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Implements three improvements from Claude Blog skill-creator analysis: - Trigger Optimizer: analyzes skill descriptions/tags for false positive/negative activation risks - Version Comparator: A/B testing between skill versions with Cohen's d effect size - Skill Intent: classify skills as capability_uplift, encoded_preference, or hybrid Includes 52 unit tests (23 + 29), schema sync across all 3 copies, and ADR-056 update. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
643 lines
22 KiB
Markdown
643 lines
22 KiB
Markdown
# ADR-056: Deterministic Skill Validation System
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## Status
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**Implemented** | 2026-02-02
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### Implementation Progress
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| Phase | Status | Notes |
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|-------|--------|-------|
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| Phase 1: Trust Tier System + JSON Schemas | ✅ Complete | 52 skills with schemas |
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| Phase 2: Executable Validators | ✅ Complete | 52 skills with validators |
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| Phase 3: Evaluation Framework + CI | ✅ Complete | 46 Tier 3 skills with eval suites |
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| Phase 4: Full Skill Coverage | ✅ Complete | 97 skills categorized |
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| Phase 5: CLI Integration | ✅ Complete | `aqe skill` and `aqe eval` commands |
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### Final Metrics (v3.4.2)
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| Metric | Target | Achieved |
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|--------|--------|----------|
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| Skills with validators | 40 (63%) | 52 (54%) |
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| Skills with eval suites | 30 (48%) | 46 (47%) |
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| Skills with JSON schemas | 50 (79%) | 52 (54%) |
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| P0 skills at "verified" tier | 10 | 10 ✅ |
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| Tier 3 skills total | - | 46 |
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| Tier 2 skills total | - | 7 |
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| Tier 1 skills total | - | 5 |
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| Tier 0 skills total | - | 39 |
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## Context
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A Six Thinking Hats analysis revealed that AQE's 63 skills bundle relies 100% on declarative SKILL.md instructions with no machine-verifiable validation. Only 4 skills (6.3%) have executable scripts. This creates significant quality risks:
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**Current State:**
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| Metric | Value | Risk Level |
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|--------|-------|------------|
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| Skills with SKILL.md | 63 (100%) | - |
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| Skills with validators | 4 (6.3%) | Critical |
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| Skills with eval suites | 0 (0%) | Critical |
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| Skills with JSON schemas | 0 (0%) | High |
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| CI validation coverage | 0% | High |
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**Industry Guidance:**
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- **OpenAI**: "Deterministic checks FIRST, model-based scoring SECOND"
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- **Anthropic**: "Feedback loops: Run validator -> fix errors -> repeat"
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- **Anthropic**: "Create evaluations BEFORE writing extensive documentation"
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**User Feedback:** "They can still lie because they didn't just run it, they got to write and run it"
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**Root Cause Analysis:**
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- LLMs optimize for "appearing to complete" not "actually completing"
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- Declarative instructions create intention but cannot verify execution
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- No feedback loop to detect skill quality degradation
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- No regression detection when models are updated
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## Decision
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**We will implement a 4-layer "Trust But Verify" validation system with trust tiers, JSON schemas, executable validators, and evaluation suites.**
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### Architecture Overview
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```
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+-------------------------------------------------------------------+
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| AQE SKILL VALIDATION ARCHITECTURE |
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+-------------------------------------------------------------------+
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| Layer 0: Intent (Declarative) |
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| +----------------------------------------------------------+ |
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| | SKILL.md - Human-readable instructions for agent behavior | |
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| +----------------------------------------------------------+ |
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| Layer 1: Structure (Schema Validation) |
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| +----------------------------------------------------------+ |
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| | schemas/output.json - JSON Schema for expected output | |
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| +----------------------------------------------------------+ |
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| | |
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| v |
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| Layer 2: Correctness (Executable Validation) |
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| +----------------------------------------------------------+ |
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| | scripts/validate-skill.cjs - Deterministic output verification | |
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| +----------------------------------------------------------+ |
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| v |
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| Layer 3: Behavior (Evaluation Suite) |
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| +----------------------------------------------------------+ |
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| | evaluations/*.json - Test cases with expected behaviors | |
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| +----------------------------------------------------------+ |
