.claude/skills/consensus-validation/SKILL.md
Orquestación de 4-judge panel (reflection, code-review, business, performance)
npx skillsauth add gonzalezpazmonica/pm-workspace consensus-validationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Lanza 4 jueces especializados. Cada uno evalúa independientemente. Output: JSON estructurado con verdicts normalizados, score ponderado, dissents. El 4º juez (performance) usa
performance-auditskill para detectar hotspots y anti-patterns.
Referencia: @docs/rules/domain/consensus-protocol.md
Before invoking consensus, answer sequentially:
type: spec | pr | decision
ref: file_path or PR_number
Dispatch via dag-scheduling skill — all 4 judges are independent (no deps), run as single parallel cohort.
Timeout: 40s por juez (120s total). Cada juez devuelve: verdict + reasoning + confidence (0.0–1.0)
| Judge | Verdict → Score | |---|---| | Reflection | VALIDATED→1.0 / CORRECTED→0.5 / REQUIRES_RETHINKING→0.0 | | Code-review | APROBADO→1.0 / CAMBIOS_MENORES→0.5 / RECHAZADO→0.0 | | Business | VÁLIDO→1.0 / INCOMPLETO→0.5 / INVÁLIDO→0.0 | | Performance | OPTIMAL→1.0 / DEGRADED→0.5 / REGRESSION→0.0 |
if (code_verdict == RECHAZADO) AND (security|gdpr|compliance in reasoning):
final_verdict = REJECTED; return early
if (perf_verdict == REGRESSION) AND (severity == CRITICAL):
final_verdict = REJECTED; return early
score = (reflection × 0.3) + (code × 0.3) + (business × 0.2) + (performance × 0.2)
if score >= 0.75: verdict = APPROVED
elif score >= 0.50: verdict = CONDITIONAL
else: verdict = REJECTED
avg = (reflection + code + business + performance) / 4
for judge in [reflection, code, business, performance]:
if abs(judge_score - avg) > 0.5:
dissents.append(judge)
If dissents and verdict == APPROVED → downgrade to CONDITIONAL
{
"input": {type, ref, timestamp},
"judges": [
{name, verdict, score, reasoning, timeout, elapsed_ms}
],
"veto": {triggered, reason},
"summary": {
"weighted_score": 0.62,
"final_verdict": "CONDITIONAL",
"dissents": ["business-analyst: ..."],
"recommended_action": "corrections_required"
}
}
Escribir a: output/consensus/YYYYMMDD-HHmmss-{type}-{ref}.json
Triggered si: abs(judge_score - promedio) > 0.5
Efecto:
Output: listar dissents con razonamiento
Errors:
SLA: 120s máximo
SDD: opt-in after spec-writer | PR: mandatory if rejected | ADR: opt-in | Audit: output/consensus/
NUNCA: override veto, modificar verdicts post-facto, saltarse jueces
testing
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
tools
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
tools
Sistema proactivo de bienestar individual
development
Search the web to resolve context gaps — documentation, versions, CVEs, best practices. Auto-starts SearxNG Docker if available, falls back to WebSearch.