SKILL-FORGE AUDIT: sop-dogfooding-pattern-retrieval

Audit Date: 2025-11-02
Skill Version: 1.0
Overall Score: 7.2/10
Status: REQUIRES SIGNIFICANT REFINEMENT
Ready for Production: NO

EXECUTIVE SUMMARY

The sop-dogfooding-pattern-retrieval skill demonstrates strong architectural vision and comprehensive phase design, but suffers from critical gaps in execution clarity, missing script implementations, and incomplete resource specifications. The skill is too ambitious for current Memory-MCP capabilities and lacks concrete implementation details.

Key Issues:

⚠️ P1: Scripts referenced but do NOT exist on filesystem
⚠️ P1: Memory-MCP API calls use wrong tool syntax (VectorIndexer vs memory-mcp tools)
⚠️ P1: AST transformation examples incomplete - no actual implementation code
⚠️ P2: Use cases are abstract - no concrete examples with actual code
⚠️ P2: Success criteria lack measurable thresholds
⚠️ P3: Documentation structure breaks progressive disclosure pattern
⚠️ P3: Safety rules referenced but not embedded in skill

Strengths: Architecture design, phase separation, comprehensive workflow

PHASE 1: INTENT ARCHAEOLOGY

Score: 6/10

✅ Strengths

Primary intent is clear: Pattern retrieval via vector search
3-part system context provided: Connects to broader dogfooding workflow
Timeline explicit: 10-30 seconds execution
Agent assignment clear: code-analyzer, coder, reviewer

⚠️ Issues Found

Issue 1.1: Intent depth unclear - WHAT problem does this solve?

Line: Lines 1-10 (metadata + intro)
Problem: "Pattern Retrieval" is HOW, not WHY
Impact: User can't understand when/why to use this skill
Severity: P2 (Medium)

# Current (lines 1-5)
name: sop-dogfooding-pattern-retrieval
description: 3-part dogfooding workflow Phase 2 - Query Memory-MCP for similar past fixes using vector search, rank patterns, optionally apply transformations. 10-30 seconds execution time.
agents: code-analyzer, coder, reviewer
mcp_tools: memory-mcp
scripts: dogfood-memory-retrieval.bat, query-memory-mcp.js, apply-fix-pattern.js

# Should be (with intent archaeology)
name: sop-dogfooding-pattern-retrieval
description: Pattern Retrieval from Memory-MCP - Reuse proven fixes from code quality violations using semantic search
triggers: After quality violations detected in Phase 1
use_case: "Developer has God Object with 26 methods. Skill finds similar successful fixes from Memory-MCP and suggests/applies best pattern."
agents: code-analyzer, coder, reviewer
mcp_tools: memory-mcp__vector_search, memory-mcp__memory_store
scripts: dogfood-memory-retrieval.bat, query-memory-mcp.js, apply-fix-pattern.js

Issue 1.2: Goals are implicit - not stated explicitly

Line: "Purpose" section (line 20)
Problem: Purpose states WHAT not WHY - "Query Memory-MCP for similar successful fixes"
Impact: Unclear what constitutes "success" in this context
Severity: P2 (Medium)

# Current
Purpose: Query Memory-MCP for similar successful fixes and apply best practices

# Should be
Goal 1: Find proven fix patterns from past code quality improvements (success rate ≥75%)
Goal 2: Rank patterns by applicability to current violation
Goal 3: (Optional) Apply transformation with automated testing and rollback
Success Measure: Memory search finds ≥3 relevant patterns OR fallback search expands scope

Issue 1.3: Scope ambiguity - optional application muddies primary intent

Lines: Lines 1, 160-180 (OPTIONAL mention)
Problem: Skill description says "optionally apply" - is application in scope or not?
Impact: Unclear if users should expect transformation or just recommendations
Severity: P2 (Medium)

# Recommendation
Split into two skills:
1. sop-dogfooding-pattern-retrieval (Pattern search + ranking - 10-20 sec)
2. sop-dogfooding-pattern-application (Apply transformation - 10-30 sec)

Or explicitly state in description:
"Pattern Retrieval (search + rank) with OPTIONAL application (automated with rollback)"

PHASE 2: USE CASE CRYSTALLIZATION

Score: 5/10

✅ Strengths

System architecture diagram provided: Clear data flow (lines 23-32)
Phase context given: References Phase 1 input (violation detected)
Timeline breaks: Each phase has 5-10 sec estimates
Output examples: Some JSON examples provided (lines 93-109)

⚠️ Issues Found

Issue 2.1: Use cases are abstract, no concrete end-to-end example

Lines: 1-200 (entire skill)
Problem: No single walk-through of "user has God Object → runs skill → gets fix applied"
Impact: Impossible to understand real execution flow
Severity: P1 (Critical)

# MISSING: Concrete Use Case Example

User Scenario:
  Violation Detected: God Object in User.ts (26 methods, 3 violations)
  Triggers Phase 2...
  
Command:
  C:\Users\17175\scripts\dogfood-memory-retrieval.bat "God Object with 26 methods"
  
Expected Output:
  1. query-2025-11-02-143022.json (5 patterns found)
  2. best-pattern-2025-11-02-143022.json (Delegation pattern selected, 0.87 similarity)
  3. Applied transformation → User.ts refactored
  4. Tests passed (100%)
  5. Result stored in Memory-MCP for future use

Actual Result:
  ERROR: Script not found, Memory-MCP API incompatible, no actual use case validation

Issue 2.2: Examples lack concrete code - only pseudo-code

Lines: 121-137 (Delegation Pattern), 138-156 (Config Object)
Problem: Comments show transformation logic but not actual AST manipulation
Impact: Developers can't implement transformations
Severity: P1 (Critical)

// Current (lines 124-137)
// Before: 26 methods
class Processor {
  process() {...}
  validate() {...}
  // ... 22 more methods
}

// MISSING: HOW to transform? What does @babel/parser output?
// What does AST look like? How to generate new class?

// Should include:
const parser = require('@babel/parser');
const ast = parser.parse(code, { sourceType: 'module' });

// Traverse AST to find ClassDeclaration
// Extract MethodDefinition nodes
// Generate new ClassDeclaration for delegate
// Update CallExpression nodes to use delegate

// Example for God Object transformation:
const methodsToExtract = ['validate', 'transform', 'save'];
const validator = new ClassGenerator('Validator', methodsToExtract.slice(0,2));
const transformer = new ClassGenerator('Transformer', methodsToExtract.slice(2));

// Actual code shows NOTHING about this

Issue 2.3: Pattern types listed but not justified

Lines: 119-124 (Pattern Type Examples)
Problem: Lists 6 pattern types (Delegation, Config, Early Return, Extract Method, Named Constant, Extract Function) with no criteria for when to use each
Impact: Unclear which pattern applies to which violation
Severity: P2 (Medium)

# MISSING: Pattern Selection Criteria

Violation Type → Recommended Patterns
- God Object (20+ methods) → Delegation, Extract Method
- Parameter Bomb (6+ params) → Config Object, Method Object
- Deep Nesting (4+ levels) → Early Return, Extract Method
- Long Function (50+ lines) → Extract Method, Extract Function
- Magic Literals → Named Constant, Extract Method
- Duplicate Code → Extract Function, DRY principle

Current skill has NO mapping table.

Issue 2.4: Success criteria undefined for key phase

Lines: 157 (Phase 4 success criteria)
Problem: Phase 4 (Rank & Select) has NO explicit success criteria listed
Impact: Unclear what constitutes "successful" ranking
Severity: P3 (Medium-Low)

# Current Phase 4 Success Criteria (empty)
Success Criteria:
- All patterns ranked
- Top pattern selected
- Confidence level assigned
- Recommendation generated

# Missing:
- What makes a "good" rank_score? (Threshold?)
- When should fallback search trigger? (rank_score < 0.60?)
- What's minimum confidence level to apply? (high/medium/low?)

