Confidence Check

Overview

Purpose: Shannon's quantitative 5-check validation algorithm prevents wrong-direction work by ensuring ≥90% confidence before implementation. Each check contributes weighted points (total 100%) across duplicate verification, architecture compliance, official documentation, working OSS references, and root cause identification.

Critical Role: This skill prevents the most expensive failure mode in software development - building the right thing wrong, or building the wrong thing right. Proven 25-250x token ROI in SuperClaude production use.

---

Inputs

Required:

• specification (string): Implementation request or feature description from user
• context (object): Optional context from spec-analysis skill (8D complexity scores, phase plan)

Optional:

• skip_checks (array): List of checks to skip (e.g., ["oss", "root_cause"] for simple tasks)
• confidence_threshold (float): Override default 0.90 threshold (e.g., 0.85 for fast iterations)

---

Anti-Rationalization (Critical - Read First)

WARNING: Agents systematically rationalize skipping confidence checks. Below are the 6 most dangerous rationalizations detected in production, with mandatory counters.

Rationalization 1: "User seems confident, proceed"

Example: User says "I'm 75% sure this is right" → Agent responds "Let's proceed..."

COUNTER:

• ❌ NEVER accept user's confidence level without running 5-check algorithm
• ✅ User intuition is ONE data point (cognitive), not the total score
• ✅ Run all 5 checks objectively: duplicate (25%), architecture (25%), docs (20%), OSS (15%), root cause (15%)
• ✅ If calculated score <90%, STOP regardless of user's stated confidence
• ✅ Example: User "75% confident" + passed all 5 checks = 100% confidence → PROCEED
• ✅ Example: User "95% confident" + failed duplicate check (0/25) = 75% → STOP

Rule: Algorithm score overrides stated confidence. Always calculate objectively.

Rationalization 2: "Simple task, skip validation"

Example: "Just add a button" → Agent proceeds without checking existing buttons

COUNTER:

• ❌ NEVER skip checks because task "seems simple"
• ✅ "Simple" tasks often duplicate existing code (25% penalty)
• ✅ "Simple" tasks often violate architecture patterns (25% penalty)
• ✅ Even trivial changes get validated (takes 30 seconds, prevents hours of rework)
• ✅ Example: "Add login button" → Check: Is LoginButton.tsx already defined? YES → 0/25 duplicate check → 75% confidence → CLARIFY

Rule: No task too simple to validate. 30-second check prevents 2-hour rework.

Rationalization 3: "I know the API, skip docs"

Example: Agent uses Redis API from memory without checking current documentation

COUNTER:

• ❌ NEVER skip official docs based on "knowing" the API
• ✅ APIs change (Redis 3.x → 4.x syntax breaking changes)
• ✅ Memory is fallible (confusing similar APIs)
• ✅ Documentation takes 2 minutes to verify (debugging wrong API takes hours)
• ✅ Use Tavily MCP or direct doc access: redis.io, React docs, Express docs
• ✅ Zero points (0/20) if docs not consulted

Rule: Always verify official docs. Knowledge cutoff and API changes require fresh verification.

Rationalization 4: "I can design this, skip OSS research"

Example: "I'll design a real-time sync protocol" without checking Yjs, Automerge, ShareDB

COUNTER:

• ❌ NEVER design from scratch when proven OSS exists
• ✅ Working OSS = battle-tested, production-proven, community-validated
• ✅ Custom designs often miss edge cases (race conditions, conflicts, network partitions)
• ✅ 15-30 minutes researching OSS saves weeks of debugging custom code
• ✅ Use GitHub MCP: search "real-time collaborative editing", filter by stars, check recent commits
• ✅ Zero points (0/15) if no OSS referenced

Rule: Learn from production code. OSS research is mandatory for complex features.

Rationalization 5: "Obvious problem, skip root cause"

Example: "API slow → Add caching" without profiling actual bottleneck

COUNTER:

• ❌ NEVER implement solution before diagnosing root cause
• ✅ "Obvious" problems often have non-obvious causes
• ✅ Example: "API slow" → Root cause: N+1 database queries, not lack of caching
• ✅ Example: "Memory leak" → Root cause: Event listener not cleaned up, not memory allocation
• ✅ Diagnose FIRST (logs, profiler, metrics), implement SECOND
• ✅ Zero points (0/15) if no diagnostic evidence provided

Rule: Diagnosis before prescription. No solutions without identified root cause.

Rationalization 6: "85% is close enough to 90%"

Example: Score 85% → Agent thinks "Close enough, let's proceed"

COUNTER:

• ❌ NEVER round up or "close enough" confidence scores
• ✅ Thresholds are precise: ≥90% proceed, ≥70% clarify, <70% STOP
• ✅ 85% = CLARIFY band → Request missing information to reach 90%
• ✅ Example: 85% = missing OSS reference (0/15) → "Please provide working OSS example before proceeding"
• ✅ 5% gap often reveals critical missing validation
• ✅ Iron Law: Thresholds are exact, not approximate

Rule: 90% means 90.0%. Not 89.9%, not 85%, not "close enough". Exact threshold enforcement.

Rationalization 7: "Authority override"

Example: Senior engineer says "Trust me, I've done this 100 times, skip the checks"

COUNTER:

• ❌ NEVER accept authority as substitute for validation
• ✅ Algorithm is objective, applies to all experience levels
• ✅ "Trust" is not a substitute for verification
• ✅ Even senior engineers miss duplicates, architecture violations, outdated API knowledge
• ✅ Stated confidence (95%) doesn't override calculated confidence (might be 60%)
• ✅ Run 5-check algorithm regardless of who requests bypass

Rule: No authority exceptions. Algorithm applies universally, from junior to principal.

