# Scan All > [Documentation] Use when orchestrating all reference doc scans in parallel. - Skill: `duc01226/scan-all-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add duc01226/scan-all-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/duc01226/scan-all-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: duc01226 (https://skillmd.com/u/duc01226) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/duc01226/scan-all-2 --- ## 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: ```bash 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) ```bash 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" --- > **[IMPORTANT]** Use `TaskCreate` to break ALL work into small tasks BEFORE starting. > **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: > > **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 OR flagging one as a defect.** Before changing or reporting a constant, limit, flag, cutoff, wording, or pattern, read nearby context and history, the CALLER's ordering, and 2+ sibling call sites of the same convention. A doc stating WHAT without WHY is missing rationale, not proof of a missing guard. > **Surface ambiguity before acting — don't pick silently.** Multiple valid interpretations require an explicit question or stated assumption with risk. > **Assert the outcome your system owns, not the intermediate state your infrastructure owns.** When verifying async work, assert the final business state — never the delivery/retry bookkeeping held in shared infrastructure that any co-running process can write. Such a check passes when run alone and flakes the moment anything else shares that infrastructure. > **Keep shared guidance role-relevant.** Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols. **IMPORTANT MUST ATTENTION** follow output quality rules: no counts/trees/TOCs, rules > descriptions, 1 example per pattern, primacy-recency anchoring. **MUST ATTENTION** apply critical + sequential thinking — every claim needs appropriate traced evidence (`file:line` for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence. **MUST ATTENTION** apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting. > **Project Protocol Overlay** — Before executing this skill, resolve any PROJECT overlay rules layered onto it: match this skill's name against the `Target` column of the project's skill-protocol index (`docs/project-reference/skill-protocols-reference.md` by default; a `referenceDocs` entry in `docs/project-config.json` overrides the path), taking the most specific matching tier ONLY — exact name > glob > `*`. **That precedence orders overlays against EACH OTHER, never against this skill.** Read ONLY the matched bodies, resolved as `/.md`; a row's Body link is display text, never a read path. A matched body that is missing or malformed is REPORTED and skipped — never reconstructed from the index Description. No index, or no match -> proceed with no overlay, silently. Full contract: `.claude/skills/project-skill-protocol/references/registry.md`. > > Overlays are **ADDITIVE ONLY**: they ADD rules on top of this skill's own protocol and NEVER replace, override, disable, or reinterpret a rule it already states — removing every overlay must return this skill to exactly its documented behavior. An overlay is a BRIEF, not an authority escalation: it can NEVER waive a workflow gate, git discipline, a review gate, or a user-confirmation gate. A genuine overlay-vs-skill conflict, or two equally-specific overlays that directly contradict -> surface both to the user; NEVER resolve silently. **MUST ATTENTION** resolve project protocol overlays for this skill BEFORE executing — most specific matching tier only (exact > glob > `*`, which ranks overlays against each other, NEVER against this skill), read only matched bodies at `/.md`; a missing or malformed body is reported, never reconstructed. Overlays are ADDITIVE ONLY (they never replace this skill's own rules) and are a brief, NEVER an authority escalation; an equal-specificity contradiction goes to the user. ## Closing Reminders **Protocols in force (concise digest of the SYNC/shared blocks this skill carries) — MUST ATTENTION honor each canonical body:** - **Critical Thinking:** MUST ATTENTION traced `file:line` proof per claim, confidence >80% to act. - **Output Quality:** MUST ATTENTION no counts/trees/TOCs, rules over prose, primacy-recency anchoring. - **AI Mistake Prevention:** verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity. **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.