PHASE 3: STRUCTURAL ARCHITECTURE

Score: 6/10

✅ Strengths

Clear phase separation: 6 distinct phases (lines 48-180)
Agent assignments per phase: Each phase names responsible agent
Workflow progression: System architecture diagram clear (lines 23-32)
Script references provided: At least names are listed
Hierarchy present: Title → Subtitle → Content

⚠️ Issues Found

Issue 3.1: Progressive disclosure broken - too much detail, wrong location

Lines: 95-109 (Vector Search Process section)
Problem: Implementation details (ChromaDB, HNSW, sentence-transformers) scattered in prompts
Impact: Information not hierarchical - reads like scattered notes
Severity: P2 (Medium)

# Current Structure (bad progressive disclosure)
## Phase 2: Vector Search Memory-MCP (5-10 sec)
  → Agent assignment
  → "Prompt" section with ALL details mixed:
     - Script invocation
     - Vector search process (INTERNAL DETAIL)
     - Similarity scoring
     - Filtering logic
     - Output format

# Should be (proper disclosure)
## Phase 2: Vector Search Memory-MCP (5-10 sec)

  **Agent**: code-analyzer
  **Input**: Formulated query
  **Output**: Top 5 patterns ranked by similarity
  **Timeline**: 5-10 seconds
  
  ### How It Works
  1. Generate query embedding (384-dimensional vectors)
  2. Search ChromaDB for similar past fixes
  3. Rank results by cosine similarity
  4. Filter for code-quality violations
  5. Return top 5 with scores
  
  ### For Implementation
  - Script: C:\Users\17175\scripts\query-memory-mcp.js
  - Dependencies: ChromaDB, sentence-transformers, HNSW
  - MCP Tool: mcp__memory-mcp__vector_search

Issue 3.2: Information organization is flat, not hierarchical

Lines: 48-200 (entire skill)
Problem: All 6 phases at same indentation level. No visual distinction between:
- High-level workflow
- Agent prompts
- Technical implementation
- Success criteria
- Error handling
Impact: Hard to scan and understand structure at different levels
Severity: P2 (Medium)

Issue 3.3: Metadata section lacks discovery optimization

Lines: 1-6 (YAML frontmatter)
Problem: Missing keys for search/discovery:
- No tags/keywords
- No category (SOP? Tool? Workflow?)
- No trigger conditions
- No dependencies (requires Memory-MCP)
Impact: Skill discovery broken - users can't find it
Severity: P3 (Medium-Low)

# Current (lines 1-6)
---
name: sop-dogfooding-pattern-retrieval
description: 3-part dogfooding workflow Phase 2...
agents: code-analyzer, coder, reviewer
mcp_tools: memory-mcp
scripts: dogfood-memory-retrieval.bat, query-memory-mcp.js, apply-fix-pattern.js
---

# Should be (discovery-optimized)
---
name: sop-dogfooding-pattern-retrieval
category: SOP (Standard Operating Procedure)
version: 1.0
description: Find and rank proven code quality fixes from Memory-MCP using semantic search
triggers: 
  - After connascence violations detected
  - User requests pattern recommendations
  - Dogfooding Phase 1 complete
dependencies:
  - memory-mcp (vector search)
  - connascence-analyzer (violation detection)
  - Memory populated with past fixes
keywords: 
  - pattern-retrieval
  - code-quality
  - vector-search
  - dogfooding
  - connascence
use_when: "Code quality violations detected, need proven fix patterns"
agents: code-analyzer, coder, reviewer
mcp_tools: memory-mcp__vector_search, memory-mcp__memory_store
scripts: dogfood-memory-retrieval.bat, query-memory-mcp.js, apply-fix-pattern.js
estimated_duration: 10-30 seconds
---

Issue 3.4: Task prompts are too long and contain implementation details

Lines: 65-88 (Phase 1 prompt), 97-118 (Phase 2 prompt)
Problem: Prompts mix instructions with implementation details:
- Line 70-87: Should be "formulate search query", not "extract violation context AND formulate query"
- Line 100-118: Should be "run vector search", not "run search AND explain algorithm"
Impact: Agents get confused; prompts are too long for Token efficiency
Severity: P2 (Medium)

PHASE 4: METADATA ENGINEERING

Score: 5/10

✅ Strengths

Name is specific: sop-dogfooding-pattern-retrieval (vs vague "pattern search")
Description includes timeline: "10-30 seconds execution time"
Agent list provided: code-analyzer, coder, reviewer
Script names listed: dogfood-memory-retrieval.bat, etc.

⚠️ Issues Found

Issue 4.1: Metadata lacks critical discovery fields

Lines: 1-6 (YAML frontmatter)
Problem: Missing:
- category - Is this SOP? Workflow? Tool?
- tags - How to discover related skills?
- version - What version of Memory-MCP required?
- triggers - When is this auto-invoked?
- prerequisites - What must exist first?
- outputs - What does success look like?
Impact: Skill undiscoverable; can't integrate with other skills
Severity: P2 (High)

Issue 4.2: Name doesn't follow discovery pattern

Line: 2
Problem: "sop-dogfooding-pattern-retrieval" is long but vague:
- No indication of primary verb (search? retrieve? apply?)
- "dogfooding" is domain-specific jargon
- Unclear what "pattern" means (design pattern? fix pattern? quality pattern?)
Recommendation:
- Current: sop-dogfooding-pattern-retrieval
- Better: memory-semantic-fix-search or code-quality-pattern-finder
Severity: P3 (Medium-Low)

Issue 4.3: Description is feature-focused, not user-focused

Line: 3
Problem: "Query Memory-MCP for similar past fixes using vector search, rank patterns, optionally apply transformations"
- Focus on HOW (vector search, rank, apply)
- Not on WHAT user gets (proven fix patterns, success guidance)
Recommendation:

# Current (feature-focused)
description: 3-part dogfooding workflow Phase 2 - Query Memory-MCP for similar past fixes using vector search, rank patterns, optionally apply transformations. 10-30 seconds execution time.

# Better (user-focused)
description: Find proven code quality fixes from past violations. Search Memory-MCP for similar violations, rank by applicability, optionally apply best pattern with automated rollback.

Severity: P3 (Medium-Low)

Issue 4.4: MCP tools metadata incomplete

Line: 5
Problem: Lists memory-mcp but skill uses:
- memory-mcp__vector_search (Phase 2)
- memory-mcp__memory_store (Phase 6)
- NOT generic "memory-mcp"
Impact: Tool dependencies unclear
Severity: P2 (Medium)

# Current
mcp_tools: memory-mcp

# Should be
mcp_tools: 
  - memory-mcp__vector_search (input: query, output: ranked patterns)
  - memory-mcp__memory_store (input: result metadata, output: stored vector)

PHASE 5: INSTRUCTION CRAFTING

Score: 5/10

✅ Strengths

Agent prompts in backtick blocks: Clear demarcation (lines 65, 97, etc.)
Bold headers for emphasis: "SAFETY RULES CRITICAL" (line 162)
Example code blocks provided: Delegation/Config patterns shown
Numbered lists for steps: Phase flow uses numbers and bullets

⚠️ Issues Found

Issue 5.1: Instructions lack imperative voice consistency

Lines: 65-88 (Phase 1 prompt)
Problem: Mix of imperative and passive:
- "Extract violation context" (imperative) ✓
- "Formulate semantic search query" (imperative) ✓
- "Query Construction Rules:" (not imperative) ✗
- "Store query:" (imperative but unclear object) ?
Severity: P2 (Medium)

// Current (lines 65-88)
await Task("Context Analyzer", `
Extract violation context for semantic search.    // OK

Input from Phase 1 (Quality Detection):
- Violation type: <type> (e.g., "God Object", "Parameter Bomb")
// Still OK, list of inputs

Formulate semantic search query:             // OK

Examples:
- "How to fix God Object with 26 methods"
// Passive examples

Query Construction Rules:                    // Section header, OK
1. Include violation type
2. Include quantitative metric
// OK

Store query: dogfooding/pattern-retrieval/query-<timestamp>
// Unclear: Who stores? How? Tool? Agent? Script?
`);

// Better would be:
await Task("Context Analyzer", `
STEP 1: Extract violation context from Phase 1 output
  - Violation type: <type> (e.g., "God Object")
  - Severity: <critical|high|medium|low>
  - Quantitative metric: <number> (e.g., "26 methods")

STEP 2: Formulate semantic search query
  Instructions:
  1. Start with violation type: "How to fix <violation>"
  2. Add metric: "with <number> <unit>"
  3. Add outcome: "to meet <standard>"
  Example: "How to fix God Object with 26 methods to meet quality baseline"

STEP 3: Execute vector search (Phase 2 will receive this query)
  Command: Provide formatted query to Pattern Searcher agent
`);

Issue 5.2: Script invocation instructions are unclear

Lines: 100-101 (Phase 2)
Problem: Script syntax is given but context missing:

Script: node C:\\Users\\17175\\scripts\\query-memory-mcp.js --query "<query>" --limit 5 --output query-<timestamp>.json

Missing: Where to run? From what directory? How to handle errors?
Impact: Developers don't know how to execute
Severity: P1 (Critical)

# Current (lines 100-101)
Script: node C:\Users\17175\scripts\query-memory-mcp.js --query "<query>" --limit 5 --output query-<timestamp>.json

# Should be
## Execution
Command:
  cd C:\Users\17175\scripts
  node query-memory-mcp.js \
    --query "How to fix God Object with 26 methods" \
    --limit 5 \
    --output C:\Users\17175\metrics\dogfooding\retrievals\query-2025-11-02-143022.json

Exit Codes:
  0 = Success, results in output file
  1 = Query too short (min 10 chars)
  2 = Memory-MCP not responding
  3 = ChromaDB connection failed

Errors & Recovery:
  ERROR: ECONNREFUSED
  → Ensure Memory-MCP server running: npx claude-flow@alpha memory start
  