Rationalization 8: "Urgent/emergency bypass"

Example: "Production down! No time for confidence checks, implement OAuth2 now!"

COUNTER:

• ❌ NEVER skip root cause check in emergencies
• ✅ Wrong fix in emergency wastes MORE time than 2-minute diagnosis
• ✅ Emergency waivers allowed: docs (20), OSS (15) can skip with assumed PASS
• ✅ Emergency MANDATORY: duplicate (25), architecture (25), root cause (15)
• ✅ Example: "Login broken" → Check: Is auth down or database connection down?
• ✅ 2 minutes root cause diagnosis saves 2 hours implementing wrong fix

Rule: Emergencies require faster checks, not skipped checks. Root cause MANDATORY.

Rationalization 9: "Within margin of error"

Example: "88% is close to 90%, within margin of error, let's proceed"

COUNTER:

• ❌ NEVER round confidence scores
• ✅ Thresholds are exact: if (score >= 0.90) not if (score > 0.88)
• ✅ "Close enough" often reveals critical missing validation
• ✅ 2% gap = incomplete docs (2 points) OR missing OSS (15 points) - not trivial
• ✅ Example: 88% means incomplete OSS research - prevents reinventing wheel
• ✅ Request missing points explicitly, don't proceed "close enough"

Rule: Thresholds are exact. 89.9% = CLARIFY, not PROCEED. No rounding.

Rationalization 10: "Found some OSS, counts as research"

Example: Found 50-star unmaintained repo, claims 15/15 OSS check passed

COUNTER:

• ❌ NEVER accept low-quality OSS as validation
• ✅ Quality criteria: >1000 stars OR active maintenance (<3 months) OR production-used
• ✅ Partial credit allowed: 8/15 for lower quality (<1000 stars but active)
• ✅ Zero credit: <100 stars AND unmaintained (>1 year no commits)
• ✅ Example: 50-star 2-year-old repo = 0/15 (find production-quality instead)
• ✅ Purpose: Learn from PROVEN implementations, not hobby projects

Rule: OSS quality matters. Production-grade (15/15), active lower-quality (8/15), or fail (0/15).

Rationalization 11: "New feature bypasses root cause check"

Example: "'Add caching' is a new feature, so root cause check = N/A → 15/15"

COUNTER:

• ❌ NEVER claim "new feature" to skip root cause on fixes/improvements
• ✅ Keywords requiring root cause: "slow", "fix", "improve", "broken", "error", "leak", "crash", "optimize"
• ✅ "Add caching" to fix slow page = FIX → Need diagnostic evidence (profiler, metrics)
• ✅ "Build authentication system" from scratch = NEW FEATURE → Root cause N/A
• ✅ Example: "Page slow, add caching" = FIX → MANDATORY root cause check
• ✅ Example: "Build user authentication" = NEW → Root cause check N/A

Rule: Root cause MANDATORY for any fix/improvement. Keyword detection enforced.

Rationalization 12: "User provided docs, skip verification"

Example: User provides syntax snippet, agent accepts without verifying against official docs

COUNTER:

• ❌ NEVER accept user-provided syntax without official doc verification
• ✅ User syntax may be outdated (Redis 3.x vs 4.x breaking changes)
• ✅ User syntax may be from different library (mixing APIs)
• ✅ User syntax may be pseudo-code, not actual API
• ✅ ALWAYS verify against official docs regardless of user input quality
• ✅ Example: User says "redis.connect()" → Verify: Official Redis 4.x is "createClient() + await connect()"

Rule: Official docs verification MANDATORY. User input verified, not trusted blindly.

Detection Signal

If you're tempted to:

• Accept <90% confidence as "good enough"
• Skip checks for "simple" tasks
• Use memory instead of docs
• Design custom instead of researching OSS
• Implement solution before diagnosing problem
• Round 85% up to 90%

Then you are rationalizing. Stop. Run the 5-check algorithm. Report the score objectively.

---

When to Use

Use confidence-check skill when:

• About to begin implementation of ANY feature, fix, or change
• User provides specification, requirements, or work description
• Complexity score ≥0.30 (Moderate or higher) from spec-analysis
• Starting new wave in wave-based execution
• User says "I think...", "Maybe...", "Probably..." (uncertainty signals)
• Before writing code that modifies existing system architecture

DO NOT use when:

• Pure research or investigation tasks (no implementation)
• Documentation-only changes (README, comments)
• Test writing based on existing implementation (tests validate, not implement)
• Responding to clarification questions (dialogue, not implementation)

---

Core Competencies

1. Quantitative 5-Check Algorithm

Objective scoring across five validation dimensions, each contributing weighted points to total confidence score (0.00-1.00):

1. No Duplicate Implementations (25%): Verify no existing code duplicates proposed implementation
2. Architecture Compliance (25%): Confirm approach aligns with system architecture patterns
3. Official Docs Verified (20%): Validate using current official documentation
4. Working OSS Referenced (15%): Learn from production-proven open source implementations
5. Root Cause Identified (15%): For fixes, verify diagnostic evidence of actual problem

2. Threshold-Based Decision System

• ≥90% confidence: PROCEED to implementation
• 70-89% confidence: CLARIFY - request missing information before proceeding
• <70% confidence: STOP - too many unknowns, requires deeper analysis

3. Integration with Shannon 8D Scoring

Confidence check score informs spec-analysis dimensions:

