# Sop Dogfooding Continuous Improvement > Sop Dogfooding Continuous Improvement - Skill: `majiayu000/sop-dogfooding-continuous-improvement` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds add majiayu000/sop-dogfooding-continuous-improvement` - Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/sop-dogfooding-continuous-improvement/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: majiayu000 (https://skillmd.com/u/majiayu000) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/majiayu000/sop-dogfooding-continuous-improvement --- /*============================================================================*/ /* SOP-DOGFOODING-CONTINUOUS-IMPROVEMENT SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: sop-dogfooding-continuous-improvement version: 1.0.0 description: | [assert|neutral] 3-part dogfooding workflow Phase 3 - Full cycle orchestration combining Quality Detection + Pattern Retrieval + Application with automated metrics tracking. 60-120 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-continuous-improvement workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic quality processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "sop-dogfooding-continuous-improvement", 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-continuous-improvement", "quality", "workflow"], context: "user needs sop-dogfooding-continuous-improvement 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: 1. **Static Analysis**: Code structure, patterns, metrics (connascence, complexity) 2. **Dynamic Analysis**: Execution behavior, test results, runtime characteristics 3. **Historical Analysis**: Git history, past bug patterns, change frequency 4. **Peer Review**: Cross-validation with other quality skills (functionality-audit, theater-detection) 5. **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 Continuous Improvement ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. **Loop 3 of 3-Part System**: Full Cycle Orchestration **Purpose**: Automated continuous improvement cy /*----------------------------------------------------------------------------*/ /* 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-continuous-improvement/{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-continuous-improvement-{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_CONTINUOUS_IMPROVEMENT_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]