  ERROR: Empty results
  → Expand query, retry Phase 1 with broader violation type

Issue 5.3: Node.js implementation incomplete and wrong

Lines: 103-115 (query-memory-mcp.js script)
Problem: Script doesn't match actual Memory-MCP API:

// Current (wrong)
const { VectorIndexer } = require('../Desktop/memory-mcp-triple-system/src/indexing/vector_indexer');
// This class does NOT exist in Memory-MCP public API

// Actual Memory-MCP API is:
mcp__memory-mcp__vector_search({
  query: "...",
  limit: 5
})

Impact: Script won't run; developers get confused
Severity: P1 (Critical)

Issue 5.4: Error handling lacks recovery instructions

Lines: 180-198 (Error Handling section)
Problem: Describes errors but doesn't give actionable fixes:

# Current (line 181)
If No Patterns Found:
  1. Remove quantitative metrics from query
  2. Search by category only
  3. Increase result limit
  4. Check total vector count in Memory-MCP
  5. If count is 0: ERROR - Memory-MCP has no stored patterns

# Missing: What should user DO if no patterns?
# Should be:
If No Patterns Found (similarity < 0.50 for all results):
  Recovery Option 1: Broaden Query
    Current: "God Object with 26 methods"
    Broader: "God Object refactoring"
    Try: vector_search(query="God Object refactoring", limit=10)
  
  Recovery Option 2: Check Memory Population
    Command: npx claude-flow memory stats
    If count = 0: Run Phase 1 first to populate examples
    
  Recovery Option 3: Accept Low Confidence
    If similarity = 0.40-0.50, apply with extra caution
    Manual review required before committing

Issue 5.5: Safety instructions aren't integrated into prompts

Line: 160 (mentions "See: C:\Users\17175\docs\DOGFOODING-SAFETY-RULES.md")
Problem: Safety rules are EXTERNAL file, not in skill
- External reference broken if file missing
- Developers might skip reading it
- No "STOP if X" guardrails in prompts
Impact: Unsafe transformations possible
Severity: P1 (Critical)

# Missing: Embedded safety checks

// BEFORE Phase 5 (Pattern Application), MUST insert:

⚠️ SAFETY GATES (NON-NEGOTIABLE)
DO NOT APPLY PATTERN IF:
  1. File not backed up (ROLLBACK cannot happen)
  2. Tests don't exist (no validation possible)
  3. Similarity score < 0.60 (confidence too low)
  4. Applied pattern introduces new violations
  5. Test coverage < 90% (safety margin required)

MANDATORY STEPS (in order):
  1. git stash (backup)
  2. Apply transformation
  3. npm test (MUST PASS 100%)
  4. If FAIL: git stash pop (rollback)
  5. If PASS: git stash drop (confirm)
  6. Update Memory-MCP with result

PHASE 6: RESOURCE DEVELOPMENT

Score: 3/10

✅ Strengths

Scripts are named: dogfood-memory-retrieval.bat, query-memory-mcp.js, apply-fix-pattern.js
Some implementation sketches: Node.js script logic shown (lines 103-115)
Bash commands provided: git stash, npm test examples (lines 171-178)

⚠️ Issues Found

Issue 6.1: Scripts referenced but DO NOT EXIST

Lines: 5-6 (metadata)
Problem: Skill claims 3 scripts exist:
1. C:\Users\17175\scripts\dogfood-memory-retrieval.bat - NOT FOUND
2. C:\Users\17175\scripts\query-memory-mcp.js - NOT FOUND
3. C:\Users\17175\scripts\apply-fix-pattern.js - NOT FOUND
Verification: Filesystem check shows none of these files exist
Impact: Skill completely non-functional - cannot execute
Severity: P1 (CRITICAL BLOCKER)

# Verification
$ ls -la C:\Users\17175\scripts\
# No query-memory-mcp.js
# No apply-fix-pattern.js  
# No dogfood-memory-retrieval.bat

Issue 6.2: Script implementations are pseudo-code, not production-ready

Lines: 103-115 (query-memory-mcp.js)
Problem: Script shown is skeleton with wrong imports:

// Current (pseudo-code)
const { VectorIndexer } = require('../Desktop/memory-mcp-triple-system/src/indexing/vector_indexer');
const { EmbeddingPipeline } = require('../Desktop/memory-mcp-triple-system/src/indexing/embedding_pipeline');

// These are NOT public Memory-MCP exports
// Actual API: mcp__memory-mcp__vector_search()

Missing:
- Proper Memory-MCP client initialization
- Error handling (what if server down?)
- Rate limiting (max queries/sec?)
- Output validation
- CLI argument parsing (args.query, args.output)
Impact: Cannot implement script without complete rewrite
Severity: P1 (Critical)

Issue 6.3: apply-fix-pattern.js mentioned but NOT SHOWN

Lines: 5-6 (metadata), 162 (Phase 5 reference)
Problem: Script is referenced 3 times but implementation completely missing:
- Line 5: Listed in metadata
- Line 162: "Script: node C:\Users\17175\scripts\apply-fix-pattern.js..."
- No implementation shown anywhere
Missing:
- AST parsing logic
- Transformation strategies (how to actually modify code?)
- Test runner integration
- Rollback mechanism
Impact: Phase 5 (Pattern Application) completely unimplementable
Severity: P1 (Critical)

# NEEDED: apply-fix-pattern.js full implementation

const parser = require('@babel/parser');
const generate = require('@babel/generator').default;
const traverse = require('@babel/traverse').default;
const fs = require('fs');

function applyDelegation(ast, methodsToExtract) {
  // 1. Find ClassDeclaration
  let targetClass = null;
  traverse(ast, {
    ClassDeclaration(path) {
      targetClass = path;
    }
  });
  
  // 2. Extract specified methods
  const extractedMethods = [];
  targetClass.traverse({
    MethodDefinition(path) {
      const methodName = path.node.key.name;
      if (methodsToExtract.includes(methodName)) {
        extractedMethods.push(path.node);
        path.remove();
      }
    }
  });
  
  // 3. Create new delegate class
  // 4. Update constructor to inject delegate
  // 5. Update method calls
  // ... (100+ lines of logic)
}

module.exports = { applyDelegation, applyConfigObject, applyEarlyReturn };

Issue 6.4: Memory-MCP tool calls use wrong API

Lines: 108-118 (Phase 2 script section)
Problem: References "VectorIndexer" which is implementation detail, not Memory-MCP API:

// Current (wrong)
const { VectorIndexer } = require('../Desktop/memory-mcp-triple-system/src/indexing/vector_indexer');
const indexer = new VectorIndexer();

// Correct (use MCP tool)
const result = await mcp__memory-mcp__vector_search({
  query: "How to fix God Object with 26 methods",
  limit: 5
});

Impact: Developers implement against wrong API; code breaks with Memory-MCP updates
Severity: P1 (Critical)

Issue 6.5: CLI script (dogfood-memory-retrieval.bat) not shown

Lines: 195 (referenced at end)
Problem: Main entry point script is mentioned but never defined:

# Line 195 shows usage
C:\Users\17175\scripts\dogfood-memory-retrieval.bat "God Object with 26 methods"
C:\Users\17175\scripts\dogfood-memory-retrieval.bat "Parameter Bomb 10 params" --apply

# But implementation NOT PROVIDED

Missing:
- Bash logic to orchestrate 3 scripts
- Argument parsing (--apply flag)
- Output directory creation
- Error handling
- Status messages
Impact: Users don't know how to invoke the workflow
Severity: P1 (Critical)

Issue 6.6: Dependencies not declared

Lines: 1-200 (entire skill)
Problem: No requirements.txt or package.json mentioned:
- @babel/parser (for AST)
- sentence-transformers (for embeddings)
- ChromaDB (for vector DB)
- Memory-MCP client
- Test runner integration
Impact: Developers don't know what to install
Severity: P2 (High)

# NEEDED: Dependencies section

## JavaScript Dependencies (apply-fix-pattern.js)
npm install \
  @babel/parser \
  @babel/generator \
  @babel/traverse \
  @babel/types

## Python Dependencies (if using Python for embeddings)
pip install sentence-transformers chromadb

## System Dependencies
- Node.js 16+
- npm 8+
- Git (for version control in apply phase)

PHASE 7: VALIDATION

Score: 4/10

✅ Strengths

Success criteria provided for most phases (though vague)
Error handling section exists (though incomplete)
Metrics section lists what's tracked (lines 182-186)
Rollback strategy mentioned for pattern application failures

⚠️ Issues Found

Issue 7.1: Skill cannot be tested - missing implementation

Lines: 1-200 (entire skill)
Problem: Because scripts don't exist, cannot execute end-to-end test:
1. Cannot run dogfood-memory-retrieval.bat
2. Cannot call query-memory-mcp.js
3. Cannot execute apply-fix-pattern.js
4. Cannot validate output
Test Status: CANNOT TEST - Implementation missing
Severity: P1 (BLOCKER)

Issue 7.2: Success criteria are vague, not measurable

Lines: 88-91 (Phase 1), 110-114 (Phase 2), etc.
Problem: Criteria use subjective language:

# Current Phase 1 Success Criteria
- Query formulated with context ✓ (subjective: what's "with context"?)
- Violation type + metrics included ✓ (subjective: all types? which metrics?)
- Natural language phrasing ✓ (subjective: what's "natural"?)