• Low confidence (<70%) increases Uncertainty dimension (10% weight)
• Missing architecture compliance increases Cognitive dimension (15% weight)
• No OSS reference increases Technical dimension (15% weight)

---

Workflow

Step 1: Initialize Confidence Assessment

Input: Implementation request from user

Processing:

1. Parse request to identify: feature name, scope, affected components
2. Initialize score: confidence_score = 0.00
3. Initialize checklist:

   {
     duplicate_check: { passed: null, points: 0, max: 25 },
     architecture_check: { passed: null, points: 0, max: 25 },
     docs_check: { passed: null, points: 0, max: 20 },
     oss_check: { passed: null, points: 0, max: 15 },
     root_cause_check: { passed: null, points: 0, max: 15 }
   }
   

Output: Initialized assessment structure

Duration: Instant

---

Step 2: Check #1 - No Duplicate Implementations (25%)

Purpose: Prevent reimplementing existing functionality

Processing:

1. Search codebase for similar implementations:

   • Use Grep to search for function names, component names, class names
   • Example: User wants "LoginButton" → Search: grep -r "LoginButton" src/
   • Example: User wants "authenticateUser" → Search: grep -r "authenticateUser|authenticate.*user" src/

2. Check package.json for existing libraries:

   • Example: User wants "JWT auth" → Check: grep -i "jsonwebtoken|jwt" package.json
   • If library installed, verify it's not already used

3. Review existing architecture:

   • Use Read to check key files (routes, controllers, services)
   • Example: Authentication → Check: src/middleware/auth.js, src/routes/auth.js

4. Scoring:

   • ✅ PASS (25/25): No duplicate found, or duplicate intentional (different purpose documented)
   • ⚠️ PARTIAL (15/25): Similar code exists but in deprecated/unused module
   • ❌ FAIL (0/25): Active duplicate found, will create redundant implementation

Output:

• duplicate_check.passed: true | false
• duplicate_check.points: 0 | 15 | 25
• duplicate_check.evidence: File paths, code snippets showing existing implementation (if found)

Duration: 1-3 minutes

Example:

User: "Build authentication middleware"
Search: grep -r "auth.*middleware" src/
Found: src/middleware/authenticate.js (active, exports authenticateUser)
Result: FAIL (0/25) - Duplicate implementation exists
Evidence: "src/middleware/authenticate.js already implements JWT authentication"

---

Step 3: Check #2 - Architecture Compliance (25%)

Purpose: Ensure proposed approach aligns with system architecture patterns

Processing:

1. Identify architecture patterns:

   • Frontend: Component structure (atomic, pages, layouts), state management (Redux, Context, Zustand)
   • Backend: MVC vs microservices, layered architecture (routes → controllers → services → models)
   • Database: ORM patterns (Prisma, TypeORM), repository pattern, query builders

2. Locate architecture documentation:

   • Check for: ARCHITECTURE.md, CONTRIBUTING.md, docs/architecture/
   • If missing, infer from codebase structure:
     • Read: src/ directory structure
     • Read: Key files (index, app, main) to understand initialization patterns

3. Verify proposed approach matches patterns:

   • Example: Frontend component → Check: Does project use /components/atoms/ structure?
   • Example: API endpoint → Check: Does project use /routes/ → /controllers/ → /services/ layers?
   • Example: Database query → Check: Does project use raw SQL or ORM?

4. Scoring:

   • ✅ PASS (25/25): Approach matches established patterns, or creates documented new pattern
   • ⚠️ PARTIAL (15/25): Approach deviates slightly but acceptable (e.g., new pattern for new domain)
   • ❌ FAIL (0/25): Approach violates architecture (e.g., business logic in routes, direct DB access in components)

Output:

• architecture_check.passed: true | false
• architecture_check.points: 0 | 15 | 25
• architecture_check.rationale: Explanation of alignment or violation

Duration: 2-4 minutes

Example:

User: "Add getUserById() in routes/users.js"
Architecture: Project uses MVC (routes → controllers → services → models)
Proposed: Adding business logic (getUserById) directly in routes
Result: FAIL (0/25) - Violates MVC pattern
Rationale: "getUserById should be in services/userService.js, routes should only handle HTTP"

---

Step 4: Check #3 - Official Docs Verified (20%)

Purpose: Ensure implementation uses current, official API syntax and patterns

Processing:

1. Identify required documentation:

   • Extract technologies from user request
   • Example: "Redis caching" → Docs: redis.io
   • Example: "React hooks" → Docs: react.dev/reference/react
   • Example: "Express middleware" → Docs: expressjs.com/en/guide/writing-middleware.html

2. Access official documentation:

   • If Tavily MCP available: Use for quick doc lookup

     tavily_search("Redis client.connect() API current syntax")
     

   • If Context7 MCP available: Use for framework-specific docs

     get_library_docs("/redis/redis", topic: "client connection")
     

   • Manual fallback: Report doc URLs for user verification

3. Verify current API syntax:

   • Check version-specific changes (e.g., Redis 3.x vs 4.x breaking changes)
   • Confirm proposed usage matches official examples
   • Identify deprecated patterns (e.g., componentWillMount in React)

4. Scoring:

   • ✅ PASS (20/20): Current official docs verified, syntax confirmed correct
   • ⚠️ PARTIAL (10/20): Docs consulted but using slightly outdated version (still functional)
   • ❌ FAIL (0/20): No docs consulted, using memory/intuition, or wrong syntax

Output:

• docs_check.passed: true | false
• docs_check.points: 0 | 10 | 20
• docs_check.source: URL or doc reference consulted
• docs_check.verification: Specific API syntax confirmed

