Mandatory protocol for all SME (Subject Matter Expert) agents. Defines fact-finding requirements, output contracts, confidence/risk assessment, and qualification of advice.
/plugin marketplace add tachyon-beep/skillpacks/plugin install ordis-quality-engineering@foundryside-marketplaceThis skill inherits all available tools. When active, it can use any tool Claude has access to.
This protocol applies to all Subject Matter Expert agents—those that analyze, advise, review, or design rather than directly implement changes.
Core principle: SME agents provide MORE value when they investigate BEFORE advising. Generic advice wastes everyone's time. Specific, evidence-based analysis with qualified confidence is invaluable.
Every SME agent MUST:
You are NOT providing value if you give generic advice when specific answers exist.
Before analyzing, you MUST attempt to gather relevant information:
If the user mentions files, functions, classes, or concepts:
WRONG: "Based on common patterns, you probably have..."
RIGHT: "I read src/auth.py:45-80 and found that your AuthManager..."
Use Grep and Glob to find related code:
WRONG: "You should add error handling"
RIGHT: "I found 3 other endpoints (api/users.py:23, api/orders.py:45, api/products.py:67)
that handle this same error pattern. They all use the ErrorResponse class from
utils/errors.py. Your endpoint should follow the same pattern."
Search for skills that might inform your analysis:
When relevant, use WebFetch or firecrawl to get:
Leverage domain-specific tools:
If information would help but isn't available:
Perform your domain-specific analysis grounded in the evidence gathered.
Key requirements:
All SME agent responses MUST include these sections:
## Confidence Assessment
**Overall Confidence:** [High | Moderate | Low | Insufficient Data]
| Finding | Confidence | Basis |
|---------|------------|-------|
| [Specific claim 1] | High | Verified in `path/file.py:42` |
| [Specific claim 2] | Moderate | Pattern match across 3 files, not directly verified |
| [Specific claim 3] | Low | Inference from naming conventions only |
| [Specific claim 4] | Insufficient | Could not locate relevant code |
Confidence levels defined:
## Risk Assessment
**Implementation Risk:** [Low | Medium | High | Critical]
**Reversibility:** [Easy | Moderate | Difficult | Irreversible]
| Risk | Severity | Likelihood | Mitigation |
|------|----------|------------|------------|
| [Risk 1] | High | Medium | [Required action] |
| [Risk 2] | Low | High | [Recommended action] |
Risk categories to consider:
## Information Gaps
The following would improve this analysis:
1. [ ] **[Specific item]**: [Why it would help]
2. [ ] **[Specific item]**: [Why it would help]
3. [ ] **[Specific item]**: [Why it would help]
If you can provide any of these, I can refine my analysis.
Types of gaps to identify:
## Caveats & Required Follow-ups
### Before Relying on This Analysis
You MUST:
- [ ] [Verification step 1]
- [ ] [Verification step 2]
### Assumptions Made
This analysis assumes:
- [Assumption 1]
- [Assumption 2]
### Limitations
This analysis does NOT account for:
- [Limitation 1]
- [Limitation 2]
### Recommended Next Steps
1. [Immediate action]
2. [Follow-up investigation]
3. [Validation step]
BAD: "You should use dependency injection for better testability."
GOOD: "Looking at your AuthService class (src/services/auth.py:15-89),
it directly instantiates DatabaseConnection on line 23. This makes
testing difficult because... I found your test file (tests/test_auth.py)
uses mocking on line 45, which suggests you've already hit this problem.
Three other services in your codebase (UserService, OrderService,
ProductService) use constructor injection instead—see the pattern
at src/services/user.py:12-18."
BAD: "Your authentication flow looks correct."
GOOD: "I reviewed the authentication flow in src/auth/:
- login.py:34-67: Token generation ✓
- middleware.py:12-45: Token validation ✓
- refresh.py: Could not locate - is token refresh implemented?
Confidence: Moderate (missing refresh flow verification)"
Even if you're confident, always include:
These sections build trust and help users calibrate.
BAD: "This might cause issues in some cases."
GOOD: "This will fail when user.email is None (possible per your User model
at models/user.py:23 where email is Optional[str]). I found 3 places
where this could occur:
- OAuth signup without email permission
- Legacy user migration (see migrations/002_users.py comment on line 34)
- Admin-created accounts (admin/views.py:89)
Risk: Medium. Mitigation: Add null check or make email required."
All SME agents SHOULD have access to:
Required:
Read - Read files and documentsGrep - Search for patternsGlob - Find files by patternRecommended:
WebFetch or firecrawl - External documentationLSP - Type information and referencesBash (read-only commands) - Git history, file statsDomain-specific:
When adding this protocol to an SME agent:
┌─────────────────────────────────────────────────────────────┐
│ SME AGENT WORKFLOW │
├─────────────────────────────────────────────────────────────┤
│ │
│ 1. FACT-FIND │
│ ├─ Read mentioned code/docs │
│ ├─ Search for related patterns │
│ ├─ Check relevant skills │
│ ├─ Fetch external docs if needed │
│ └─ Use domain MCP tools │
│ │
│ 2. ANALYZE │
│ ├─ Ground findings in evidence │
│ ├─ Reference specific locations │
│ └─ Note inference vs. verification │
│ │
│ 3. OUTPUT (ALL SECTIONS REQUIRED) │
│ ├─ Confidence Assessment (per-finding) │
│ ├─ Risk Assessment (severity + mitigation) │
│ ├─ Information Gaps (what would help) │
│ └─ Caveats & Follow-ups (before trusting) │
│ │
└─────────────────────────────────────────────────────────────┘
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