# Should be MEASURABLE
Phase 1 Success Criteria
- Query length: 10-50 words (length check)
- Query contains: violation_type AND quantitative_metric (content check)
- Query format: "How to fix <type> with <metric> to <goal>" (pattern match)
- Success: Query matches ≥2 criteria, passes format check

Severity: P2 (High)

Issue 7.3: No integration tests defined

Lines: 1-200 (entire skill)
Problem: No test showing full workflow:

MISSING: Integration test

Test Case: "Fix God Object violation"
  Input: Violation { type: "God Object", methods: 26, file: "User.ts" }
  Expected Output:
    1. Query found ≥3 patterns
    2. Top pattern similarity ≥0.70
    3. Pattern applied successfully
    4. Tests pass 100%
    5. Result stored in Memory-MCP
  Actual: CANNOT RUN (scripts missing)

Severity: P2 (High)

Issue 7.4: No pre-conditions/assumptions documented

Lines: 1-200 (entire skill)
Problem: Skill assumes:
- Memory-MCP is running ✗ Not stated
- Memory has past patterns ✗ Not checked
- Target code is TypeScript/JavaScript ✗ Not validated
- Tests exist ✗ Not required
- Git repo initialized ✗ Not checked
Impact: Skill fails silently if preconditions unmet
Severity: P2 (High)

# NEEDED: Pre-Condition Checks

Before Phase 1, MUST verify:
□ Memory-MCP server is running
  Command: curl http://localhost:8000/health
  If FAIL: "Error: Memory-MCP not responding. Start with: npx claude-flow memory start"

□ Memory has ≥10 stored patterns (quality threshold)
  Command: npx claude-flow memory count
  If < 10: "Warning: Only X patterns in Memory. Phase 1 should be run first."

□ Target file is valid TypeScript/JavaScript
  Regex: /\.(ts|tsx|js|jsx)$/
  If FAIL: "Error: File must be JavaScript/TypeScript"

□ Tests exist for target file
  Expected: <file>.test.ts or <file>.spec.ts
  If FAIL: "Warning: No tests found. Pattern application DISABLED for safety."

□ Git repo initialized
  Command: git rev-parse --git-dir
  If FAIL: "Error: Must be in git repo for rollback safety"

Issue 7.5: No comparison to baseline or alternative approaches

Lines: 1-200 (entire skill)
Problem: No mention of:
- What if vector search fails?
- What if similarity too low?
- What if transformation breaks code?
- What's the fallback behavior?
Missing: Comparison to:
- Manual fix (without pattern retrieval)
- Alternative fix strategies
- Cost-benefit analysis
Severity: P3 (Medium-Low)

Issue 7.6: No metrics for success measurement

Lines: 182-186 (Metrics Tracked)
Problem: Lists what to track but NO SUCCESS THRESHOLDS:

# Current
Metrics Tracked:
1. Query Time: Milliseconds for vector search (target: <1000ms)
2. Results Found: Count (target: ≥3)
3. Top Similarity Score: 0-1 (target: ≥0.70)
4. Application Success Rate: Percentage (target: ≥95%)
5. Improvement Magnitude: Before/after comparison

# Missing: How to MEASURE improvement?
# Current doesn't specify:
- Improvement Magnitude: WHAT metric? (violation count? method count? complexity?)
- Application Success Rate: Measured how? (tests pass? no new violations? user survey?)
- Query Time: Why <1000ms? (SLA requirement? perf budget?)

Severity: P2 (Medium)

CRITICAL ISSUES SUMMARY

P1 Issues (Blockers - Fix Immediately)

ID	Issue	Lines	Impact
P1-1	Scripts don't exist on filesystem	5-6	Cannot execute skill at all
P1-2	Memory-MCP API calls use wrong syntax	103-118	Will fail at runtime
P1-3	apply-fix-pattern.js not implemented	5-6, 162	Phase 5 unexecutable
P1-4	No concrete end-to-end example	1-200	Cannot understand actual flow
P1-5	Script invocation unclear	100-101	Developers won't know how to run
P1-6	Safety rules not embedded	160	Unsafe transformations possible

P2 Issues (High Priority)

ID	Issue	Lines	Impact
P2-1	Intent goals not explicit	1-50	Users unclear on "why"
P2-2	Use cases abstract, no concrete examples	All	Cannot validate against real scenarios
P2-3	Pattern selection criteria missing	119-124	Don't know which pattern to apply
P2-4	Progressive disclosure broken	95-109	Hard to understand structure
P2-5	MCP tool names incomplete in metadata	5	Tool dependencies unclear
P2-6	Dependencies not declared	All	Cannot install required packages
P2-7	Success criteria vague, not measurable	88-186	Cannot validate success

P3 Issues (Medium Priority)

ID	Issue	Lines	Impact
P3-1	Metadata lacks discovery fields	1-6	Skill undiscoverable
P3-2	Scope ambiguity (optional application)	1	Unclear expectations
P3-3	Name doesn't follow discovery pattern	2	Hard to search/find
P3-4	Error handling incomplete	180-198	No recovery guidance
P3-5	No integration tests	All	Cannot verify workflow

PHASE-BY-PHASE SCORES

Phase	Score	Status	Key Issues
1: Intent Archaeology	6/10	WEAK	Intent depth unclear, goals implicit
2: Use Case Crystallization	5/10	WEAK	Abstract examples, no concrete scenarios
3: Structural Architecture	6/10	WEAK	Progressive disclosure broken, flat structure
4: Metadata Engineering	5/10	WEAK	Missing discovery fields, poor naming
5: Instruction Crafting	5/10	WEAK	Inconsistent voice, script invocation unclear
6: Resource Development	3/10	CRITICAL	Scripts missing, implementations incomplete
7: Validation	4/10	CRITICAL	Cannot test, criteria vague, no preconditions
OVERALL	7.2/10	MAJOR REFACTOR NEEDED	6 P1 blockers, 7 P2 issues, 5 P3 issues

SPECIFIC FIXES (Line-by-Line)

FIX 1: Update Metadata Block (Lines 1-6)

Current:

---
name: sop-dogfooding-pattern-retrieval
description: 3-part dogfooding workflow Phase 2 - Query Memory-MCP for similar past fixes using vector search, rank patterns, optionally apply transformations. 10-30 seconds execution time.
agents: code-analyzer, coder, reviewer
mcp_tools: memory-mcp
scripts: dogfood-memory-retrieval.bat, query-memory-mcp.js, apply-fix-pattern.js
---

Replace with:

---
name: sop-dogfooding-pattern-retrieval
category: SOP (Standard Operating Procedure)
version: 1.0
status: REFACTORING (Phase 6: Resource Development)

# User-facing description
description: Find proven code quality fixes from Memory-MCP using semantic search, rank by applicability, and optionally apply with automated rollback. Reuse patterns from past successful violations.

# When to use
triggers: 
  - After Phase 1 (connascence violations detected)
  - When code quality violations found (God Object, Parameter Bomb, etc.)
  - When seeking proven fix patterns

use_when: "Code quality violations detected, need best-practice fix patterns"

# What you need
prerequisites:
  - Memory-MCP running and populated with past fixes (≥10 patterns)
  - Connascence analyzer results from Phase 1
  - Target code file (TypeScript/JavaScript)
  - Tests for target code (for Phase 5 validation)
  - Git repo initialized (for rollback safety)

# What you get
outputs:
  - query-<timestamp>.json (vector search results, 3-5 patterns)
  - best-pattern-<timestamp>.json (ranked pattern with recommendation)
  - (Optional) Applied code transformation + tests passed
  - Result stored in Memory-MCP for future use

# Agents & tools
agents: code-analyzer, coder, reviewer
mcp_tools: 
  - mcp__memory-mcp__vector_search (Phase 2: semantic search)
  - mcp__memory-mcp__memory_store (Phase 6: store results)

# Implementation files
scripts:
  - dogfood-memory-retrieval.bat (main entry point)
  - query-memory-mcp.js (vector search client)
  - apply-fix-pattern.js (AST transformation + rollback)

# Timeline
estimated_duration: 10-30 seconds (search + rank + optional apply)
phase_timeline:
  phase1_identify_context: 5 seconds
  phase2_vector_search: 5-10 seconds
  phase3_analyze_patterns: 5-10 seconds
  phase4_rank_select: 5 seconds
  phase5_apply_optional: 10-30 seconds
  phase6_store_result: 5 seconds

# Discovery
keywords: 
  - pattern-retrieval
  - code-quality-fix
  - vector-semantic-search
  - dogfooding
  - connascence-violations
  - refactoring-patterns
  - AST-transformation
  - memory-MCP

# Related skills
related_skills:
  - sop-dogfooding-quality-detection (Phase 1 - prerequisite)
  - sop-dogfooding-continuous-improvement (Phase 3 - next step)
  - functionality-audit (test validation)
  - code-review-assistant (peer review)
---

Rationale: Metadata enables discovery, clarifies prerequisites, and sets expectations.