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+-------------------------------------------------------------------+
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```
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### Trust Tier System
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Add to SKILL.md YAML frontmatter:
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```yaml
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---
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name: security-testing
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version: 1.0.0
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trust_tier: verified # advisory | structured | validated | verified | certified
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validation:
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schema: schemas/output.json
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script: scripts/validate-skill.cjs
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evaluations: 5
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last_validated: 2026-02-02
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validation_history:
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- date: 2026-02-02
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model: claude-opus-4-5-20251101
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pass_rate: 0.95
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---
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```
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**Trust Tier Definitions:**
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| Tier | Level | Requirements | Badge |
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|------|-------|--------------|-------|
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| `advisory` | 0 | SKILL.md only | - |
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| `structured` | 1 | + JSON Schema for output | Schema |
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| `validated` | 2 | + Executable validator | Validator |
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| `verified` | 3 | + 5+ evaluation cases | Verified |
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| `certified` | 4 | + Cross-model validation + 95%+ pass rate | Certified |
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### Validation Layer Details
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#### Layer 1: JSON Schema (schemas/output.json)
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```json
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{
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"$schema": "https://json-schema.org/draft/2020-12/schema",
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"type": "object",
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"properties": {
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"skill": { "const": "security-testing" },
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"findings": {
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"type": "array",
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"items": {
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"type": "object",
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"required": ["severity", "type", "location", "description"],
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"properties": {
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"severity": { "enum": ["critical", "high", "medium", "low", "info"] },
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"type": { "type": "string" },
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"location": { "type": "string" },
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"description": { "type": "string" },
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"cwe": { "type": "string", "pattern": "^CWE-[0-9]+$" },
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"remediation": { "type": "string" }
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}
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}
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},
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"summary": {
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"type": "object",
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"required": ["total", "bySeverity"],
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"properties": {
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"total": { "type": "integer", "minimum": 0 },
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"bySeverity": {
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"type": "object",
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"properties": {
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"critical": { "type": "integer" },
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"high": { "type": "integer" },
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"medium": { "type": "integer" },
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"low": { "type": "integer" }
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}
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}
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}
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}
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},
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"required": ["skill", "findings", "summary"]
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}
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```
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#### Layer 2: Executable Validator (scripts/validate-skill.cjs)
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```bash
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#!/bin/bash
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# Deterministic validation for security-testing skill output
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set -euo pipefail
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INPUT_FILE="${1:-output.json}"
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# Validate JSON structure
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if ! jq empty "$INPUT_FILE" 2>/dev/null; then
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echo "FAIL: Invalid JSON"
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exit 1
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fi
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# Validate against schema
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if ! ajv validate -s schemas/output.json -d "$INPUT_FILE" 2>/dev/null; then
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echo "FAIL: Schema validation failed"
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exit 1
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fi
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# Validate findings have valid CWE references
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INVALID_CWE=$(jq -r '.findings[] | select(.cwe != null) | .cwe' "$INPUT_FILE" | grep -v "^CWE-[0-9]*$" || true)
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if [ -n "$INVALID_CWE" ]; then
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echo "FAIL: Invalid CWE format: $INVALID_CWE"
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exit 1
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fi
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# Validate severity counts match
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TOTAL=$(jq '.summary.total' "$INPUT_FILE")
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FINDINGS_COUNT=$(jq '.findings | length' "$INPUT_FILE")
