scan-all

Quick Summary

Goal: Run all 12 scan-* skills in parallel and clear the staleness gate.

Workflow:

1. Check Prerequisites — Verify project has content (not empty)
2. Launch Parallel Scans — All 12 skills simultaneously
3. Collect Results — Read scan output from reference docs
4. Clear Staleness Flag — Re-evaluate all docs via refreshScanStaleFlag(), which removes .claude/.scan-stale once every doc is fresh (see Post-Scan Cleanup)
5. Build Knowledge Graph — Run /graph-build to update structural graph
6. Enhance Docs — Run /prompt-enhance on all 12 scanned docs
7. Summarize — Report what was refreshed

Key Rules:

• All 12 scans run in PARALLEL for speed
• Does NOT modify code — only populates docs/project-reference/
• Clears .claude/.scan-stale flag after completion
• /prompt-enhance ensures AI attention anchoring on all generated docs

When to Use

• Staleness gate blocks prompts ("BLOCKED: Reference docs are stale")
• First time using easy-claude on an existing project (project onboarding)
• Periodic refresh when codebase has changed significantly
• User runs /scan-all manually

When to Skip

• Empty/greenfield project (no code to scan)
• All reference docs are already fresh (no staleness warning)

Execution

Each scan reads real code evidence and (re)populates ONE reference doc under docs/project-reference/. Those docs are injected into AI context downstream, so scanning is what keeps that guidance true to the current codebase — the Purpose column says what each scan documents and therefore why it matters. Launch all 12 code-derived scans in parallel:

#	Invocation	Target Doc	Purpose — what the scan documents
1	/scan --target=project-structure	project-structure-reference.md	Service architecture, ports, directory layout, tech stack, deployment & module registry (spans backend, frontend, infra)
2	/scan --target=backend-patterns	backend-patterns-reference.md	Repository, CQRS, validation, entity, event & migration patterns
3	/scan --target=seed-test-data	seed-test-data-reference.md	Seeder patterns & conventions, from real code evidence
4	/scan --target=frontend-patterns	frontend-patterns-reference.md	Component, state, form, API, routing & styling patterns
5	/scan --target=integration-tests	integration-test-reference.md	Integration-test base classes, fixtures, helpers & service setup
6	/scan --target=feature-spec	feature-spec-reference.md	Feature-doc structure, app→service mapping, spec templates & conventions
7	/scan --target=code-review-rules	code-review-rules.md	Code conventions, anti-patterns, architecture rules & review checklists
8	/scan --target=scss-styling	scss-styling-guide.md	SCSS architecture, BEM conventions, mixins, variables, theming & responsive patterns
9	/scan --target=design-system	design-system/README.md	Design tokens, component inventory & app→doc design-system mappings
10	/scan --target=e2e-tests	e2e-test-reference.md	E2E architecture, page objects, step definitions, config & framework patterns
11	/scan --target=domain-entities	domain-entities-reference.md	Domain entities, DTOs, aggregate boundaries, sync patterns & ER diagrams
12	/scan --target=docs-index	docs-index-reference.md	Documentation structure, categories, relationships & lookup tables

Coverage & count. These 12 are the code-derived docs. The child scan skill exposes a 13th key, ui-system — a meta-target that only fan-runs #4, #8, #9 together — intentionally excluded here to avoid double-scanning. Curated/static docs (lessons.md, spec-principles.md, spec-system-reference.md, workflow-spec-test-code-cycle-reference.md) are hand-authored, not scanned, so they are absent by design. Purpose text mirrors each target's description in .claude/skills/scan/references/targets.md — update it THERE first if a target's scope changes, then reflect it here.

Post-Scan Cleanup

After all scans complete, clear the staleness flag:

node -e "require('./.claude/hooks/lib/session-init-helpers.cjs').refreshScanStaleFlag()"

This re-evaluates all docs and removes the .scan-stale gate if all are now fresh.

Post-Scan: Build Knowledge Graph (MANDATORY)

After all scans complete, MUST ATTENTION create a follow-up task:

TaskCreate: "Run /graph-build to build/update code knowledge graph"

The knowledge graph uses project-config.json (populated by scans) for API connector patterns and implicit connection rules. Building the graph after scans ensures:

• Frontend↔backend API_ENDPOINT edges use accurate service paths
• MESSAGE_BUS implicit edges use correct consumer patterns
• Graph trace shows full system flow (frontend → backend → cross-service consumers)

python .claude/scripts/code_graph build --json

Post-Scan: Enhance Generated Docs (MANDATORY)

Each scan-* sub-skill now self-enhances its own doc as its final step. After graph build, MUST ATTENTION confirm /prompt-enhance ran on every scanned doc and backfill any that were skipped. Reference docs are injected into AI context — attention anchoring (top/bottom summaries, inline READ summaries, token density) directly improves AI output quality.

TaskCreate one task per doc, parallel OK:

#	Target File
1	docs/project-reference/project-structure-reference.md
2	docs/project-reference/backend-patterns-reference.md
3	docs/project-reference/seed-test-data-reference.md
4	docs/project-reference/frontend-patterns-reference.md
5	docs/project-reference/integration-test-reference.md
6	docs/project-reference/feature-spec-reference.md
7	docs/project-reference/code-review-rules.md
8	docs/project-reference/scss-styling-guide.md
9	docs/project-reference/design-system/README.md
10	docs/project-reference/e2e-test-reference.md
11	docs/project-reference/domain-entities-reference.md
12	docs/project-reference/docs-index-reference.md

Run via: /prompt-enhance docs/project-reference/{filename}

Summary Output

After all scans complete, report:

"Scan All Complete:

• {X}/12 scans succeeded
• Reference docs refreshed in docs/project-reference/
• Staleness gate cleared
• Prompt-enhanced {Y}/12 docs
• Knowledge graph rebuilt via /graph-build"

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[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting.

<!-- SYNC:critical-thinking-mindset -->

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.

<!-- /SYNC:critical-thinking-mindset --> <!-- SYNC:output-quality-principles -->

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

<!-- /SYNC:output-quality-principles --> <!-- SYNC:ai-mistake-prevention -->

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

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

<!-- /SYNC:ai-mistake-prevention --> <!-- SYNC:output-quality-principles:reminder -->

IMPORTANT MUST ATTENTION follow output quality rules: no counts/trees/TOCs, rules > descriptions, 1 example per pattern, primacy-recency anchoring.

<!-- /SYNC:output-quality-principles:reminder --> <!-- SYNC:critical-thinking-mindset:reminder -->

MUST ATTENTION apply critical + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact

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