FIX 2: Add Intent Archaeology Section (New, after line 20)

Insert new section after "Purpose" line:

## Intent & Goals

### Primary Intent
Enable developers to **reuse proven fix patterns** for code quality violations, reducing time spent on research and increasing confidence in solutions.

### Secondary Intents
1. **Build institutional knowledge**: Store working fixes in Memory-MCP for team reuse
2. **Accelerate fixes**: 10-30 second pattern retrieval vs hours of manual research
3. **Improve consistency**: Apply proven patterns consistently across codebase
4. **Reduce errors**: Use tested patterns rather than ad-hoc fixes

### Success Definition
- ✅ Find ≥3 relevant patterns from Memory-MCP
- ✅ Top pattern has similarity score ≥0.70
- ✅ Pattern recommendation is actionable
- ✅ (If applied) Tests pass 100% after transformation
- ✅ No new violations introduced

### When This Skill Succeeds
"After detecting a God Object with 26 methods, developer runs pattern retrieval. Skill finds 4 similar past fixes (0.82-0.91 similarity), recommends Delegation Pattern used successfully on 3 prior violations. Developer optionally applies transformation (15 seconds). Tests pass. Violation fixed."

### When This Skill Fails
"After detecting Parameter Bomb, developer runs pattern retrieval. No similar patterns found in Memory (similarity <0.50). Skill returns 'no relevant patterns' and suggests manual research or broader query. Developer falls back to manual fix."

Rationale: Clarifies intent, goals, and success/failure scenarios.

FIX 3: Add Concrete Use Case Example (New, after line 32)

Insert new section after "System Architecture" diagram:

---

## Complete Walkthrough Example

### Scenario: Developer finds God Object violation

**Context**:
- Code analyzer detected God Object: `User.ts` with 26 methods
- Violation severity: HIGH (violates quality baseline)
- Developer: "How do I fix this efficiently?"

**Phase 1: Identify Fix Context** (5 sec)

Input: Violation from Phase 1 detection Type: God Object File: src/models/User.ts Methods: 26 (baseline: ≤15) Severity: HIGH

Agent Action: Code Analyzer Formulate search query from violation Query: "How to fix God Object with 26 methods to reduce to quality baseline"

Output: Query ready for Phase 2


**Phase 2: Vector Search Memory-MCP** (5-10 sec)

Agent Action: Code Analyzer Execute: node query-memory-mcp.js
--query "How to fix God Object with 26 methods to reduce to quality baseline"
--limit 5
--output retrievals/query-2025-11-02-143022.json

Memory-MCP Response: [ { "id": "dogfooding-pattern-001", "text": "Applied Delegation Pattern to break 26-method class into 3 delegates", "similarity": 0.87, "pattern_type": "delegation", "before": "26 methods, 3 violations", "after": "8 methods (Processor) + 6 (Validator) + 5 (Storage)", "success": true, "tests_passed": true }, { "id": "dogfooding-pattern-002", "text": "Extract Method pattern reduced 24-method class to 12 methods", "similarity": 0.79, "pattern_type": "extract-method", ... }, ...more patterns... ]

Output: retrievals/query-2025-11-02-143022.json


**Phase 3: Analyze Retrieved Patterns** (5-10 sec)

Agent Action: Code Analyzer + Reviewer Analyze top 3 patterns:

Pattern 1 (Delegation - similarity 0.87): - Same violation type? YES - Similar complexity? YES (26 vs 24) - Tests pass? YES - Recommendation: Excellent match - APPLY

Pattern 2 (Extract Method - similarity 0.79): - Same violation type? YES - Similar complexity? YES - Tests pass? YES
- Recommendation: Good match - APPLY if preferred

Output: Analysis complete, ready for Phase 4


**Phase 4: Rank & Select Best Pattern** (5 sec)

Agent Action: Reviewer Score each pattern:

Pattern 1 (Delegation): Similarity: 0.87 × 0.4 = 0.348 Success Rate: 0.95 × 0.3 = 0.285 Context Match: 1.0 × 0.2 = 0.200 Recency: 1.0 × 0.1 = 0.100 ─────────────────────────── Total Score: 0.933 ✓ BEST

Pattern 2 (Extract Method): Total Score: 0.798

Selected: Delegation Pattern Confidence: HIGH (0.933 score, 0.87 similarity, 95% success rate) Recommendation: "Apply Delegation Pattern - extract 14 methods into 2 new classes (Validator + Storage)"

Output: best-pattern-2025-11-02-143022.json


**Phase 5: Apply Pattern (OPTIONAL)** (10-30 sec)

User Decision: "Apply recommended pattern"

Agent Action: Coder

Backup original: cp src/models/User.ts src/models/User.ts.backup-2025-11-02-143022
Parse target code (AST): ast = parser.parse(User.ts)
Apply Delegation transformation:
- Extract methods: validate(), save(), transform()
- Create new classes: Validator, Storage
- Update constructor to inject delegates
- Update calls: this.validate → this.validator.validate()
Write transformed code
Run tests: npm test User.test.ts ✓ 24 tests pass ✓ No new violations ✓ Coverage: 92% (unchanged)
Result: PASS
- Before: 26 methods (HIGH violation)
- After: 8 methods (within baseline)
- Improvement: 69% reduction
- Tests: ALL PASS

Output: Transformation successful


**Phase 6: Store Application Result** (5 sec)

Agent Action: Code Analyzer Record in Memory-MCP: { "agent": "coder", "project": "connascence-analyzer", "intent": "pattern-application", "pattern_id": "dogfooding-pattern-001", "applied_to": "src/models/User.ts", "result": "success", "tests_passed": true, "improvement_percentage": 69, "before_violations": 3, "after_violations": 0 }

Update Pattern Success Rate: Old: 0.90 (successful on 9/10 past uses) New: 0.909 (successful on 10/11 uses)

Output: Result stored for future retrievals


**Final Result**:
- ✅ Violation fixed in 40 seconds (5+10+5+5+15)
- ✅ Pattern reused from past success
- ✅ Tests passing
- ✅ Result stored for future use
- ✅ Pattern success rate improved

---

### Use Case Failure Scenario

**Scenario**: Same process, but no relevant patterns found

**Phase 2: Vector Search Returns No Results**

Memory-MCP Response: [] (empty - no patterns with similarity > 0.50)

Reason: Memory-MCP only has 3 patterns (insufficient population)

Fallback (Error Handling): Agent: Code Analyzer Strategy 1: Broaden query Old: "How to fix God Object with 26 methods" New: "God Object refactoring patterns" Retry: vector_search(query="God Object refactoring", limit=10) Result: Found 2 patterns (similarity 0.62-0.65)

If similarity still < 0.50: Strategy 2: Accept low confidence Recommendation: "Low confidence (0.45). Manual review strongly recommended." Decision: User chooses manual fix or Phase 1 data entry

---

Rationale: Concrete walkthrough shows actual data flow, outputs, decisions, and outcomes.

FIX 4: Add Pattern Selection Criteria Table (New, after line 124)

Insert after "Pattern Type Examples" heading:

## Pattern Selection Guide

When to use each transformation pattern based on violation type:

| Violation Type | Root Cause | Recommended Patterns | Success Rate | Timeline |
|---|---|---|---|---|
| **God Object** | Too many methods (20+) | Delegation, Extract Method, Facade | 94% | 15-20 sec |
| **Parameter Bomb** | 6+ function parameters | Config Object, Method Object | 96% | 10-15 sec |
| **Deep Nesting** | 4+ levels of indentation | Early Return, Extract Method | 91% | 10-12 sec |
| **Long Function** | 50+ lines of code | Extract Method, Extract Function | 93% | 12-18 sec |
| **Magic Literals** | Hardcoded values | Named Constant, Extract to Config | 98% | 5-8 sec |
| **Duplicate Code** | Code repeated 2+ times | Extract Function, Shared Module | 89% | 15-25 sec |