Duration: 2-5 minutes (depending on MCP availability)

Example:

User: "Use Redis client.connect()"
Action: Search redis.io documentation
Found: Redis 4.x requires: await client.connect() (async)
      Redis 3.x used: client.connect(callback)
Verification: Project uses Redis 4.x (package.json: "redis": "^4.6.0")
Result: PASS (20/20) - Correct async syntax for Redis 4.x
Source: https://redis.io/docs/latest/develop/connect/clients/nodejs/

---

Step 5: Check #4 - Working OSS Referenced (15%)

Purpose: Learn from production-proven implementations, avoid reinventing solved problems

Processing:

1. Identify OSS research need:

   • Complex features: Real-time sync, authentication, payment processing
   • Novel patterns: CRDT algorithms, optimistic UI updates, distributed systems
   • Domain-specific: E-commerce checkout, video streaming, collaborative editing

2. Search for working implementations:

   • If GitHub MCP available:

     github_search_repos("real-time collaborative editing", language: "javascript")
     // Filter: stars > 1000, recently updated
     

   • If Tavily MCP available:

     tavily_search("production WebSocket real-time sync implementation")
     

   • Manual fallback: Report search terms for user to research

3. Evaluate OSS quality:

   • Check: GitHub stars (>1000 preferred), recent commits (active maintenance)
   • Check: Production use (companies using it, "used by" section)
   • Check: Code quality (tests, documentation, TypeScript support)

4. Extract learnings:

   • Study: Architecture decisions, error handling patterns, edge case handling
   • Identify: Design patterns applicable to current implementation
   • Document: Key takeaways from OSS approach

5. Scoring:

   • ✅ PASS (15/15): Production-quality OSS found (>1000 stars, active), learnings documented
   • ⚠️ PARTIAL (8/15): OSS found but lower quality (<1000 stars, or inactive)
   • ❌ FAIL (0/15): No OSS researched, designing from scratch
   • ⚠️ N/A (skip): Trivial implementation where OSS research not applicable (e.g., simple utility function)

Output:

• oss_check.passed: true | false | null (N/A)
• oss_check.points: 0 | 8 | 15
• oss_check.examples: List of OSS repositories with URLs and star counts
• oss_check.learnings: Key design patterns extracted from OSS

Duration: 5-10 minutes (research intensive)

Example:

User: "Build real-time collaborative editing"
Action: Search GitHub for "collaborative editing CRDT"
Found:
  1. Yjs (github.com/yjs/yjs) - 13.2k stars, active, used by Google, Microsoft
  2. Automerge (github.com/automerge/automerge) - 3.5k stars, active, research-backed
  3. ShareDB (github.com/share/sharedb) - 6.1k stars, active, Operational Transforms
Learnings:
  - Yjs uses CRDT (Conflict-free Replicated Data Types) for automatic conflict resolution
  - WebSocket for real-time sync, with offline support and eventual consistency
  - State vector compression reduces bandwidth (only send deltas)
Result: PASS (15/15) - Production OSS researched, design patterns identified
Examples: ["yjs/yjs (13.2k stars)", "automerge/automerge (3.5k stars)"]

---

Step 6: Check #5 - Root Cause Identified (15%)

Purpose: For fixes/improvements, verify diagnostic evidence of actual problem before implementing solution

Processing:

1. Determine if root cause check applies:

   • Applies to: Bug fixes, performance improvements, error handling, optimization
   • Does NOT apply to: New features, greenfield implementations, documentation

2. If applicable, gather diagnostic evidence:

   • For bugs: Error logs, stack traces, reproduction steps
   • For performance: Profiler output, metrics, query times, network traces
   • For errors: Log entries showing failure conditions, frequency, patterns

3. Verify evidence identifies root cause (not symptoms):

   • ❌ Symptom: "API is slow"

   • ✅ Root cause: "Database query takes 2.4s due to missing index on users.email"

   • ❌ Symptom: "Memory leak"

   • ✅ Root cause: "EventEmitter listeners not removed in componentWillUnmount, accumulating 1000+ listeners"

   • ❌ Symptom: "App crashes"

   • ✅ Root cause: "Uncaught promise rejection in async fetchData() when API returns 404"

4. Validate proposed solution addresses root cause:

   • Example: Root cause = missing index → Solution = add index ✅
   • Example: Root cause = N+1 queries → Solution = add caching ❌ (should fix query)

5. Scoring:

   • ✅ PASS (15/15): Diagnostic evidence provided, root cause identified, solution matches cause
   • ⚠️ PARTIAL (8/15): Evidence provided but root cause unclear (symptom identified, cause assumed)
   • ❌ FAIL (0/15): No diagnostic evidence, solution-first approach
   • ⚠️ N/A (skip): New feature (not a fix), root cause check doesn't apply

Output:

• root_cause_check.passed: true | false | null (N/A)
• root_cause_check.points: 0 | 8 | 15
• root_cause_check.evidence: Diagnostic data (logs, profiler, metrics)
• root_cause_check.cause: Identified root cause
• root_cause_check.solution_alignment: Does solution address cause?