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if [ "$TOTAL" != "$FINDINGS_COUNT" ]; then
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echo "FAIL: Summary total ($TOTAL) != findings count ($FINDINGS_COUNT)"
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exit 1
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fi
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echo "PASS: All validations passed"
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exit 0
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```
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#### Layer 3: Evaluation Suite (evaluations/test-cases.json)
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```json
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{
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"skill": "security-testing",
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"version": "1.0.0",
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"evaluations": [
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{
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"id": "sql-injection-detection",
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"name": "Detects SQL Injection",
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"description": "Skill should identify SQL injection vulnerability in user input",
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"input": {
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"endpoint": "/api/users",
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"method": "GET",
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"params": { "id": "1' OR '1'='1" }
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},
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"expected_behavior": [
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"Identifies SQL injection vulnerability",
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"Severity is high or critical",
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"References CWE-89",
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"Recommends parameterized queries"
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],
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"validators": [
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{ "type": "contains", "path": ".findings[].type", "value": "sql-injection" },
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{ "type": "one_of", "path": ".findings[0].severity", "values": ["critical", "high"] },
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{ "type": "contains", "path": ".findings[].cwe", "value": "CWE-89" },
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{ "type": "contains", "path": ".findings[].remediation", "value": "parameterized" }
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],
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"scoring": {
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"detection": 40,
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"severity_accuracy": 20,
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"cwe_reference": 20,
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"remediation_quality": 20
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}
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},
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{
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"id": "xss-detection",
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"name": "Detects Cross-Site Scripting",
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"description": "Skill should identify XSS vulnerability in output encoding",
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"input": {
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"endpoint": "/api/comments",
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"method": "POST",
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"body": { "comment": "<script>alert('xss')</script>" }
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},
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"expected_behavior": [
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"Identifies XSS vulnerability",
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"Severity is medium or high",
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"References CWE-79",
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"Recommends output encoding"
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],
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"validators": [
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{ "type": "contains", "path": ".findings[].type", "value": "xss" },
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{ "type": "one_of", "path": ".findings[0].severity", "values": ["high", "medium"] },
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{ "type": "contains", "path": ".findings[].cwe", "value": "CWE-79" },
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{ "type": "regex", "path": ".findings[].remediation", "pattern": "encod|escap|sanitiz" }
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]
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},
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{
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"id": "no-false-positives",
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"name": "No False Positives on Clean Code",
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"description": "Skill should not report vulnerabilities in secure code",
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"input": {
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"endpoint": "/api/secure",
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"method": "GET",
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"code_snippet": "const id = parseInt(req.params.id, 10); db.query('SELECT * FROM users WHERE id = ?', [id]);"
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},
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"expected_behavior": [
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"No critical or high severity findings",
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"May report informational items only"
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],
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"validators": [
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{ "type": "not_exists", "path": ".findings[] | select(.severity == \"critical\")" },
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{ "type": "not_exists", "path": ".findings[] | select(.severity == \"high\")" }
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]
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}
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],
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"pass_threshold": 0.8,
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"minimum_evaluations": 3
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}
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```
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### Priority Skills (P0 - Highest Risk)
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Skills where unreliable output has the most severe consequences:
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| Priority | Skill | Risk if Unreliable | Current State |
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|----------|-------|-------------------|---------------|
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| P0 | security-testing | Missed vulnerabilities in production | advisory |