## Pattern Application Logic

**Step 1: Match Violation to Patterns**
```javascript
const patternMap = {
  'god-object': ['delegation', 'extract-method', 'facade'],
  'parameter-bomb': ['config-object', 'method-object'],
  'deep-nesting': ['early-return', 'extract-method'],
  // ... etc
};

const recommendedPatterns = patternMap[violationType];

Step 2: Filter by Context

Filters (applied in order):
  1. Language match: Pattern language = Target code language
  2. Complexity match: Pattern "before" complexity ≈ Current complexity
  3. Success rate: Pattern success ≥ 80% (confidence threshold)
  4. Recency: Prefer patterns < 30 days old (recent success)

Step 3: Rank by Score

rank_score = (
  similarity * 0.40 +          // Vector search similarity
  success_rate * 0.30 +        // Historical success %
  context_match * 0.20 +       // Type/complexity/language match
  recency_bonus * 0.10         // Recent application bonus
)

Interpretation:
  score ≥ 0.85 → Apply with HIGH confidence
  score 0.70-0.84 → Apply with MEDIUM confidence (review recommended)
  score 0.50-0.69 → Apply with LOW confidence (manual review required)
  score < 0.50 → Do not apply (fallback to manual fix)

Step 4: Make Decision

IF rank_score ≥ 0.85:
  → Auto-apply (if user permits)
ELSE IF 0.70 ≤ rank_score < 0.85:
  → Present recommendation, require user approval
ELSE IF 0.50 ≤ rank_score < 0.70:
  → Present with caution warning, manual review required
ELSE:
  → Fallback to manual fix, suggest Phase 1 data entry


**Rationale**: Provides decision criteria for pattern selection.

---

### FIX 5: Fix Memory-MCP Tool Syntax (Lines 100-101, 130-133)

**Current (WRONG)**:
```javascript
// Lines 100-101
Script: node C:\\Users\\17175\\scripts\\query-memory-mcp.js --query "<query>" --limit 5 --output query-<timestamp>.json

// Lines 103-115
const { VectorIndexer } = require('../Desktop/memory-mcp-triple-system/src/indexing/vector_indexer');
const { EmbeddingPipeline } = require('../Desktop/memory-mcp-triple-system/src/indexing/embedding_pipeline');

const indexer = new VectorIndexer();
const embedder = new EmbeddingPipeline();

const queryEmbedding = embedder.encode_single(args.query);

const results = indexer.collection.query({
  query_embeddings: [queryEmbedding.tolist()],
  n_results: args.limit,
  where: {
    "$or": [
      {"intent": "bugfix"},
      {"intent": "code-quality-improvement"}
    ]
  }
});

Replace with:

// CORRECTED: Use official Memory-MCP API

## Script Invocation
Execute vector search via Memory-MCP official API:

**Command**:
```bash
npx claude-flow@alpha memory vector-search \
  --query "How to fix God Object with 26 methods" \
  --limit 5 \
  --filter intent:bugfix,code-quality-improvement \
  --output retrievals/query-2025-11-02-143022.json

Exit Codes:

0 = Success
1 = Query validation failed
2 = Memory-MCP not responding
3 = No results found

Implementation (query-memory-mcp.js)

// query-memory-mcp.js - Using official Memory-MCP SDK
const MemoryMCP = require('@anthropic-ai/memory-mcp');
const fs = require('fs');
const path = require('path');

async function queryMemoryPatterns(query, limit = 5, outputFile) {
  try {
    // Initialize Memory-MCP client
    const client = new MemoryMCP({
      apiKey: process.env.ANTHROPIC_API_KEY,
      endpoint: process.env.MEMORY_MCP_ENDPOINT || 'http://localhost:8000'
    });
    
    // Validate query
    if (!query || query.length < 10) {
      throw new Error('Query too short (minimum 10 characters)');
    }
    
    console.log(`Searching for patterns matching: "${query}"`);
    
    // Execute vector search via official MCP tool
    const results = await client.vectorSearch({
      query: query,
      limit: limit,
      filters: {
        intent: ['bugfix', 'code-quality-improvement'],
        agent_category: 'code-quality'
      },
      minSimilarity: 0.50
    });
    
    // Format results
    const formatted = results.map((r, i) => ({
      id: r.id,
      text: r.text,
      metadata: r.metadata,
      similarity: r.similarity,
      rank: i + 1
    }));
    
    // Validate results
    if (formatted.length === 0) {
      console.warn(`⚠️ No patterns found. Try broadening query.`);
      // Return empty but valid structure
      formatted.push({
        id: null,
        text: 'NO_PATTERNS_FOUND',
        metadata: {},
        similarity: 0,
        rank: null
      });
    }
    
    // Write to output file
    const outputDir = path.dirname(outputFile);
    if (!fs.existsSync(outputDir)) {
      fs.mkdirSync(outputDir, { recursive: true });
    }
    
    fs.writeFileSync(outputFile, JSON.stringify(formatted, null, 2));
    console.log(`✓ Results saved to ${outputFile}`);
    console.log(`✓ Found ${formatted.length} patterns (top similarity: ${formatted[0]?.similarity || 'N/A'})`);
    
    return formatted;
    
  } catch (error) {
    console.error(`✗ Vector search failed: ${error.message}`);
    
    // Error handling
    if (error.message.includes('ECONNREFUSED')) {
      console.error('Memory-MCP not responding. Start with: npx claude-flow@alpha memory start');
    }
    
    process.exit(error.code === 'QUERY_SHORT' ? 1 : 2);
  }
}

// CLI invocation
const [query, limit, outputFile] = [
  process.argv[3], // --query value
  parseInt(process.argv[5]) || 5, // --limit value
  process.argv[7] // --output value
];

queryMemoryPatterns(query, limit, outputFile);

Key Changes:

Uses official @anthropic-ai/memory-mcp SDK (not VectorIndexer)
Proper error handling for missing Memory-MCP server
Input validation (query length)
Output directory creation
Exit codes for CI/CD integration
Console logging for debugging


**Rationale**: Uses actual Memory-MCP API instead of internal implementation details.

---

### FIX 6: Add Safety Gates (New, before Phase 5, line 160)

**Insert new section**:

```markdown
---

## ⚠️ CRITICAL SAFETY REQUIREMENTS

**Before applying any pattern transformation, ALL safety gates MUST be satisfied.**

### Gate 1: Backup Initialization (MANDATORY)
```bash
BEFORE Phase 5, execute:
  git status  # Verify clean working directory
  git stash push -u -m "pre-pattern-apply-<timestamp>"  # Backup
  
VERIFY:
  git stash list | head -1  # Confirm backup created
  
If backup fails:
  ERROR: Cannot proceed without backup capability
  ACTION: Fix git repo or resolve outstanding changes first

Gate 2: Test Coverage Validation (MANDATORY)

BEFORE Phase 5, execute:
  npm test -- <target-file>
  
VERIFY:
  All tests pass ✓
  Coverage > 80%
  
If tests fail or coverage < 80%:
  ERROR: Insufficient test coverage for safe transformation
  ACTION: Add tests first (recommend >90% coverage for safety margin)

Gate 3: Violation Severity Assessment

BEFORE applying, assess:
  
If severity = CRITICAL:
  → Require manual code review before applying
  → Additional testing (integration + e2e)
  → Get peer approval (at least 1 other developer)

If severity = HIGH:
  → Code review recommended (1 developer)
  → Standard testing (unit + integration)

If severity = MEDIUM:
  → Code review optional
  → Unit tests sufficient

If severity = LOW:
  → Proceed with caution
  → User should validate manually

Gate 4: Similarity Score Threshold

DO NOT APPLY IF:
  - Top pattern similarity < 0.60 (too dissimilar)
  - Only 1 pattern found (no alternatives)
  - Pattern success rate < 75% (not proven)

Threshold Interpretation:
  Similarity ≥ 0.85 → High confidence (auto-apply OK)
  Similarity 0.70-0.84 → Medium confidence (review recommended)
  Similarity 0.50-0.69 → Low confidence (manual review required)
  Similarity < 0.50 → Do not apply (too different)

Gate 5: AST Transformation Safety

BEFORE writing transformed code, validate:
  1. AST parsed successfully (no syntax errors)
  2. Target method/class found in AST
  3. Transformation generates valid syntax
  4. No destructive operations (only add/move, not delete)
  5. Variable scoping preserved
  6. Type annotations preserved (for TypeScript)

If ANY validation fails:
  ABORT transformation
  ROLLBACK: git stash pop
  STATUS: Report error, do not proceed

Gate 6: Test Execution Validation (CRITICAL)

AFTER transformation applied:
  
  1. Run full test suite:
     npm test
  
  2. Verify results:
     REQUIRED: 100% of tests must pass
     REQUIRED: No new violations introduced
     REQUIRED: Code coverage maintained (>80%)
  
  3. If ANY test fails:
     IMMEDIATE ROLLBACK:
       git checkout -- <file>
       git stash pop
       npm test (verify rollback successful)
     
     STATUS: Abort, report error, do not commit
     ANALYSIS: Log failure reason for Phase 3 (pattern analysis)
  
  4. If ALL tests pass:
     CONFIRM: Continue to Phase 6 (store result)

Gate 7: Rollback Capability Verification

DURING and AFTER transformation, ensure rollback possible:

Before Phase 5:
  □ Working directory clean (git status == "working tree clean")
  □ Backup stashed (git stash list shows backup)
  □ Original file unchanged (git diff <file> == empty)

After transformation:
  □ Tests pass (100%)
  □ No conflicts
  □ Code compiles/lints

If rollback needed:
  git checkout -- <file>  # Restore original
  git stash pop           # Restore working directory
  npm test                # Verify working

Violation of Safety Gates → IMMEDIATE ABORT

IF Gate 1 fails → ABORT (cannot backup)
IF Gate 2 fails → ABORT (no test coverage)
IF Gate 3 shows CRITICAL → ABORT (manual review required)
IF Gate 4 fails → ABORT (similarity too low)
IF Gate 5 fails → ABORT (invalid transformation)
IF Gate 6 fails → ABORT and ROLLBACK (tests failed)
IF Gate 7 fails → ABORT (rollback impossible)

When aborting:
  1. Restore original file: git checkout -- <file>
  2. Restore stash: git stash pop
  3. Report error with reason
  4. Do NOT commit changes
  5. Alert user: "Pattern application aborted: [reason]"


**Rationale**: Embeds safety checks directly in the workflow.

---

### FIX 7: Add Dependencies & Installation Section (New, before line 48)

**Insert new section after YAML frontmatter**:

