Dogfooding System

/============================================================================/ /* DOGFOODING-SYSTEM SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: dogfooding-system version: 1.0.0 description: | [assert|neutral] Self-improvement system enabling Claude Code to detect code quality violations, retrieve past fix patterns from memory, and orchestrate automated improvement cycles. Uses Connascence Analyzer for 7+ v [ground:given] [conf:0.95] [state:confirmed] category: quality tags:

• quality
• testing
• validation author: ruv cognitive_frame: primary: evidential goal_analysis: first_order: "Execute dogfooding-system workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic quality processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "dogfooding-system", 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: ["dogfooding-system", "quality", "workflow"], context: "user needs dogfooding-system 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

Dogfooding System - Self-Improvement Architecture

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

A comprehensive 3-phase self-improvement system that enables Claude Code to automatically im

/----------------------------------------------------------------------------/ /* 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/dogfooding-system/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := { WHO: "dogfooding-system-{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] DOGFOODING_SYSTEM_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]