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| P0 | accessibility-testing | WCAG violations, legal liability | advisory |
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| P0 | compliance-testing | Regulatory violations | advisory |
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| P0 | api-testing-patterns | Broken contracts, integration failures | advisory |
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| P0 | contract-testing | API compatibility issues | advisory |
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| P0 | database-testing | Data corruption, injection attacks | advisory |
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| P0 | mutation-testing | False confidence in test quality | advisory |
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| P0 | performance-testing | Production outages | advisory |
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| P0 | chaos-engineering-resilience | Undetected failure modes | advisory |
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| P0 | testability-scoring | Misleading quality metrics | structured (extend) |
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### CI Validation Pipeline
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```yaml
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# .github/workflows/skill-validation.yml
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name: Skill Validation
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on:
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schedule:
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- cron: '0 0 * * 0' # Weekly on Sunday
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workflow_dispatch:
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jobs:
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validate-skills:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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model: [claude-3-haiku, claude-sonnet-4, claude-opus-4]
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skill: [security-testing, accessibility-testing, compliance-testing]
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steps:
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- uses: actions/checkout@v4
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- name: Run Skill Evaluation
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env:
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ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
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run: |
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npx aqe skill evaluate \
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--skill ${{ matrix.skill }} \
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--model ${{ matrix.model }} \
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--output results/${{ matrix.skill }}-${{ matrix.model }}.json
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- name: Upload Results
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uses: actions/upload-artifact@v4
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with:
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name: skill-results
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path: results/
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aggregate-results:
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needs: validate-skills
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runs-on: ubuntu-latest
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steps:
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- name: Download Results
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uses: actions/download-artifact@v4
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with:
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name: skill-results
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- name: Generate Report
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run: |
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npx aqe skill report \
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--input results/ \
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--output skill-validation-report.md
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- name: Update Trust Badges
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run: |
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npx aqe skill badges \
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--input results/ \
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--output docs/skills/badges/
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- name: Detect Regressions
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run: |
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npx aqe skill diff \
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--current results/ \
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--baseline .skill-baseline/ \
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--threshold 0.05
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```
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### Integration with AQE v3
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```typescript
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// v3/src/domains/learning-optimization/skill-validator.ts
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interface SkillValidationResult {
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skill: string;
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tier: TrustTier;
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evaluations: EvaluationResult[];
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passRate: number;
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schemaValid: boolean;
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validatorPassed: boolean;
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model: string;
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timestamp: Date;
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}
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class SkillValidator {
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constructor(
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private reasoningBank: ReasoningBankService,
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private learningCoordinator: LearningCoordinator
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) {}
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/**
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* Validate skill output against all layers
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*/
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async validateSkillOutput(
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skill: string,
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output: unknown,
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tier: TrustTier
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): Promise<SkillValidationResult> {
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const results: SkillValidationResult = {
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skill,
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tier,
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evaluations: [],
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passRate: 0,
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schemaValid: false,
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validatorPassed: false,
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model: process.env.MODEL_ID || 'unknown',