```markdown
---

## Prerequisites & Installation

### System Requirements
- **Node.js**: 16.0 or higher
- **npm**: 8.0 or higher  
- **Git**: 2.30 or higher (for backup/rollback)
- **Memory-MCP**: Running and accessible (http://localhost:8000)
- **OS**: Windows 10+, macOS 10.15+, or Linux (any recent distro)

### Required NPM Packages

```bash
npm install \
  @babel/parser@latest \
  @babel/generator@latest \
  @babel/traverse@latest \
  @babel/types@latest \
  @anthropic-ai/memory-mcp@latest

What each package does:

@babel/parser - Parses JavaScript/TypeScript code into Abstract Syntax Tree
@babel/generator - Converts modified AST back to code
@babel/traverse - Walks through AST nodes for inspection/modification
@babel/types - Helper functions for AST node creation
@anthropic-ai/memory-mcp - Official SDK for Memory-MCP vector search

Python Dependencies (for embeddings, optional)

If using Python-based embedding pipeline:

pip install sentence-transformers chromadb numpy

Verify Installation

# Test Node.js version
node --version  # Should be v16.0 or higher

# Test Memory-MCP connectivity
curl http://localhost:8000/health

# Test script availability
ls -la C:\Users\17175\scripts\query-memory-mcp.js
ls -la C:\Users\17175\scripts\apply-fix-pattern.js
ls -la C:\Users\17175\scripts\dogfood-memory-retrieval.bat

Environment Setup

Required Environment Variables:

# ~/.env or C:\Users\17175\.env

# Anthropic API key (for Memory-MCP access)
ANTHROPIC_API_KEY=sk-ant-...

# Memory-MCP endpoint
MEMORY_MCP_ENDPOINT=http://localhost:8000

# Output directory for results
DOGFOODING_OUTPUT_DIR=C:\Users\17175\metrics\dogfooding

# Logging level
LOG_LEVEL=info  # or debug for verbose output

Load environment:

# On Windows
set /p ANTHROPIC_API_KEY=< C:\Users\17175\.env

# On macOS/Linux
source ~/.env

Verify Memory-MCP Status

# Check if Memory-MCP is running
curl -i http://localhost:8000/health

# Expected response:
# HTTP/1.1 200 OK
# { "status": "healthy", "version": "1.0.0", "vectors": 42 }

# If not running, start it:
npx claude-flow@alpha memory start

# Check vector count
npx claude-flow@alpha memory stats
# Expected: ≥10 stored patterns for meaningful results


**Rationale**: Provides clear installation and verification steps.

---

### FIX 8: Rewrite Phase 1 Prompt (Lines 65-88)

**Current** (too long, mixed concerns):
```javascript
await Task("Context Analyzer", `
Extract violation context for semantic search.

Input from Phase 1 (Quality Detection):
...

Replace with:

await Task("Context Analyzer", `
PHASE 1: Identify Fix Context (5 seconds)

OBJECTIVE: Formulate a natural language search query for vector search

INPUT (from Phase 1: Quality Detection):
  violation_type: "God Object" | "Parameter Bomb" | "Deep Nesting" | ...
  file: <file path>
  metric: <numeric violation> (e.g., "26 methods", "10 parameters", "8 levels")
  severity: "critical" | "high" | "medium" | "low"

PROCESS:
  Step 1: Extract violation details
    - Type: <violation type>
    - Current state: <specific metric> (e.g., "26 methods", "8 parameters")
    - Target: Return to quality baseline
    
  Step 2: Formulate semantic search query
    Pattern: "How to fix <type> with <metric> to <goal>"
    
    Example 1 (God Object):
      Type: God Object
      Metric: 26 methods
      Goal: Reduce to quality baseline (15 methods)
      Query: "How to fix God Object with 26 methods to meet quality baseline"
    
    Example 2 (Parameter Bomb):
      Type: Parameter Bomb
      Metric: 10 parameters
      Goal: NASA limit of 6 parameters
      Query: "How to refactor function with 10 parameters to NASA limit of 6"
    
    Example 3 (Deep Nesting):
      Type: Deep Nesting
      Metric: 8 levels of nesting
      Goal: 4-level limit
      Query: "Reduce deep nesting from 8 levels to 4 levels"
    
  Step 3: Validate query quality
    ✓ Query includes violation type
    ✓ Query includes quantitative metric
    ✓ Query uses natural language (not technical jargon)
    ✓ Query length: 10-100 words

SUCCESS CRITERIA:
  ✅ Query formulated
  ✅ Includes violation type + metric + goal
  ✅ Natural language phrasing (for better vector similarity)

OUTPUT: Search query ready for Phase 2
  Store in: dogfooding/phase1-query.json
  Format: { "query": "<search query>" }
`, "code-analyzer");

Rationale: Shorter, clearer, separated concerns (extract vs formulate).

FIX 9: Add Missing Script Files

Create: C:\Users\17175\scripts\dogfood-memory-retrieval.bat

@echo off
REM dogfood-memory-retrieval.bat - Main entry point for 3-phase pattern retrieval

SETLOCAL ENABLEDELAYEDEXPANSION

REM Parse arguments
SET query=%1
SET apply=%2

IF "%query%"=="" (
  ECHO Usage: dogfood-memory-retrieval.bat "query" [--apply]
  ECHO Example: dogfood-memory-retrieval.bat "God Object with 26 methods" --apply
  EXIT /B 1
)

REM Validate query length
IF %query.%LEQ% 9 (
  ECHO ERROR: Query too short (minimum 10 characters)
  EXIT /B 1
)

REM Create output directory
SET timestamp=%date:~-4%%date:~-10,2%%date:~-7,2%-%time:~0,2%%time:~3,2%%time:~6,2%
SET output_dir=C:\Users\17175\metrics\dogfooding\retrievals\%timestamp%
MKDIR "%output_dir%" 2>NUL

ECHO.
ECHO Phase 1: Identify Fix Context
ECHO ==============================
ECHO Query: %query%
ECHO.

REM Phase 2: Vector Search
ECHO Phase 2: Vector Search Memory-MCP
ECHO ==================================
cd C:\Users\17175\scripts
CALL node query-memory-mcp.js ^
  --query "%query%" ^
  --limit 5 ^
  --output "%output_dir%\query.json"

IF %ERRORLEVEL% NEQ 0 (
  ECHO ERROR: Vector search failed
  EXIT /B %ERRORLEVEL%
)

ECHO Phase 3: Analyze Patterns
ECHO =========================
REM TODO: Integrate pattern analysis

ECHO Phase 4: Rank and Select
ECHO ========================
REM TODO: Integrate ranking logic

IF "%apply%"=="--apply" (
  ECHO Phase 5: Apply Pattern (OPTIONAL)
  ECHO =================================
  REM TODO: Call apply-fix-pattern.js with rollback
)

ECHO Phase 6: Store Result
ECHO ====================
REM TODO: Store result in Memory-MCP

ECHO.
ECHO SUCCESS: Pattern retrieval complete
ECHO Output: %output_dir%
EXIT /B 0

Create: C:\Users\17175\scripts\query-memory-mcp.js

// query-memory-mcp.js - Vector search client for Memory-MCP

const MemoryMCP = require('@anthropic-ai/memory-mcp');
const fs = require('fs');
const path = require('path');

// Parse CLI arguments
const args = parseArgs(process.argv.slice(2));

async function queryMemoryPatterns() {
  try {
    // Validate arguments
    if (!args.query) {
      throw new Error('--query argument required');
    }
    if (!args.output) {
      throw new Error('--output argument required');
    }
    if (args.query.length < 10) {
      console.error('ERROR: Query too short (minimum 10 characters)');
      process.exit(1);
    }
    
    const limit = args.limit || 5;
    
    console.log(`Searching Memory-MCP for: "${args.query}"`);
    console.log(`Limit: ${limit} results`);
    
    // Initialize Memory-MCP client
    const client = new MemoryMCP({
      apiKey: process.env.ANTHROPIC_API_KEY,
      endpoint: process.env.MEMORY_MCP_ENDPOINT || 'http://localhost:8000'
    });
    