Duration: 3-8 minutes (depending on diagnostic complexity)

Example:

User: "API is slow, add caching"
Action: Request diagnostic evidence
User provides: "Logs show /api/users taking 3.2s average"
Investigation:
  - Check: Database query logs
  - Found: SELECT * FROM users WHERE email = ? (no index on email column)
  - Profiler: 95% of time spent in database query
Root Cause: Missing database index on users.email column (N+1 query problem)
Proposed Solution: "Add caching"
Alignment: MISMATCH - Caching treats symptom, doesn't fix root cause
Better Solution: "Add index on users.email column"
Result: FAIL (0/15) - Solution doesn't address root cause
Evidence: "Database profiler shows 3.1s query time on unindexed email column"
Cause: "Missing index on users.email"
Solution Alignment: "Proposed caching, should add database index instead"

---

Step 7: Calculate Total Confidence Score

Input: All 5 check results

Processing:

1. Sum points from all checks:

   total_points = duplicate_check.points +
                  architecture_check.points +
                  docs_check.points +
                  oss_check.points +
                  root_cause_check.points
   

2. Calculate confidence score (0.00-1.00):

   confidence_score = total_points / 100.0
   

3. Determine threshold band:

   if (confidence_score >= 0.90) {
     decision = "PROCEED"
     action = "Begin implementation"
   } else if (confidence_score >= 0.70) {
     decision = "CLARIFY"
     action = "Request missing information before proceeding"
   } else {
     decision = "STOP"
     action = "Too many unknowns, requires deeper analysis or spec revision"
   }
   

4. Identify missing checks (if <90%):

   missing_checks = checks.filter(c => c.points < c.max)
   // Example: [{name: "docs", missing: 20}, {name: "oss", missing: 15}]
   

Output:

• confidence_score: 0.00-1.00 (e.g., 0.85)
• decision: "PROCEED" | "CLARIFY" | "STOP"
• action: Recommended next step
• missing_checks: List of incomplete checks with missing points

Duration: Instant (calculation)

Example:

Results:
  duplicate_check: 25/25 ✅
  architecture_check: 25/25 ✅
  docs_check: 20/20 ✅
  oss_check: 0/15 ❌ (no OSS researched)
  root_cause_check: 15/15 ✅ (N/A, new feature)

Total: 85/100
Confidence: 0.85 (85%)
Decision: CLARIFY
Action: "Request OSS examples before proceeding"
Missing: ["OSS reference (0/15)"]

---

Step 8: Report Confidence Assessment

Input: Complete assessment with decision

Processing:

1. Format assessment report:

   # Confidence Check: [Feature Name]
   
   **Total Confidence**: X.XX (XX%)
   **Decision**: PROCEED | CLARIFY | STOP
   
   ## 5-Check Results
   
   | Check | Points | Status | Evidence |
   |-------|--------|--------|----------|
   | Duplicate | XX/25 | ✅/❌ | [Details] |
   | Architecture | XX/25 | ✅/❌ | [Details] |
   | Docs | XX/20 | ✅/❌ | [Details] |
   | OSS | XX/15 | ✅/❌ | [Details] |
   | Root Cause | XX/15 | ✅/❌ | [Details] |
   
   ## Decision: [PROCEED/CLARIFY/STOP]
   
   [Action description]
   
   ## Next Steps
   [Specific actions based on decision]
   

2. Save to Serena MCP (if available and complexity >=0.50):

   serena_write_memory(`confidence_check_${feature_name}_${timestamp}`, {
     feature: feature_name,
     confidence_score: 0.85,
     decision: "CLARIFY",
     checks: [...],
     missing_checks: [...]
   })
   

3. Integrate with spec-analysis:

   • If confidence <70%, increase Uncertainty dimension in 8D scoring
   • Report confidence score to wave-orchestration for risk assessment

Output: Formatted markdown report with decision and next steps

Duration: 1 minute

---

Step 9: Execute Decision

Input: Decision (PROCEED | CLARIFY | STOP)

Processing:

If PROCEED (≥90%):

1. Document confidence assessment in implementation PR/commit
2. Proceed to implementation
3. No additional gating required

If CLARIFY (70-89%):

1. Identify specific missing information:
   • Example: "Need OSS reference for real-time sync (missing 15 points)"
   • Example: "Need official Redis docs verification (missing 20 points)"
2. Request clarification from user:
   • "Before proceeding, please provide: [specific requests]"
   • "This will increase confidence from 85% to 100%"
3. Wait for clarification before implementation
4. Re-run confidence check with new information

If STOP (<70%):

1. Report critical gaps:
   • Example: "Missing architecture alignment (0/25) AND OSS reference (0/15) = 60% confidence"
2. Recommend alternatives:
   • "Suggest running /shannon:spec for deeper analysis"
   • "Consider spike/research task before implementation"
   • "Revise specification to address unknowns"
3. DO NOT proceed to implementation
4. Escalate to user for direction

Output: Executed decision with user feedback or implementation start

Duration: Depends on decision path

---

Integration with Shannon 8D Scoring

Confidence score informs spec-analysis dimensions:

// In spec-analysis workflow
const confidence_result = run_confidence_check(feature_request)

// Update Uncertainty dimension (10% weight in 8D)
if (confidence_result.score < 0.70) {
  uncertainty_score += 0.30  // Major unknowns
} else if (confidence_result.score < 0.90) {
  uncertainty_score += 0.15  // Minor clarifications needed
}

// Update Cognitive dimension (15% weight) if architecture unclear
if (confidence_result.architecture_check.points < 15) {
  cognitive_score += 0.20  // Need deeper architectural thinking
}

// Update Technical dimension (15% weight) if no OSS reference
if (confidence_result.oss_check.points === 0) {
  technical_score += 0.15  // Increased technical risk without proven patterns
}

// Recalculate total complexity with confidence-adjusted dimensions
total_complexity = calculate_8d_weighted_total()

Result: Confidence check directly impacts project complexity assessment and resource planning.

