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[Documentation] Use when you need orchestrate all reference doc scans in parallel.
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npx skills add https://github.com/duc01226/EasyPlatform --skill scan-allThe command stays on one line. Scroll horizontally to inspect it before copying.
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Based on SOC occupation classification
[Architecture] Use when designing solution architecture across backend, frontend, deployment, monitoring, testing, and code quality.
[Utilities] Use when you need to answer technical and architectural questions.
[Content] Use when you need to brainstorm as a PO/BA — structured ideation for problem-solving, new product creation, or feature enhancement.
Showing SKILL.md
| name | scan-all |
| version | 1.0.0 |
| description | [Documentation] Use when you need orchestrate all reference doc scans in parallel. |
Goal: Run all 12 scan-* skills in parallel and clear the staleness gate.
Workflow:
.claude/.scan-stale so the gate unblocks/graph-build to update structural graph/prompt-enhance on all 12 scanned docsKey Rules:
.claude/.scan-stale flag after completion/prompt-enhance ensures AI attention anchoring on all generated docs/scan-all manuallyLaunch all 12 scan skills in parallel:
| # | Invocation | Target Doc |
|---|---|---|
| 1 | /scan --target=project-structure | project-structure-reference.md |
| 2 | /scan --target=backend-patterns | backend-patterns-reference.md |
| 3 | /scan --target=seed-test-data | seed-test-data-reference.md |
| 4 | /scan --target=frontend-patterns | frontend-patterns-reference.md |
| 5 | /scan --target=integration-tests | integration-test-reference.md |
| 6 | /scan --target=feature-spec | feature-spec-reference.md |
| 7 | /scan --target=code-review-rules | code-review-rules.md |
| 8 | /scan --target=scss-styling | scss-styling-guide.md |
| 9 | /scan --target=design-system | design-system/README.md |
| 10 | /scan --target=e2e-tests | e2e-test-reference.md |
| 11 | /scan --target=domain-entities | domain-entities-reference.md |
| 12 | /scan --target=docs-index | docs-index-reference.md |
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.
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:
python .claude/scripts/code_graph build --json
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}
After all scans complete, report:
"Scan All Complete:
[IMPORTANT] Use
TaskCreateto 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.
- No inventories/counts — AI can
grep | wc -l. Counts go stale instantly- No directory trees — AI can
glob/ls. Use 1-line path conventions- No TOCs — AI reads linearly. TOC wastes tokens
- No examples that repeat what rules say — one example only if non-obvious
- Lead with answer, not reasoning. Skip filler words and preamble
- Sacrifice grammar for concision in reports
- 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. 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.
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.
Protocols in force (concise digest of the SYNC/shared blocks this skill carries) — MUST ATTENTION honor each canonical body:
file:line proof per claim, confidence >80% to act.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.