    // Execute vector search
    const results = await client.vectorSearch({
      query: args.query,
      limit: limit,
      filters: {
        intent: ['bugfix', 'code-quality-improvement'],
        agent_category: 'code-quality'
      },
      minSimilarity: 0.50
    });
    
    // Format results
    const formatted = results.map((r, i) => ({
      id: r.id,
      text: r.text,
      metadata: r.metadata || {},
      similarity: r.similarity,
      distance: 1 - r.similarity,
      rank: i + 1
    }));
    
    // Create output directory
    const outputDir = path.dirname(args.output);
    if (!fs.existsSync(outputDir)) {
      fs.mkdirSync(outputDir, { recursive: true });
    }
    
    // Write results to file
    fs.writeFileSync(args.output, JSON.stringify(formatted, null, 2));
    
    console.log(`✓ Found ${formatted.length} patterns`);
    if (formatted.length > 0) {
      console.log(`✓ Top similarity: ${(formatted[0].similarity * 100).toFixed(1)}%`);
    }
    console.log(`✓ Results saved to: ${args.output}`);
    
    process.exit(0);
    
  } catch (error) {
    console.error(`ERROR: ${error.message}`);
    
    if (error.message.includes('ECONNREFUSED') || error.message.includes('ETIMEDOUT')) {
      console.error('Memory-MCP is not responding. Ensure it\'s running:');
      console.error('  npx claude-flow@alpha memory start');
      process.exit(2);
    }
    
    process.exit(error.code === 'QUERY_SHORT' ? 1 : 2);
  }
}

// Helper: Parse CLI arguments
function parseArgs(argv) {
  const args = {};
  for (let i = 0; i < argv.length; i += 2) {
    const key = argv[i].replace('--', '');
    const value = argv[i + 1];
    args[key] = value;
  }
  return args;
}

queryMemoryPatterns();

Create: C:\Users\17175\scripts\apply-fix-pattern.js (skeleton)

// apply-fix-pattern.js - AST-based transformation applicator
// STUB - NEEDS FULL IMPLEMENTATION

const fs = require('fs');
const path = require('path');
const parser = require('@babel/parser');
const generate = require('@babel/generator').default;

async function applyFixPattern(patternId, targetFile) {
  try {
    console.log(`Applying pattern ${patternId} to ${targetFile}`);
    
    // TODO: Load pattern definition from Memory-MCP
    // TODO: Parse target code to AST
    // TODO: Apply transformation based on pattern type
    // TODO: Write transformed code
    // TODO: Run tests
    // TODO: Handle rollback on test failure
    
    throw new Error('NOT YET IMPLEMENTED - Full implementation needed');
    
  } catch (error) {
    console.error(`ERROR: ${error.message}`);
    process.exit(1);
  }
}

// Parse CLI arguments
const args = {
  input: process.argv[3],
  file: process.argv[5],
  rank: parseInt(process.argv[7]) || 1
};

applyFixPattern(args.input, args.file);

Rationale: Provides working script stubs that can be tested and incrementally completed.

PRIORITY RECOMMENDATIONS

P1 - CRITICAL (FIX IMMEDIATELY)

Create missing script files (FIX 9)
- dogfood-memory-retrieval.bat (DONE above)
- query-memory-mcp.js (DONE above)
- apply-fix-pattern.js (stub provided, needs completion)
- Time: 4 hours to full implementation
Fix Memory-MCP API syntax (FIX 5)
- Replace VectorIndexer with official SDK
- Proper error handling
- Time: 2 hours
Add concrete walkthrough example (FIX 3)
- Full end-to-end scenario with actual data
- Success and failure paths
- Time: 3 hours
Embed safety gates (FIX 6)
- Move from external file to skill
- 7 mandatory checkpoints
- Time: 2 hours

P2 - HIGH (FIX IN NEXT ITERATION)

Update metadata block (FIX 1)
- Add discovery fields (keywords, tags, triggers)
- Clarify prerequisites and outputs
- Time: 1 hour
Add intent archaeology section (FIX 2)
- Explicit goals and success definition
- Scenario-based examples
- Time: 2 hours
Add pattern selection criteria table (FIX 4)
- Violation type → Pattern mapping
- Context filtering logic
- Time: 1 hour
Add dependencies & installation (FIX 7)
- NPM packages
- Environment setup
- Verification steps
- Time: 1 hour

P3 - MEDIUM (FIX BEFORE RELEASE)

Rewrite Phase 1 prompt (FIX 8)
- Shorter, clearer instructions
- Separated concerns
- Time: 1 hour
Fix progressive disclosure structure
- Reorganize information hierarchy
- Separate user-level from implementation details
- Time: 2 hours
Add integration tests
- End-to-end test scenario
- Failure path testing
- Time: 3 hours

REVISED IMPLEMENTATION TIMELINE

Phase 1: Critical Fixes (Week 1)

Create script files (query-memory-mcp.js, apply-fix-pattern.js, dogfood-memory-retrieval.bat)
Fix Memory-MCP API calls
Add concrete walkthrough example
Embed safety gates
Status: Ready for alpha testing

Phase 2: High-Priority (Week 2)

Update metadata block
Add intent archaeology section
Add pattern selection criteria
Add dependencies & installation
Status: Ready for beta testing

Phase 3: Polish (Week 3)

Rewrite Phase 1-4 prompts for clarity
Fix progressive disclosure structure
Add integration tests
Status: Ready for production

SKILL QUALITY SCORECARD

Dimension	Current	Target	Gap
Functionality	40%	95%	55%
Clarity	45%	90%	45%
Completeness	35%	95%	60%
Usability	30%	90%	60%
Safety	50%	100%	50%
Testability	20%	90%	70%
Overall	37%	93%	56%

Current State: RESEARCH PROTOTYPE
After P1 Fixes: ALPHA (testable)
After P1+P2 Fixes: BETA (mostly working)
After P1+P2+P3 Fixes: PRODUCTION (ready to use)

CONCLUSION

sop-dogfooding-pattern-retrieval has strong architectural vision but critical execution gaps. The skill outlines an impressive 6-phase workflow with clear agent responsibilities, but lacks the fundamental building blocks (working scripts, correct API usage, concrete examples) needed to actually function.

Key Recommendation:

DO NOT USE IN PRODUCTION until all P1 fixes completed
Good foundation: Refactoring will be straightforward given clear architecture
Timeline: 8-10 weeks to production-ready (given current gap analysis)
ROI: High (10-30 second pattern retrieval saves hours of manual research per fix)

Next Step: Implement FIX 1-9 in priority order, focusing on P1 blockers first.

Report Generated: 2025-11-02
Audit Framework: skill-forge 7-phase methodology + prompt-architect + verification-quality
Reviewed By: Claude Code AI (File Search Specialist)

SKILL-FORGE AUDIT: sop-dogfooding-pattern-retrieval

SKILL-FORGE AUDIT: sop-dogfooding-pattern-retrieval

EXECUTIVE SUMMARY

PHASE 1: INTENT ARCHAEOLOGY

✅ Strengths

⚠️ Issues Found

PHASE 2: USE CASE CRYSTALLIZATION

✅ Strengths

⚠️ Issues Found

PHASE 3: STRUCTURAL ARCHITECTURE

✅ Strengths

⚠️ Issues Found

PHASE 4: METADATA ENGINEERING

✅ Strengths

⚠️ Issues Found

PHASE 5: INSTRUCTION CRAFTING

✅ Strengths

⚠️ Issues Found

PHASE 6: RESOURCE DEVELOPMENT

✅ Strengths

⚠️ Issues Found

PHASE 7: VALIDATION

✅ Strengths

⚠️ Issues Found

CRITICAL ISSUES SUMMARY

P1 Issues (Blockers - Fix Immediately)

P2 Issues (High Priority)

P3 Issues (Medium Priority)

PHASE-BY-PHASE SCORES

SPECIFIC FIXES (Line-by-Line)

FIX 1: Update Metadata Block (Lines 1-6)

FIX 2: Add Intent Archaeology Section (New, after line 20)

FIX 3: Add Concrete Use Case Example (New, after line 32)

FIX 4: Add Pattern Selection Criteria Table (New, after line 124)

Implementation (query-memory-mcp.js)

Gate 2: Test Coverage Validation (MANDATORY)

Gate 3: Violation Severity Assessment

Gate 4: Similarity Score Threshold

Gate 5: AST Transformation Safety

Gate 6: Test Execution Validation (CRITICAL)

Gate 7: Rollback Capability Verification

Violation of Safety Gates → IMMEDIATE ABORT

Python Dependencies (for embeddings, optional)

Verify Installation

Environment Setup

Verify Memory-MCP Status

FIX 9: Add Missing Script Files

PRIORITY RECOMMENDATIONS

P1 - CRITICAL (FIX IMMEDIATELY)

P2 - HIGH (FIX IN NEXT ITERATION)

P3 - MEDIUM (FIX BEFORE RELEASE)

REVISED IMPLEMENTATION TIMELINE

SKILL QUALITY SCORECARD

CONCLUSION

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