DNYoussef /============================================================================/ /* SOP-DOGFOODING-QUALITY-DETECTION SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/
name: sop-dogfooding-quality-detection version: 1.0.0 description: | [assert|neutral] 3-part dogfooding workflow Phase 1 - Run Connascence analysis, store results in Memory-MCP with WHO/WHEN/PROJECT/WHY tagging. 30-60 seconds execution time. [ground:given] [conf:0.95] [state:confirmed] category: quality tags:
- quality
- testing
- validation author: ruv cognitive_frame: primary: evidential goal_analysis: first_order: "Execute sop-dogfooding-quality-detection workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic quality processes"
/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/
[define|neutral] SKILL := { name: "sop-dogfooding-quality-detection", category: "quality", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/
[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/
[define|neutral] TRIGGER_POSITIVE := { keywords: ["sop-dogfooding-quality-detection", "quality", "workflow"], context: "user needs sop-dogfooding-quality-detection capability" } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/
When to Use This Skill
Use this skill when:
- Code quality issues are detected (violations, smells, anti-patterns)
- Audit requirements mandate systematic review (compliance, release gates)
- Review needs arise (pre-merge, production hardening, refactoring preparation)
- Quality metrics indicate degradation (test coverage drop, complexity increase)
- Theater detection is needed (mock data, stubs, incomplete implementations)
When NOT to Use This Skill
Do NOT use this skill for:
- Simple formatting fixes (use linter/prettier directly)
- Non-code files (documentation, configuration without logic)
- Trivial changes (typo fixes, comment updates)
- Generated code (build artifacts, vendor dependencies)
- Third-party libraries (focus on application code)
Success Criteria
- [assert|neutral] This skill succeeds when: [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Violations Detected*: All quality issues found with ZERO false negatives [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] False Positive Rate*: <5% (95%+ findings are genuine issues) [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Actionable Feedback*: Every finding includes file path, line number, and fix guidance [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Root Cause Identified*: Issues traced to underlying causes, not just symptoms [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Fix Verification*: Proposed fixes validated against codebase constraints [ground:acceptance-criteria] [conf:0.90] [state:provisional]
Edge Cases and Limitations
Handle these edge cases carefully:
- Empty Files: May trigger false positives - verify intent (stub vs intentional)
- Generated Code: Skip or flag as low priority (auto-generated files)
- Third-Party Libraries: Exclude from analysis (vendor/, node_modules/)
- Domain-Specific Patterns: What looks like violation may be intentional (DSLs)
- Legacy Code: Balance ideal standards with pragmatic technical debt management
Quality Analysis Guardrails
CRITICAL RULES - ALWAYS FOLLOW:
- NEVER approve code without evidence: Require actual execution, not assumptions
- ALWAYS provide line numbers: Every finding MUST include file:line reference
- VALIDATE findings against multiple perspectives: Cross-check with complementary tools
- DISTINGUISH symptoms from root causes: Report underlying issues, not just manifestations
- AVOID false confidence: Flag uncertain findings as "needs manual review"
- PRESERVE context: Show surrounding code (5 lines before/after minimum)
- TRACK false positives: Learn from mistakes to improve detection accuracy
Evidence-Based Validation
Use multiple validation perspectives:
- Static Analysis: Code structure, patterns, metrics (connascence, complexity)
- Dynamic Analysis: Execution behavior, test results, runtime characteristics
- Historical Analysis: Git history, past bug patterns, change frequency
- Peer Review: Cross-validation with other quality skills (functionality-audit, theater-detection)
- Domain Expertise: Leverage .claude/expertise/{domain}.yaml if available
Validation Threshold: Findings require 2+ confirming signals before flagging as violations.
Integration with Quality Pipeline
This skill integrates with:
- Pre-Phase: Load domain expertise (.claude/expertise/{domain}.yaml)
- Parallel Skills: functionality-audit, theater-detection-audit, style-audit
- Post-Phase: Store findings in Memory MCP with WHO/WHEN/PROJECT/WHY tags
- Feedback Loop: Learnings feed dogfooding-system for continuous improvement
SOP: Dogfooding Quality Detection
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
Loop 1 of 3-Part System: Code Quality Detection → Memory Storage
Purpose: Automatically detect code
/----------------------------------------------------------------------------/ /* S4 SUCCESS CRITERIA / /----------------------------------------------------------------------------*/
[define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S5 MCP INTEGRATION / /----------------------------------------------------------------------------*/
[define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]
/----------------------------------------------------------------------------/ /* S6 MEMORY NAMESPACE / /----------------------------------------------------------------------------*/
[define|neutral] MEMORY_NAMESPACE := { pattern: "skills/quality/sop-dogfooding-quality-detection/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]
[define|neutral] MEMORY_TAGGING := { WHO: "sop-dogfooding-quality-detection-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S7 SKILL COMPLETION VERIFICATION / /----------------------------------------------------------------------------*/
[direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S8 ABSOLUTE RULES / /----------------------------------------------------------------------------*/
[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]
[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]
[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* PROMISE / /----------------------------------------------------------------------------*/
[commit|confident] SOP_DOGFOODING_QUALITY_DETECTION_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]