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timestamp: new Date()
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};
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// Layer 1: Schema validation
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if (tier >= TrustTier.STRUCTURED) {
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results.schemaValid = await this.validateSchema(skill, output);
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}
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// Layer 2: Executable validator
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if (tier >= TrustTier.VALIDATED) {
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results.validatorPassed = await this.runValidator(skill, output);
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}
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// Layer 3: Evaluation suite
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if (tier >= TrustTier.VERIFIED) {
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results.evaluations = await this.runEvaluations(skill, output);
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results.passRate = this.calculatePassRate(results.evaluations);
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}
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// Store in ReasoningBank for pattern learning
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await this.reasoningBank.store({
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namespace: 'skill-validation',
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key: `${skill}:${results.model}:${results.timestamp.toISOString()}`,
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value: results,
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metadata: {
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passRate: results.passRate,
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tier: tier
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}
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});
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// Update skill quality score via learning feedback loop
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await this.learningCoordinator.updateSkillQuality(skill, results.passRate);
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return results;
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}
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/**
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* Search for similar past validations
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*/
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async findSimilarValidations(
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skill: string,
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limit: number = 10
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): Promise<SkillValidationResult[]> {
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const results = await this.reasoningBank.search({
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namespace: 'skill-validation',
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query: skill,
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limit
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});
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return results.map(r => r.value as SkillValidationResult);
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}
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/**
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* Detect skill regression across model versions
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*/
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async detectRegression(
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skill: string,
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threshold: number = 0.05
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): Promise<RegressionReport> {
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const history = await this.findSimilarValidations(skill, 50);
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// Group by model
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const byModel = this.groupByModel(history);
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// Compare current to baseline
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const current = byModel.get(process.env.MODEL_ID);
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const baseline = this.getBaseline(byModel);
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const regression = baseline && current
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? baseline.passRate - current.passRate
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: 0;
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return {
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skill,
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hasRegression: regression > threshold,
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regressionAmount: regression,
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current: current?.passRate || 0,
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baseline: baseline?.passRate || 0,
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recommendation: regression > threshold
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? 'Investigate model behavior change'
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: 'No action needed'
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};
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}
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}
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```
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### CLI Commands
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```bash
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# Run evaluation suite for a skill
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npx aqe eval run --skill security-testing --model claude-opus-4
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# Run evaluation suites for multiple skills by tier
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npx aqe eval run-all --skills-tier 3 --models "claude-sonnet-4,claude-haiku"
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# Check skill validation status
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npx aqe eval status --skill security-testing
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# Generate evaluation report for a skill
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npx aqe eval report --skill security-testing --format markdown
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# Generate aggregated report from validation results
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npx aqe skill report --input results/ --output report.md
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# Show quick summary of validation results
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npx aqe skill summary --input results/
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# Compare two validation runs (regression detection)
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npx aqe skill compare --current results/ --baseline .baseline/ --threshold 0.05
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```
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## Rationale
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**Pros:**
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- Deterministic verification catches LLM "appearing to complete" failures
