evidence-quality-check
A validation tool to mark claims as verified, inferred, or assumed based on actual source data.
Install
mkdir -p .claude/skills/evidence-quality-check && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/15316" && unzip -o skill.zip -d .claude/skills/evidence-quality-check && rm skill.zipInstalls to .claude/skills/evidence-quality-check
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
**Призначення**: перевірити що твердження у відповіді мають реальні докази, не вигадані. **Коли викликати**: при будь-якому RECON / DISCOVERY / review / ADR.Key capabilities
- →Verify claims in responses have real evidence
- →Audit evidence quality in RECON, DISCOVERY, review, or ADR processes
- →Identify fabricated line numbers in code references
- →Detect 'should be' statements disguised as facts
- →Flag phantom imports not verified by grep
How it works
The skill defines canonical markers like [VERIFIED path:line] and [INFERRED] to tag claims. It also outlines forbidden anti-patterns such as fabricated line numbers and 'should be' statements.
Inputs & outputs
When to use evidence-quality-check
- →Validating technical documentation
- →Reviewing analysis findings
- →Auditing ADR claims
About this skill
Evidence Quality Check Skill
Призначення: перевірити що твердження у відповіді мають реальні докази, не вигадані. Коли викликати: при будь-якому RECON / DISCOVERY / review / ADR.
Канонічні маркери (SSOT)
| Маркер | Значення | Приклад |
|---|---|---|
[VERIFIED path:line] | Бачив код, перевірив | [VERIFIED core/smc/engine.py:42] |
[VERIFIED terminal] | Запустив, побачив output | [VERIFIED terminal: pytest passed] |
[INFERRED] | Логічний висновок з даних | [INFERRED from naming pattern] |
[ASSUMED — verify: <cmd>] | Гіпотеза, треба перевірити | [ASSUMED — verify: grep "FINAL_SOURCES"] |
[UNKNOWN — risk: H/M/L] | Сліпа зона | [UNKNOWN — risk: M (legacy code path)] |
[path:?] | Шлях відомий, line — НЕ перевірений | [runtime/store/uds.py:?] |
Forbidden anti-patterns
A1 — Fabricated line numbers
❌ core/smc/engine.py:42 коли ти не читав файл
✅ core/smc/engine.py:? або [INFERRED]
A2 — "Should be" disguised as fact
❌ "Function X handles error correctly" ✅ "Function X appears to handle error correctly [INFERRED — not VERIFIED with terminal run]"
A3 — Phantom imports
❌ "Module Y imports Z" коли ти не grep'ав ✅ "Module Y likely imports Z [ASSUMED — verify: grep 'import Z' Y.py]"
A4 — Hand-wave conclusions
❌ "Tests cover this case" ✅ "Tests cover this case [VERIFIED tests/test_X.py:N test_case_Y]"
A5 — Stale memory
❌ Цитуєш файл по пам'яті як "I remember it says..."
✅ read_file зараз → [VERIFIED path:line]
Audit checklist
Перед здачею тверджень:
- Кожне твердження про код має маркер
-
[VERIFIED path:line]— line був реально прочитаний цим запитом? (не з summary, не з пам'яті) -
[INFERRED]— ясно що це не factual, а логічний висновок -
[UNKNOWN]чесно прописані з risk level - Жодного fabricated line number
Червоні прапори у власній відповіді:
- "Це працює" без
[VERIFIED terminal] - "У файлі X є функція Y" без
[VERIFIED X:line] - "Я перевірив" без showing how
- "Я впевнений" — це слово майже завжди = no evidence
Output format
При повідомленні висновків після RECON:
## FACTS
- [VERIFIED core/smc/engine.py:128] SmcEngine.on_bar() returns None on stale data
- [VERIFIED terminal: pytest tests/test_smc_e1.py] all 12 tests pass
- [INFERRED] config drift у smc.max_compute_ms (default 50ms у code, 30ms у docs)
- [ASSUMED — verify: grep "DERIVE_CHAIN"] DERIVE_CHAIN визначений тільки у core/derive.py
## UNKNOWN
- [UNKNOWN — risk: M] чи D1 anchor offset актуальний для weekend gap (не тестовано)
## EVIDENCE QUALITY: HIGH (3 VERIFIED, 1 INFERRED, 0 fabricated)
Правило використання
- Будь-який RECON / DISCOVERY = обов'язкове маркування
- R_REJECTOR: окремо аудитує evidence quality (CA4)
- R_PATCH_MASTER: проводить evidence-check у RECON gate
- R_BUG_HUNTER: ніяких висновків без VERIFIED маркерів
When not to use it
- →When the goal is to generate new content without requiring evidence
- →When the task does not involve reviewing or auditing existing claims
Limitations
- →Requires explicit marking of every statement about code
- →Relies on the agent to apply markers correctly
- →Does not generate evidence, only verifies its presence
How it compares
This skill provides a structured, auditable tagging system for claims, unlike a manual review that relies on subjective assessment.
Compared to similar skills
evidence-quality-check side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| evidence-quality-check (this skill) | 0 | 4mo | No flags | Intermediate |
| citation-management | 23 | 7mo | Review | Beginner |
| microsoft-docs | 10 | 5mo | Review | Beginner |
| peer-review | 11 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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