Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Verify that task outputs actually achieve stated goals through 4 progressive levels of verification
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Verify that task outputs actually achieve stated goals through 4 progressive levels of verification. Works backward from the goal to verify each artifact exists, is substantive, is wired into the system, and passes functional tests.
Skill({ skill: 'goal-backward-verification' });
Invoke when:
Verify the expected output artifact is present on disk.
# Check file existence
test -f <expected_path> && echo "PASS: File exists" || echo "FAIL: File missing"
Pass: File exists at expected path. Fail: File missing — task did not produce expected output.
Verify the file contains real content, not a stub or placeholder.
Checks:
# Count substantive lines (non-empty, non-comment)
grep -cvE '^\s*(//|#|\*|$|TODO|PLACEHOLDER|FIXME|STUB)' <file>
Pass: File has substantive content (>10 real lines). Fail: Stub detected — file exists but contains no real implementation.
Verify the artifact is imported, referenced, or registered by the system.
Checks:
.cjs/.js modules: at least 1 require() or import reference in other filesskills: frontmatter or skill-index.json# Search for references to the artifact
grep -r "require.*<module-name>" .claude/ --include="*.cjs" --include="*.js" | grep -v node_modules | grep -v _archive
Pass: At least 1 external reference found. Fail: Orphaned artifact — exists but nothing uses it.
Verify the artifact passes its associated tests end-to-end.
Checks:
tests/<mirror-path>/<name>.test.cjs)node --test <test-file># Run associated tests
node --test tests/<path>/<artifact>.test.cjs
Pass: All tests pass (exit code 0). Fail: Test failures detected — artifact may be broken.
All 4 levels must pass. Any failure at any level blocks the verification.
All 4 levels are checked, but failures produce warnings instead of blocking. Useful for incremental development where wiring may not be complete yet.
Set mode via environment variable:
GOAL_VERIFICATION_MODE=strict # or advisory
{
"artifact": "path/to/file",
"levels": {
"exists": { "passed": true },
"substantive": { "passed": true, "lines": 45 },
"wired": { "passed": true, "references": ["path/to/importer.cjs"] },
"functional": { "passed": false, "reason": "2 tests failed", "exitCode": 1 }
},
"verdict": "PARTIAL",
"mode": "advisory",
"passedLevels": 3,
"totalLevels": 4
}
Verdict values:
PASS — All 4 levels passedPARTIAL — Some levels passed, some failedFAIL — Level 1 (Exists) failed (nothing else to check)Read the task's acceptance criteria or plan file to identify expected output artifacts (files, schemas, skills, hooks).
For each artifact, run Levels 1-4 in order. Stop early if Level 1 fails (no point checking substantiveness of a missing file).
Generate structured JSON output with per-artifact, per-level results. Include the overall verdict.
In advisory mode, log warnings to session-gap-log for reflection-agent consumption. In strict mode, return FAIL verdict to block completion.
verification-before-completion — Evidence-based completion gatesproactive-audit — Framework health checkstdd — Test-driven development ensuring Level 4 passes아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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