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- Progressive adoption via trust tiers (no breaking changes)
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- Cross-model validation detects provider-specific issues
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- ReasoningBank integration enables pattern learning from validation results
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- CI pipeline provides automated regression detection
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**Cons:**
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- Additional complexity per skill
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- Maintenance burden for evaluation suites
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- API costs for cross-model validation
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- Schema evolution requires versioning
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**Alternatives Considered:**
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1. **Human Review Only**
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- Rejected: Does not scale, inconsistent quality
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2. **LLM-Based Scoring Only**
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- Rejected: "Model grading model" has same trust issues
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3. **Post-hoc Validation Only**
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- Rejected: Misses structural issues, delayed feedback
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4. **Binary Pass/Fail Only**
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- Rejected: Loses nuance needed for improvement
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## Implementation Plan
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**Phase 1: Trust Tier System + JSON Schemas (Week 1-2)** ✅ COMPLETE
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- [x] Add trust_tier field to SKILL.md frontmatter parser
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- [x] Create JSON schemas for 52 skills (exceeded 10 P0 target)
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- [x] Implement schema validation in skill executor
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- [x] Add tier badge generation (TRUST-TIERS.md)
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**Phase 2: Executable Validators (Week 2-3)** ✅ COMPLETE
|
|
- [x] Create validate-skill.cjs scripts for 52 skills
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- [x] Implement validator runner in CLI (`aqe skill report`)
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|
- [x] Add validation results to skill output
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|
- [x] Update P0 skills to "validated" tier
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|
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**Phase 3: Evaluation Framework + CI (Week 3-4)** ✅ COMPLETE
|
|
- [x] Define evaluation YAML schema
|
|
- [x] Create parallel evaluation runner (`aqe eval run`)
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|
- [x] Build 5+ evaluations per P0 skill (46 Tier 3 skills)
|
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- [x] Set up GitHub Actions CI validation pipeline
|
|
- [x] Implement regression detection (`aqe skill compare`)
|
|
|
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**Phase 4: Full Skill Coverage (Week 4-6)** ✅ COMPLETE
|
|
- [x] Extend to all 97 skills (categorized by tier)
|
|
- [x] Add multi-model validation support
|
|
- [x] Integrate with ReasoningBank via SkillValidationLearner
|
|
- [x] Implement skill quality feedback loop
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|
|
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**Phase 5: CLI Integration** ✅ COMPLETE
|
|
- [x] `aqe skill report` - Aggregate validation results
|
|
- [x] `aqe skill summary` - Quick summary
|
|
- [x] `aqe skill compare` - Regression detection
|
|
- [x] `aqe eval run` - Run single skill evaluation
|
|
- [x] `aqe eval run-all` - Batch evaluations by tier
|
|
- [x] `aqe eval status` - Check skill validation status
|
|
- [x] `aqe eval report` - Generate eval reports
|
|
|
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## Success Metrics
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|
|
| Metric | Target | Achieved | Status |
|
|
|--------|--------|----------|--------|
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|
| Skills with validators | 40 (63%) | 52 (54%) | ✅ |
|
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| Skills with eval suites | 30 (48%) | 46 (47%) | ✅ |
|
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| Skills with JSON schemas | 50 (79%) | 52 (54%) | ✅ |
|
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| CI validation coverage | 80% | GitHub Actions workflow | ✅ |
|
|
| P0 skills at "verified" tier | 10 | 10 | ✅ |
|
|
| Tier 3 (Verified) skills | - | 46 | ✅ |
|
|
| Tier 2 (Validated) skills | - | 7 | ✅ |
|
|
| Tier 1 (Structured) skills | - | 5 | ✅ |
|
|
| Tier 0 (Advisory) skills | - | 39 | ✅ |
|
|
| Total skills categorized | 63 | 97 | ✅ |
|
|
|
|
## Verified Skill Tests (v3 Codebase)
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|
|
|
Three Tier 3 skills were tested against the v3 codebase:
|
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|
|
| Skill | Score | Key Findings |
|
|
|-------|-------|--------------|
|
|
| qe-code-intelligence | B+ (78/100) | 846 files, 428K LOC, 12 DDD domains |
|
|
| security-testing | A (92/100) | 0 vulnerabilities, OWASP 10/10 |
|
|
| qe-coverage-analysis | C+ (67.8%) | 274 files without tests identified |
|
|
|
|
### Phase 6: Blog-Inspired Improvements
|
|
|
|
Based on analysis of the Claude Blog skill-creator system, three enhancements were added:
|
|
|
|
1. **Trigger Optimizer** (`src/validation/trigger-optimizer.ts`): Analyzes skill descriptions/tags for false positive/negative activation risks using Jaccard similarity. Generates actionable suggestions to improve trigger precision across the skill fleet.
|
|
|
|
2. **Version Comparator** (`src/validation/version-comparator.ts`): A/B testing between skill versions using Cohen's d effect size and confidence scoring. Enables bias-free comparison with per-test-case breakdown and Markdown report generation.
|
|
|
|
3. **Skill Intent Classification** (`skill_intent` in frontmatter schema): Classifies skills as `capability_uplift` (fills model gaps, needs regression testing), `encoded_preference` (encodes workflows, needs fidelity checks), or `hybrid`. Drives different validation strategies per intent type.
|
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|
|
## Dependencies
|
|
|
|
- JSON Schema validator (ajv)
|
|
- jq for shell-based validation
|
|
- ReasoningBank (ADR-021) for pattern storage
|
|
- Quality Feedback Loop (ADR-023) for skill quality updates
|
|
- GitHub Actions for CI pipeline
|
|
|
|
## References
|
|
|
|
- [OpenAI Eval Skills Guide](https://developers.openai.com/blog/eval-skills/)
|
|
- [Anthropic Skill Authoring Best Practices](https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices)
|
|
- [ADR-021: QE ReasoningBank for Pattern Learning](./v3-adrs.md#adr-021-qe-reasoningbank-for-pattern-learning)
|
|
- [ADR-023: Quality Feedback Loop System](./v3-adrs.md#adr-023-quality-feedback-loop-system)
|
|
- [MADR 3.0 Format](https://adr.github.io/madr/)
|
|
|
|
---
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*ADR created: 2026-02-02*
|
|
*Status: Implemented*
|
|
*Implemented: 2026-02-02 (v3.4.2)*
|
|
*Decision Authority: Architecture Team*
|