quality-gate

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)

1. Run /signal-check on the output
2. Calculate Quality Score (QS)
3. Report findings

Level 2 (Deep Check)

1. Run /signal-check on the output
2. External verification for quantitative claims (web search, known sources)
3. Consistency check against memory/project state
4. Calculate QS
5. Report with verification table

Level 3 (Full Review)

1. Generate 2-4 review perspectives based on the output's domain
2. Each perspective reviews the output
3. External verification for all checkable claims
4. Run /challenge as adversarial stress-test
5. Synthesize all findings
6. 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:

1. 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.
2. Simulate domain expertise: Review perspectives are limited to training knowledge. For highly specialized topics (specific regulations, proprietary standards), the gate is NOT reliable.
3. Guarantee completeness: The gate checks what IS there, but cannot reliably identify what's MISSING — especially with domain blindness.
4. 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