claude-md-init

name	claude-md-init
version	1.0.0
description	[Documentation] Use when you need initialize, update, or refactor CLAUDE markdown from project-config JSON and codebase scan results.

Quick Summary

Goal: Automate CLAUDE.md lifecycle — generate from project-config.json + template, incrementally update marked sections, or refactor for token efficiency.

Workflow:

1. Detect Mode — init (no CLAUDE.md or --mode init), update (--mode update), refactor (--mode refactor)
2. Run Generator — node .claude/skills/claude-md-init/scripts/generate-claude-md.cjs --mode <mode>
3. AI Fill — Review output, fill creative sections (project description, golden rules inference)
4. Verify — Confirm output is valid, no project-specific leaks from template

Key Rules:

• Generic — works in any project by reading docs/project-config.json
• Section markers (<!-- SECTION:key -->) enable incremental updates without overwriting user content
• Conditional sections — generated ONLY when config has matching data; empty config = section omitted
• Static framework sections (8 total) are portable across all projects

Modes

Mode	When	Behavior
init	No CLAUDE.md exists, or first-time setup	Generate fresh CLAUDE.md from template + config. Populates all markers.
update	CLAUDE.md exists with markers	Replace only content between markers. Preserve everything else.
refactor	CLAUDE.md exists, needs optimization	AI reads entire CLAUDE.md, optimizes for token efficiency, removes redundancy, improves structure. No script — pure AI.

Prerequisites

• docs/project-config.json — primary data source (run /project-config first if missing)
• Node.js available (for generator script)

Phase 1: Detect Mode

# Check CLAUDE.md state
node .claude/skills/claude-md-init/scripts/generate-claude-md.cjs --detect

Decision logic:

• No CLAUDE.md → init
• CLAUDE.md with markers → update
• CLAUDE.md without markers → smart-merge (see below)
• User explicit --mode flag → override detection

Phase 2: Run Generator Script

# Init mode: generate fresh CLAUDE.md
node .claude/skills/claude-md-init/scripts/generate-claude-md.cjs --mode init

# Update mode: sync marked sections only
node .claude/skills/claude-md-init/scripts/generate-claude-md.cjs --mode update

Script behavior:

1. Reads docs/project-config.json
2. Reads template (references/claude-md-template.md) for init, or existing CLAUDE.md for update
3. Calls section builders to generate content for each marker key
4. Writes output to CLAUDE.md (creates backup .claude-md.backup first)
5. Outputs report: which sections were generated, which skipped (no data), which preserved

Smart-Merge (Update on CLAUDE.md Without Markers)

When running update on an existing CLAUDE.md that has NO section markers:

1. Read existing CLAUDE.md
2. Match sections by ## heading text against known section keys (see references/section-registry.md)
3. For each matched section: wrap with markers, replace content with generated content
4. For unmatched user sections: preserve as-is (no markers added)
5. Write output with backup

Phase 3: AI Fill (Post-Script)

After the script generates the mechanical parts, AI reviews and fills:

1. Project description in TL;DR — write a concise 2-3 sentence description based on config + codebase
2. Golden rules — infer from contextGroups[].rules in config, but rewrite as human-readable rules
3. Decision quick-ref — build from modules[] + framework config, add project-specific patterns
4. Naming conventions — detect from codebase patterns if not in config

Phase 4: Verify

• CLAUDE.md is valid markdown
• All section markers are properly paired (open + close)
• No template placeholder text remains (e.g., {project-name}, TODO)
• No .claude/skills/claude-md-init/ references leak into output (self-reference)
• Conditional sections with no data are omitted (not empty stubs)

Refactor Mode (AI-Only)

When --mode refactor or user asks to optimize CLAUDE.md:

1. Read entire CLAUDE.md
2. Identify: redundant sections, verbose explanations, duplicate info available in referenced docs
3. Apply token efficiency: remove duplication, consolidate tables, shorten where possible
4. Preserve all section markers
5. Report: lines before/after, sections changed, estimated token savings

Section Marker Protocol

<!-- SECTION:tldr -->

Auto-generated content here...

<!-- /SECTION:tldr -->

Rules:

• Only content between markers is replaced on update
• Content outside markers is never touched
• Missing markers in update mode → section skipped (not inserted)
• Init mode uses template which includes all markers
• Markers use lowercase kebab-case keys matching section-registry.md

Section Keys (Quick Reference)

See references/section-registry.md for full mapping. Summary:

Key	Source	Conditional?
tldr	project.*, modules[], framework.*	No — always generated
golden-rules	contextGroups[].rules	Yes — skip if no rules
decision-quick-ref	modules[], framework.*	Yes — skip if no modules
key-locations	modules[].pathRegex	Yes — skip if no modules
dev-commands	testing.commands, infrastructure.*	Yes — skip if no commands
infra-ports	modules[].meta.port (infra)	Yes — skip if no ports
api-ports	modules[].meta.port (services)	Yes — skip if no ports
integration-testing	framework.integrationTestDoc	Yes — skip if no doc
e2e-testing	framework.e2eTestDoc or scan	Yes — skip if no tests
doc-index	Scan docs/ directory	Yes — skip if no docs/
doc-lookup	modules[] + business features	Yes — skip if no modules

Running Tests

node .claude/skills/claude-md-init/scripts/test-generate-claude-md.cjs

---

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

Output Quality — Token efficiency without sacrificing quality.

1. No inventories/counts — AI can grep | wc -l. Counts go stale instantly
2. No directory trees — AI can glob/ls. Use 1-line path conventions
3. No TOCs — AI reads linearly. TOC wastes tokens
4. No examples that repeat what rules say — one example only if non-obvious
5. Lead with answer, not reasoning. Skip filler words and preamble
6. Sacrifice grammar for concision in reports
7. Unresolved questions at end, if any

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

IMPORTANT MUST ATTENTION maintain >=8 rules per 100 lines. Critical rules in first+last 5 lines. Tables over prose.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Closing Reminders

IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act) IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.