When Analyzing Skill Gaps Use Skill Gap Analyzer

/============================================================================/ / WHEN-ANALYZING-SKILL-GAPS-USE-SKILL-GAP-ANALYZER SKILL :: VERILINGUA x VERIX EDITION / /============================================================================/

---

name: when-analyzing-skill-gaps-use-skill-gap-analyzer version: 1.0.0 description: | [assert|neutral] Analyze skill library to identify coverage gaps, redundant overlaps, optimization opportunities, and provide recommendations for skill portfolio improvement [ground:given] [conf:0.95] [state:confirmed] category: foundry tags:

• meta-tool
• skill-management
• gap-analysis
• portfolio-optimization

author: ruv cognitiveframe: primary: compositional goalanalysis: firstorder: "Execute when-analyzing-skill-gaps-use-skill-gap-analyzer workflow" secondorder: "Ensure quality and consistency" third_order: "Enable systematic foundry processes" ---

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

[define|neutral] SKILL := { name: "when-analyzing-skill-gaps-use-skill-gap-analyzer", category: "foundry", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ / S1 COGNITIVE FRAME / /----------------------------------------------------------------------------/

[define|neutral] COGNITIVE_FRAME := { frame: "Compositional", source: "German", force: "Build from primitives?" } [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: ["when-analyzing-skill-gaps-use-skill-gap-analyzer", "foundry", "workflow"], context: "user needs when-analyzing-skill-gaps-use-skill-gap-analyzer capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ / S3 CORE CONTENT / /----------------------------------------------------------------------------/

Skill Execution Criteria

When to Use This Skill

• [AUTO-EXTRACTED from skill description and content]
• [Task patterns this skill is optimized for]
• [Workflow contexts where this skill excels]

When NOT to Use This Skill

• [Situations where alternative skills are better suited]
• [Anti-patterns that indicate wrong skill choice]
• [Edge cases this skill doesn't handle well]

Success Criteria

• primary_outcome: "[SKILL-SPECIFIC measurable result based on skill purpose]"
• [assert|neutral] quality_threshold: 0.85 [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• verification_method: "[How to validate skill executed correctly and produced expected outcome]"

Edge Cases

• case: "Ambiguous or incomplete input"

handling: "Request clarification, document assumptions, proceed with explicit constraints"

• case: "Conflicting requirements or constraints"

handling: "Surface conflict to user, propose resolution options, document trade-offs"

• case: "Insufficient context for quality execution"

handling: "Flag missing information, provide template for needed context, proceed with documented limitations"

Skill Guardrails

NEVER:

• "[SKILL-SPECIFIC anti-pattern that breaks methodology]"
• "[Common mistake that degrades output quality]"
• "[Shortcut that compromises skill effectiveness]"

ALWAYS:

• "[SKILL-SPECIFIC requirement for successful execution]"
• "[Critical step that must not be skipped]"
• "[Quality check that ensures reliable output]"

Evidence-Based Execution

selfconsistency: "After completing this skill, verify output quality by [SKILL-SPECIFIC validation approach]" programofthought: "Decompose this skill execution into: [SKILL-SPECIFIC sequential steps]" planand_solve: "Plan: [SKILL-SPECIFIC planning phase] -> Execute: [SKILL-SPECIFIC execution phase] -> Verify: [SKILL-SPECIFIC verification phase]"

Skill Gap Analyzer

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Purpose: Perform comprehensive analysis of skill library to identify missing capabilities, redundant functionality, optimization opportunities, and provide actionable recommendations for skill portfolio improvement.

When to Use This Skill

• When building a new skill library
• Quarterly skill portfolio reviews
• Before large refactoring efforts
• When considering new skill additions
• After major project pivots
• When optimizing resource allocation

Analysis Dimensions

1. Coverage Gap Analysis

• Domain coverage mapping
• Missing capability identification
• Use case scenario testing
• Workflow completeness assessment
• Integration point analysis

2. Redundancy Detection

• Duplicate functionality identification
• Overlapping capability mapping
• Consolidation opportunity analysis
• Version conflict detection
• Naming collision identification

3. Optimization Opportunities

• Under-utilized skill detection
• Over-complex skill identification
• Composability improvement suggestions
• Dependency optimization
• Performance bottleneck analysis

4. Usage Pattern Analysis

• Frequency metrics
• Co-occurrence patterns
• Success rate tracking
• Token efficiency measurement
• Agent utilization patterns

5. Recommendation Generation

• Prioritized action items
• Consolidation strategies
• New skill proposals
• Deprecation candidates
• Restructuring plans

Execution Process

Phase 1: Library Inventory

# Initialize analysis session
npx claude-flow@alpha hooks pre-task --description "Analyzing skill library gaps"

# Scan skill directories
find ~/.claude/skills -name "SKILL.md" -o -name "*.skill.md"

Inventory Script:

function inventorySkills(skillDirectory) {
  const inventory = {
    totalSkills: 0,
    categories: {},
    capabilities: {},
    agents: {},
    complexity: {},
    tags: {}
  };

  // Parse each SKILL.md file
  const skillFiles = findSki

/*----------------------------------------------------------------------------*/
/* 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/foundry/when-analyzing-skill-gaps-use-skill-gap-analyzer/{project}/{timestamp}",
  store: ["executions", "decisions", "patterns"],
  retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := {
  WHO: "when-analyzing-skill-gaps-use-skill-gap-analyzer-{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] WHEN_ANALYZING_SKILL_GAPS_USE_SKILL_GAP_ANALYZER_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [DNYoussef](https://github.com/DNYoussef)
- **Source:** [DNYoussef/context-cascade](https://github.com/DNYoussef/context-cascade)
- **License:** MIT

Install and usage instructions live in the source repository linked above.