claude-md-init

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

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
• User-question prompts mean to ask the user directly in Codex.
• Ignore Claude-specific mode-switch instructions when they appear.
• Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
• Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
• Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
• For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
• If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex does not receive Claude hook-based doc injection. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

• docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
• docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
• docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Missing-file hard stop: If docs/project-config.json, the docs index, lessons.md, or any task-required reference doc is missing, stop immediately and ask the user to run $project-config and $scan-all.

Situation-based docs:

• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec/test-case planning or TC mapping: feature-docs-reference.md
• Integration test implementation/review: integration-test-reference.md
• E2E test implementation/review: e2e-test-reference.md
• Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

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

Bootstrap Gate (when CLAUDE.md is missing or incomplete)

This skill is the AI-runnable route the agent-files bootstrap gate offers when a portable .claude install lands in a project without a root CLAUDE.md — or with one that carries only project-specific knowledge and is missing the universal portable guides. Two hooks detect the gap and route here (shared detection lib: .claude/hooks/lib/agent-files-state.cjs):

• init-prompt-gate.cjs (UserPromptSubmit) — blocks the first prompt once project-config.json is populated but CLAUDE.md / AGENTS.md is missing or incomplete.
• agent-files-skill-gate.cjs (PreToolUse: Skill) — blocks non-meta skills under the same condition.

Three-state detection per root file: missing → routes to --mode init (fresh from template); incomplete → routes to --mode update (smart-merge — preserves your project content, injects the guides); ok → no block. Completeness is decided by hasUniversalGuides(): a current-or-newer sentinel (<!-- CK:UNIVERSAL-GUIDES v1 -->) → complete; an older sentinel → flag for update; no sentinel → fall back to scanning required anchors (First Action Decision, Task Planning Rules, Code Responsibility Hierarchy, Evidence-Based Reasoning) so legacy/hand-written complete files still pass.

Run $claude-md-init (or the generator directly) to produce CLAUDE.md from docs/project-config.json + template. The generated file ships the universal session-start guides (workflow ask-confirm gate, task-planning rules, code hierarchy, naming, evidence/confidence rules) and stamps the sentinel at the top so the gate recognizes it as complete.

Opt-out — to keep a project-only CLAUDE.md/AGENTS.md (your custom knowledge, none of the universal guides), set portability.requireUniversalGuides: false in docs/project-config.json (persistent; default true). The gate then checks only existence, never completeness. The transient skip init escape still dismisses both hooks for 24h. The gate is dormant in empty/greenfield folders and before config is populated. AGENTS.md is generated separately by $codex-sync (user-invoke-only).

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

Generator + bootstrap-gate coverage lives in the hooks test suite:

node .claude/hooks/tests/run-all-tests.cjs --filter=agent-files

---

[IMPORTANT] Use task tracking 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. Keep domain concepts out of generic/shared/infrastructure layers. A reusable layer (shared library, framework, infra module) must reference NO consumer-specific domain concept — tenant/customer/product IDs, business entities, feature rules. The leak compiles and runs, so it passes review silently while coupling the "reusable" layer to one consumer. Push domain fields/logic down into the consumer via subclass or composition.

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 task tracking 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 task tracking.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/hooks/lib/prompt-injections.cjs + .claude/.ck.json

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. If either file or a required reference doc is missing, stop immediately and ask the user to run the project-config and scan-all skills. Any supported AI tool may execute when this shared context and local docs are available.

1. DETECT: Match prompt against workflow catalog
2. ANALYZE: Find best-match workflow AND evaluate if a custom step combination would fit better
3. ASK (REQUIRED FORMAT): Use a direct user question with this structure unless the user explicitly invoked a workflow/skill and the local protocol treats explicit invocation as confirmation:
   • Question: "Which workflow do you want to activate?"
   • Option 1: "Activate [BestMatch Workflow] (Recommended)"
   • Option 2: "Activate custom workflow: [step1 → step2 → ...]" (include one-line rationale)
4. ACTIVATE (if confirmed): Call $workflow-start <workflowId> for standard; sequence custom steps manually
5. CREATE TASKS: task tracking for ALL workflow steps
6. EXECUTE: Follow each step in sequence [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: 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. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows. Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass. Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
6. Auto-fix gate: "Could $code-review/$code-simplifier/$security/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.