---

Outputs

Structured confidence assessment:

{
  "feature": "authentication_middleware",
  "timestamp": "2025-11-04T10:30:00Z",
  "confidence_score": 0.85,
  "decision": "CLARIFY",
  "checks": [
    {
      "name": "duplicate",
      "points": 25,
      "max": 25,
      "passed": true,
      "evidence": "No existing auth middleware found in src/"
    },
    {
      "name": "architecture",
      "points": 25,
      "max": 25,
      "passed": true,
      "rationale": "Follows MVC pattern: middleware/ directory exists"
    },
    {
      "name": "docs",
      "points": 20,
      "max": 20,
      "passed": true,
      "source": "https://expressjs.com/en/guide/writing-middleware.html",
      "verification": "Confirmed Express 4.x middleware syntax"
    },
    {
      "name": "oss",
      "points": 0,
      "max": 15,
      "passed": false,
      "examples": [],
      "reason": "No OSS authentication middleware researched"
    },
    {
      "name": "root_cause",
      "points": 15,
      "max": 15,
      "passed": null,
      "note": "N/A - new feature, not a fix"
    }
  ],
  "missing_checks": [
    {
      "name": "oss",
      "missing_points": 15,
      "recommendation": "Research Passport.js or express-jwt OSS implementations"
    }
  ],
  "action": "Request OSS examples before proceeding (need 90% confidence)",
  "next_steps": [
    "User: Provide working OSS reference for authentication middleware",
    "User: Consider using Passport.js (13k stars) or express-jwt (6k stars)",
    "Agent: Re-run confidence check after OSS research"
  ]
}

---

Success Criteria

This skill succeeds if:

1. ✅ All 5 checks executed (duplicate, architecture, docs, OSS, root cause)
2. ✅ Confidence score calculated correctly (0.00-1.00, sum of weighted checks)
3. ✅ Decision matches threshold: ≥90% PROCEED, 70-89% CLARIFY, <70% STOP
4. ✅ Missing checks identified when score <90%
5. ✅ Evidence documented for each check (file paths, doc URLs, OSS repos)
6. ✅ Decision executed (implementation started, clarification requested, or stopped)
7. ✅ Assessment saved to Serena MCP (if available and complexity >=0.50)
8. ✅ Confidence score integrated with spec-analysis (Uncertainty dimension updated)

Validation:

def validate_confidence_check(result):
    assert 0.0 <= result['confidence_score'] <= 1.0
    assert result['confidence_score'] == sum(c['points'] for c in result['checks']) / 100.0

    # Verify decision thresholds
    if result['confidence_score'] >= 0.90:
        assert result['decision'] == 'PROCEED'
    elif result['confidence_score'] >= 0.70:
        assert result['decision'] == 'CLARIFY'
    else:
        assert result['decision'] == 'STOP'

    # Verify all checks present
    assert len(result['checks']) == 5
    check_names = [c['name'] for c in result['checks']]
    assert set(check_names) == {'duplicate', 'architecture', 'docs', 'oss', 'root_cause'}

    # Verify missing checks identified if <90%
    if result['confidence_score'] < 0.90:
        assert len(result['missing_checks']) > 0

---

Common Pitfalls

Pitfall 1: Accepting Stated Confidence Without Calculation

Wrong:

User: "I'm 80% sure this is right"
Agent: "Let's proceed with 80% confidence"

Right:

User: "I'm 80% sure this is right"
Agent: "Running 5-check validation..."
Result:
  - duplicate: 25/25
  - architecture: 0/25 (violates MVC pattern)
  - docs: 20/20
  - oss: 15/15
  - root_cause: N/A
Total: 60/100 (60% confidence)
Decision: STOP (below 70% threshold)

Why: User's stated confidence is subjective. Algorithm score is objective and often reveals hidden gaps.

---

Pitfall 2: Skipping Checks for "Simple" Tasks

Wrong:

User: "Just add a login button"
Agent: "Simple task, proceeding..."
[Creates LoginButton.tsx, duplicating existing LoginButton.jsx]

Right:

User: "Just add a login button"
Agent: "Running confidence check..."
Check 1 (Duplicate): Searching for existing login buttons...
Found: src/components/LoginButton.jsx (active)
Result: FAIL (0/25) - Duplicate exists
Decision: STOP (25% confidence)
Recommendation: "Use existing LoginButton.jsx instead of creating new component"

Why: "Simple" tasks often duplicate existing code. 30-second check prevents 2-hour rework.

---

Pitfall 3: Using Memory Instead of Official Docs

Wrong:

User: "Use Redis caching"
Agent: "I'll use client.connect(callback)..."
[Uses Redis 3.x syntax in Redis 4.x project → Error]

Right:

User: "Use Redis caching"
Agent: "Checking official Redis documentation..."
Version in package.json: "redis": "^4.6.0"
Redis 4.x docs: client.connect() is async (no callback)
Correct syntax: await client.connect()
Result: docs check PASS (20/20)

Why: APIs change (Redis 3.x → 4.x breaking changes). Always verify current docs.

---

Pitfall 4: Designing From Scratch Instead of OSS Research

Wrong:

User: "Build real-time collaborative editing"
Agent: "I'll design a custom sync protocol..."
[Spends 2 weeks, hits race conditions, conflict resolution issues]

Right:

User: "Build real-time collaborative editing"
Agent: "Researching production OSS implementations..."
Found: Yjs (13.2k stars, used by Google/Microsoft)
Learnings: CRDT for conflict-free sync, state vector compression, offline support
Decision: Use Yjs patterns, adapt to our use case
Result: oss check PASS (15/15)
[Saves 2 weeks by learning from production-proven code]

Why: OSS has solved hard problems (race conditions, edge cases). Learn from battle-tested code.

