gonzalezpazmonica/skill-evaluation
.claude/skills/skill-evaluation/SKILL.md
Motor de evaluación inteligente de skills basado en análisis de prompt y contexto
$ install --global
skillsauthnpx skillsauth add gonzalezpazmonica/pm-workspace skill-evaluationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 24, 2026, 8:50 PM1.8s1 file scanned
SKILL.md
- name:
- skill-evaluation
- description:
- Motor de evaluación inteligente de skills basado en análisis de prompt y contexto
- summary:
- |
- Output:
- skills recomendados con score de relevancia.
- maturity:
- stable
- context:
- fork
- context_cost:
- low
- category:
- reporting
- tags:
- ["skill-eval", "prompt-analysis", "scoring", "activation"]
- priority:
- low
Skill Evaluation Engine
§1 Prompt Analysis
Entrada: user_prompt, active_project, available_skills[]
Algoritmo:
- Tokenizar el prompt en keywords
- Para cada skill disponible: a. Calcular keyword_score = matched_keywords / total_keywords * 100 b. Calcular context_score = project_type_match * 100 c. Calcular history_score = previous_activations_success_rate * 100 d. final_score = keyword_score * 0.4 + context_score * 0.3 + history_score * 0.3
- Filtrar skills con final_score > threshold (default 30)
- Ordenar por final_score descendente
- Retornar top-5
Salida: Lista de skills recomendados con scores y razones
§2 Context Detection
Tipos de proyecto detectables:
- software: presencia de package.json, .sln, Cargo.toml, pom.xml
- research: presencia de experiments/, bibliography/, datasets/
- hardware: presencia de hardware/, bom.json, revisions/
- legal: presencia de legal/, deadlines.json, court-calendar.json
- healthcare: presencia de quality/, pdca/, incidents/
- nonprofit: presencia de impact/, volunteers/
- education: presencia de curricula/, classroom/
Mapping proyecto→skills:
- software → architecture-intelligence, developer-experience
- research → predictive-analytics, diagram-generation
- hardware → regulatory-compliance, cost-management
- legal → cost-management, regulatory-compliance
- healthcare → regulatory-compliance, coherence-check
- nonprofit → executive-reporting, cost-management
§3 Instinct Integration
Cuando un instinto de categoría "context" tiene confianza >70%, boost el score de los skills asociados en +20 puntos.
§4 Feedback Loop
Cada activación registra:
- skill_name, timestamp, prompt_summary, user_accepted (bool)
- Si accepted → +2 al history_score futuro
- Si rejected → -3 al history_score futuro
- Registry:
.claude/skills/eval-registry.json
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