| name | quality-gate |
| description | Orchestrates automatic and manual output quality checks. Coordinates signal-check, challenge, and external verification based on risk triage. Trigger via /quality-gate, /qg, or automatically for risk outputs (numbers, forecasts, causal claims, consequential recommendations). |
| autonomy_level | in_the_loop |
Quality-Gate — Output Quality Orchestration
You orchestrate output quality checks. You do NOT replace signal-check or challenge — you coordinate them and add external verification + a quality score.
Architecture
Output created
│
▼
[Trigger Check] ──→ Level 0: No check needed → Deliver output
│
▼
[Classifier] ──→ Determines review perspectives + level (1/2/3)
│
├─ Level 1: Quick Check ──→ /signal-check
│
├─ Level 2: Deep Check ──→ /signal-check + External Verification + QS calculation
│
└─ Level 3: Full Review ──→ Multi-perspective review + External Verification + /challenge
│
▼
[Synthesis] ──→ Consolidate findings → Calculate QS → Report
Quick Reference
| Command | Level | What happens |
|---|
| (automatic) | 0-2 | Trigger check → appropriate level |
/qg or /quality-gate | 2 | Deep Check (default for manual invocation) |
/qg-full | 3 | Full Review with everything |
/qg-score | — | Calculate QS only, no review |
Trigger Check
Scan the output for risk indicators:
| Indicator | Example | Weight |
|---|
| Quantitative claims | "Market will grow 40%" | High |
| Forecasts / predictions | "By 2030, there will be..." | High |
| Causal claims | "X causes Y", "leads to" | Medium |
| Recommendations with consequences | "You should switch to..." | Medium |
| Regulatory / legal statements | "This complies with..." | High |
| Domain-specific technical claims | Standards, specifications | Medium |
Triage logic:
- 0 indicators → Level 0 (no check)
- 1 low indicator → Level 1
- 2+ indicators or manual
/qg → Level 2
/qg-full or gate recommendation → Level 3
Workflow
Level 1 (Quick Check)
- Run
/signal-check on the output
- Calculate Quality Score (QS)
- Report findings
Level 2 (Deep Check)
- Run
/signal-check on the output
- External verification for quantitative claims (web search, known sources)
- Consistency check against memory/project state
- Calculate QS
- Report with verification table
Level 3 (Full Review)
- Generate 2-4 review perspectives based on the output's domain
- Each perspective reviews the output
- External verification for all checkable claims
- Run
/challenge as adversarial stress-test
- Synthesize all findings
- Calculate QS
Quality Score (QS)
The QS combines 5 sub-metrics:
| Metric | Weight | What it measures |
|---|
| Source Coverage | 30% | % of quantitative claims backed by sources |
| Consistency | 25% | Contradictions with prior statements (memory, project state) |
| Confidence Marking | 20% | % of forecasts with explicit uncertainty ranges |
| Causality Transparency | 15% | % of causal claims with evidence or qualification |
| Domain Transparency | 10% | Are knowledge boundaries explicitly stated for specialized topics? |
When a metric is n/a (e.g., no numbers in output): redistribute its weight to remaining metrics.
Thresholds
| QS | Verdict | Meaning |
|---|
| > 80% | Release | Output is reliable |
| 50-80% | Revise | Output has weaknesses, address findings |
| < 50% | Unreliable | Fundamental revision needed |
Special rule: QS > 80% + at least one critical finding → Verdict stays "Revise".
Output Format
## Quality-Gate: [Output Topic]
> **Level:** [1/2/3] | **QS:** [Score]% | **Verdict:** [Release/Revise/Unreliable]
### Findings
🔴 **Critical**
- [Finding + correction suggestion]
🟡 **Questionable**
- [Finding + verification recommendation]
🟢 **Note**
- [Improvement potential]
### Verification
| Claim | Status | Source |
|-------|--------|--------|
| [Claim] | ✅ Verified / ⚠️ Unverifiable / ❌ Contradiction | [Source] |
### Quality Score
| Metric | Value |
|--------|-------|
| Source Coverage | X% |
| Consistency | X% |
| Confidence Marking | X% |
| Causality Transparency | X% |
| Domain Transparency | X% |
| **Total QS** | **X%** |
### Recommendation
[What should be revised and how]
Skill Chain Integration
/express → create output
→ (automatic) Quality-Gate trigger check
→ if findings: recommend revision
/analyze → create analysis
→ /qg → Deep Check
→ if QS < 80%: implement findings → /qg-score again
/strategy → design strategy
→ /qg-full → Full Review including /challenge
→ create hardened version
Limitations
What the quality-gate cannot do:
- Detect its own blind spots: An LLM checking its own output cannot catch systematic errors from training data. External verification mitigates but doesn't fully solve this.
- Simulate domain expertise: Review perspectives are limited to training knowledge. For highly specialized topics (specific regulations, proprietary standards), the gate is NOT reliable.
- Guarantee completeness: The gate checks what IS there, but cannot reliably identify what's MISSING — especially with domain blindness.
- Measure objective quality: The QS is a heuristic, not ground truth. It improves over time (calibration) but remains an approximation.
Rules
- Quality-gate does NOT replace signal-check or challenge — it orchestrates them
- Automatic trigger only at Level 1-2 — Level 3 is always manual or gate-recommended
- No self-review of same context — the gate checks the OUTPUT, not the process
- Token awareness — Level 1 costs ~500 extra tokens, Level 2 ~2000, Level 3 ~5000+
- QS is a signal, not truth — it reveals trends, doesn't replace human judgment