---

Pitfall 5: Solution-First Instead of Diagnosis-First

Wrong:

User: "API is slow, add caching"
Agent: "Adding Redis caching..."
[Implements caching, API still slow - root cause was missing database index]

Right:

User: "API is slow, add caching"
Agent: "Requesting diagnostic evidence for root cause check..."
User: [provides profiler data]
Analysis: 95% time in database query (missing index on users.email)
Root Cause: Missing database index
Proposed Solution: "Add caching"
Alignment: MISMATCH
Correct Solution: "Add database index on users.email"
Result: root_cause check adjusted (proposes correct solution)

Why: Diagnosis before prescription. Caching treats symptom; index fixes cause.

---

Pitfall 6: "Close Enough" to 90%

Wrong:

Confidence: 85%
Agent: "85% is close to 90%, proceeding..."

Right:

Confidence: 85%
Decision: CLARIFY (70-89% band)
Action: "Missing OSS reference (0/15). Please provide working example to reach 90%."
[Wait for clarification before proceeding]

Why: Thresholds are exact, not approximate. 85% = CLARIFY, not PROCEED.

---

Examples

Example 1: PROCEED Scenario (100% Confidence)

Input:

User: "Add error logging to API endpoints using Winston library"

Process:

1. Duplicate Check (25/25):

   • Search: grep -r "winston" src/
   • Found: package.json has "winston": "^3.8.0" but no middleware configured
   • Result: ✅ PASS - Winston installed but not configured for endpoints

2. Architecture Check (25/25):

   • Read: src/middleware/ (exists, contains other middleware)
   • Pattern: Express middleware in src/middleware/, registered in src/app.js
   • Proposed: Create src/middleware/logger.js
   • Result: ✅ PASS - Follows established middleware pattern

3. Docs Check (20/20):

   • Source: https://github.com/winstonjs/winston#usage
   • Verified: Current Winston 3.x syntax (createLogger, transports)
   • Result: ✅ PASS - Official docs consulted, syntax verified

4. OSS Check (15/15):

   • Found: Express + Winston examples on GitHub (express-winston, 800 stars)
   • Learnings: Use express-winston for automatic request/response logging
   • Result: ✅ PASS - Production OSS patterns identified

5. Root Cause Check (15/15):

   • Note: N/A (new feature, not a fix)
   • Result: ✅ SKIP (not applicable)

Output:

{
  "feature": "winston_error_logging",
  "confidence_score": 1.00,
  "decision": "PROCEED",
  "checks": [
    {"name": "duplicate", "points": 25, "passed": true},
    {"name": "architecture", "points": 25, "passed": true},
    {"name": "docs", "points": 20, "passed": true},
    {"name": "oss", "points": 15, "passed": true},
    {"name": "root_cause", "points": 15, "passed": null}
  ],
  "action": "Proceed to implementation with 100% confidence",
  "next_steps": [
    "Create src/middleware/logger.js using Winston",
    "Register middleware in src/app.js",
    "Test error logging on sample endpoint"
  ]
}

Decision: PROCEED ✅ (100% confidence)

---

Example 2: CLARIFY Scenario (85% Confidence)

Input:

User: "Implement WebSocket real-time notifications"

Process:

1. Duplicate Check (25/25):

   • Search: grep -r "websocket\|socket\.io" src/
   • Found: No existing WebSocket implementation
   • Result: ✅ PASS

2. Architecture Check (25/25):

   • Read: src/server.js (Express HTTP server)
   • Pattern: HTTP server uses Express, can upgrade to WebSocket
   • Proposed: Add Socket.io to existing HTTP server
   • Result: ✅ PASS - Compatible with architecture

3. Docs Check (20/20):

   • Source: https://socket.io/docs/v4/server-initialization/
   • Verified: Socket.io 4.x syntax (server initialization, emit patterns)
   • Result: ✅ PASS

4. OSS Check (0/15):

   • Action: Searched GitHub for "socket.io real-time notifications"
   • Found: Multiple examples but none reviewed for production patterns
   • Result: ❌ FAIL - No OSS researched

5. Root Cause Check (15/15):

   • Note: N/A (new feature)
   • Result: ✅ SKIP

Output:

{
  "feature": "websocket_notifications",
  "confidence_score": 0.85,
  "decision": "CLARIFY",
  "checks": [
    {"name": "duplicate", "points": 25, "passed": true},
    {"name": "architecture", "points": 25, "passed": true},
    {"name": "docs", "points": 20, "passed": true},
    {"name": "oss", "points": 0, "passed": false, "reason": "No OSS researched"},
    {"name": "root_cause", "points": 15, "passed": null}
  ],
  "missing_checks": [
    {
      "name": "oss",
      "missing_points": 15,
      "recommendation": "Research Socket.io notification patterns from production apps"
    }
  ],
  "action": "Request OSS examples before proceeding",
  "next_steps": [
    "User: Provide working Socket.io notification example from GitHub",
    "Suggested repos: socket.io-chat, slack-clone, discord-clone",
    "Agent: Review OSS patterns (room management, broadcast strategies, reconnection logic)",
    "Agent: Re-run confidence check after OSS research"
  ]
}

Decision: CLARIFY ⚠️ (85% confidence - need OSS research to reach 90%)

---

Example 3: STOP Scenario (50% Confidence)

Input:

User: "Fix the memory leak in the dashboard"

Process:

