise reduction Before interrupting the user, evaluate signal strength: Weak signal → auto suppress: minority <5% AND count <10 → majority wins, minority goes to DONTs Strong signal → grill: near even split, or semantic fork on a core dimension Small project exception : sources ≲50, "3 vs 2" is NOT a majority → grill it 4. Resolve conflicts one at a time (Grilling Protocol) For each strong signal conflict, present exactly ONE question with 4 options: Evidence: pathA uses style X, pathB uses style Y WARNING: Risk: mixing both fractures the project style Choose: 1 follow X 2 follow Y 3 this is evolution, update rules 4 I have a new rule Suspend until the user answers, then proceed to the next conflict. Never stack questions. 5. Generate .ai style rules.md with three mandatory sections: [Golden Files] — real exemplar paths annotated with what they demonstrate [Naming & State Control Rules] — concrete, checkable conventions [DONTs] — anti patterns that must not propagate 6. Install the persistent hook Ask the user for enforcement strength (use AskUserQuestion ): Option Mechanism 1 Soft hook (recommended) Write @.ai style rules.md reference into project CLAUDE.md 2 Hard hook Soft hook + PreToolUse[Write\ Edit\ MultiEdit] Hook in settings.json 3 No hook Keep the rules file; user references manually Branch B — Incremental Sniff 1. Read existing .ai style rules.md ; if it has a commit fingerprint, git diff <last hash HEAD stat to pinpoint delta 2. Read recent Git changes ( git log 3 stat → inspect suspect files on demand) 3. For oversized diffs ( hundreds of files): stat summary only + sample the largest changes 4. Compare new code against recorded rules → conflicts go through Grilling Protocol 5. Append evolution log at the end of .ai style rules.md (never overwrite old rules) Per Turn Enforcement When .ai style rules.md is in context (loaded via CLAUDE.md), every code writing task must open with a compliance declaration in the reasoning chain, naming the exemplar being followed and the DONTs being avoided. How It Works This skill auto detects whether it's a first time or incremental run via .ai style rules.md presence: First time (Branch A) — Measures project scale, scans codebase across 4 meta architecture dimensions (File Anatomy, State & Control Flow, Infrastructure, Error Handling), applies signal threshold noise reduction to suppress weak conflicts, resolves strong signal conflicts one at a time with the user, generates .ai style rules.md with Golden Files / Naming Rules / DONTs, and offers optional enforcement hooks. Incremental (Branch B) — Reads existing rules, checks recent Git diffs for new or conflicting patterns, runs the same one at a time grilling protocol for any conflicts found, and appends evolution logs without overwriting existing rules. Per Turn Enforcement — When hooked via CLAUDE.md , every code writing task opens with a compliance declaration naming the exemplar followed and the DONTs avoided. Output Specification .ai style rules.md at project root (with commit fingerprint + scale tier in header) Optionally CLAUDE.md with @.ai style rules.md reference Evolution logs appended as [YYYY MM DD] Style Evolution Log entries Anti Patterns FAIL: Do NOT skip the scale measurement step — sampling a 30 file project "starves" it; full scanning a 5,000 file repo blows up FAIL: Do NOT stack multiple conflict questions at once — grilling is strictly one at a time FAIL: Do NOT overwrite old rules in incremental mode — always append evolution logs FAIL: Do NOT default to "hard hook" without asking — enforcement strength is the user's call FAIL: Do NOT judge syntax or tech stack quality — this skill aligns meta architecture only FAIL: Do NOT copy bugs from exemplar files — reuse structure, flag defects Best Practices Announce the detected mode (first time vs incremental) and scale tier in one line before scanning For large projects, read stat summaries first, then targeted Read on suspect files Let the signal threshold handle noise — a 843 vs 8 naming split should auto resolve without user interruption When in doubt about signal strength, lean toward asking The CLAUDE.md soft hook ( @.ai style rules.md ) is usually sufficient; hard hook only if the user wants mechanical enforcement Related Skills init — initialize a new CLAUDE.md with codebase documentation code review — review diffs for correctness and style issues simplify — review code for reuse and simplification opportunities Examples 1. First time onboarding User: "Help me onboard AI to this older codebase without changing its style." Action: Run Branch A full scan → measure scale → scan 4 dimensions → grill conflicts → generate .ai style rules.md → offer hook strength (soft/hard/none). 2. Incremental update after team changes User: "We added a new module; keep existing style rules intact." Action: Run Branch B incremental sniff → compare Git deltas to recorded rules → grill any new conflicts → append evolution log without overwriting. 3. Enforcing DONTs via CLAUDE.md User: "Make sure all new code stays consistent with the project's rules." Action: Soft hook installed → .ai style rules.md auto loaded every session → every code writing task opens with compliance declaration, reusing exemplar patterns and avoiding DONTs.