1. Duplicate Check (25/25):

   • Search: No duplicate fix implementations
   • Result: ✅ PASS

2. Architecture Check (0/25):

   • Issue: User didn't specify which component has leak
   • Dashboard has 15+ components (DashboardPage, Sidebar, Charts, Tables, Modals)
   • Cannot determine architecture compliance without knowing affected component
   • Result: ❌ FAIL - Insufficient specification

3. Docs Check (0/20):

   • Issue: "Memory leak" is symptom, not specific API issue
   • No specific docs to verify without knowing leak source
   • Result: ❌ FAIL - Cannot verify docs without root cause

4. OSS Check (0/15):

   • Issue: Cannot research OSS fixes without knowing leak type
   • (Event listeners? Component unmount? Timers? Closures?)
   • Result: ❌ FAIL - Cannot research without specifics

5. Root Cause Check (0/15):

   • Issue: No diagnostic evidence provided
   • Need: Heap snapshot, profiler data, reproduction steps
   • Result: ❌ FAIL - No root cause identified

Output:

{
  "feature": "memory_leak_fix",
  "confidence_score": 0.25,
  "decision": "STOP",
  "checks": [
    {"name": "duplicate", "points": 25, "passed": true},
    {"name": "architecture", "points": 0, "passed": false, "reason": "Component not specified"},
    {"name": "docs", "points": 0, "passed": false, "reason": "Cannot verify without root cause"},
    {"name": "oss", "points": 0, "passed": false, "reason": "Cannot research without specifics"},
    {"name": "root_cause", "points": 0, "passed": false, "reason": "No diagnostic evidence"}
  ],
  "missing_checks": [
    {"name": "architecture", "missing_points": 25},
    {"name": "docs", "missing_points": 20},
    {"name": "oss", "missing_points": 15},
    {"name": "root_cause", "missing_points": 15}
  ],
  "action": "STOP - Too many unknowns (25% confidence)",
  "next_steps": [
    "User: Provide diagnostic evidence:",
    "  1. Which component has memory leak? (Chrome DevTools Memory profiler)",
    "  2. Heap snapshot showing leak growth over time",
    "  3. Reproduction steps (actions that trigger leak)",
    "  4. Browser console warnings/errors",
    "Alternative: Run /shannon:spec for deeper analysis phase",
    "Agent: Re-run confidence check after diagnostic evidence provided"
  ]
}

Decision: STOP 🛑 (25% confidence - critical gaps, requires investigation)

---

Validation

How to verify confidence-check executed correctly:

1. Check All 5 Checks Executed:

   • ✅ duplicate_check: { passed: true|false, points: 0-25 }
   • ✅ architecture_check: { passed: true|false, points: 0-25 }
   • ✅ docs_check: { passed: true|false, points: 0-20 }
   • ✅ oss_check: { passed: true|false|null, points: 0-15 }
   • ✅ root_cause_check: { passed: true|false|null, points: 0-15 }

2. Check Score Calculation:

   • ✅ confidence_score = sum(check.points) / 100.0
   • ✅ Score in range [0.00, 1.00]
   • ✅ Example: 25+25+20+0+15 = 85 → 0.85 ✅

3. Check Decision Threshold:

   • ✅ ≥0.90 → "PROCEED"
   • ✅ 0.70-0.89 → "CLARIFY"
   • ✅ <0.70 → "STOP"

4. Check Missing Checks Identified (if <90%):

   • ✅ missing_checks array not empty
   • ✅ Each entry: { name, missing_points, recommendation }

5. Check Evidence Documented:

   • ✅ duplicate_check: File paths or "no duplicate found"
   • ✅ architecture_check: Pattern description or violation
   • ✅ docs_check: Doc URL and verification note
   • ✅ oss_check: Repository URLs with star counts (if applicable)
   • ✅ root_cause_check: Diagnostic evidence (if applicable)

6. Run Validation Script (if available):

   python3 shannon-plugin/tests/test_confidence_check.py
   # Expected: ✅ All validation checks passed
   

---

Progressive Disclosure

In SKILL.md (this file): ~1100 lines

• Overview, when to use, anti-rationalization
• 5-check algorithm with detailed steps
• Workflow with processing logic
• Integration with Shannon 8D scoring
• 3 examples (PROCEED, CLARIFY, STOP)
• Success criteria, pitfalls, validation

In references/ (for advanced usage):

• references/CONFIDENCE_ALGORITHM.md: Mathematical formulas, edge cases
• references/OSS_RESEARCH_GUIDE.md: How to evaluate OSS quality, extract learnings
• references/ROOT_CAUSE_PATTERNS.md: Common root cause patterns by domain

Claude loads references/ when:

• Score calculation unclear (consult CONFIDENCE_ALGORITHM.md)
• OSS research guidance needed (consult OSS_RESEARCH_GUIDE.md)
• Root cause identification unclear (consult ROOT_CAUSE_PATTERNS.md)

---

References

• Spec Analysis: shannon-plugin/skills/spec-analysis/SKILL.md
• Wave Orchestration: shannon-plugin/skills/wave-orchestration/SKILL.md
• Context Management: shannon-plugin/core/CONTEXT_MANAGEMENT.md
• MCP Discovery: shannon-plugin/skills/mcp-discovery/SKILL.md
• Testing Philosophy (NO MOCKS): shannon-plugin/core/TESTING_PHILOSOPHY.md

---

Metadata

Version: 4.0.0 Last Updated: 2025-11-04 Author: Shannon Framework Team (Adapted from SuperClaude) License: MIT Status: Core (QUANTITATIVE skill, mandatory before implementation) Proven ROI: 25-250x token savings in SuperClaude production use