Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/gonzalezpazmonica/pm-workspaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/gonzalezpazmonica/pm-workspace/ai-incident)<a href="https://agentmods.dev/commands/gonzalezpazmonica/pm-workspace/ai-incident"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/pm-workspace/ai-incident/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/gonzalezpazmonica/pm-workspace/ai-incident"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/pm-workspace/ai-incident.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.00848 |
| Opus 5 | $0.00009 | $0.00424 |
| Sonnet 5 | $0.00003 | $0.00170 |
| Haiku 4.5 | $0.00002 | $0.00085 |
Grade A, and why
ai-incident scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-incident — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ai-incident
🦉 Los errores de Savia son datos. Aprendemos de ellos para mejorar.
Registrar, categorizar y analizar incidentes donde las recomendaciones o acciones de Savia fallaron o fueron incorrectas.
Categorías de Incidentes
- BIAS — Savia favoreció un resultado sin justificación objetiva
- HALLUCINATION — Savia inventó datos o asumió hechos sin verificar
- CONTEXT-LOSS — Savia olvidó o ignoró información crítica
- OUTDATED — Savia usó datos desactualizados
- BOUNDARY-VIOLATION — Savia excedió sus límites definidos
- CONFIDENCE-MISMATCH — Confianza mostrada alta pero resultado incorrecto
Flujo de Registro
/ai-incident new
Savia presenta formulario interactivo:
- ¿Qué sucedió? — descripción breve del incidente
- ¿Cuándo? — fecha aproximada
- ¿Qué recomendó Savia? — recomendación original
- ¿Qué era lo esperado? — resultado correcto
- ¿Impacto? — bajo/medio/alto/crítico
- ¿Categoría? — bias/hallucination/context-loss/outdated/boundary-violation/confidence-mismatch
- Detalles adicionales — evidencia o contexto
Análisis Automático
Tras 5+ incidentes registrados:
/ai-incident analyze
Genera:
- Estadísticas: total, resueltos, abiertos, tasa
- Top categorías: frecuencia de cada tipo
- Tendencias: patrones (ej: "sprint-planning es el comando más propenso a errores")
- Recomendaciones: acciones para mejorar (cargar datos faltantes, reducir confianza, etc.)
Comandos
/ai-incident list [--proyecto] [--categoría] [--días N]
/ai-incident view {id}
/ai-incident search "{texto}"
/ai-incident analyze [--últimos N]
/ai-incident export [--formato csv|json|md]
Integración con AI Safety
Los incidentes informan automáticamente:
- Recalibración de confianza: si recomendaciones de tipo X tienen alto % de incidentes, bajar confianza
- Actualización de límites: si un límite se viola frecuentemente, considerarlo
- Mejora de context-map: si hay context-loss recurrente, cargar más datos
- Alertas: "Has tenido 3 incidentes en asignaciones — Savia pedirá validación extra"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 122 lines · 17 tokens per session scan A be17f0ceaf35
ai-incident is a command published in the GitHub repository gonzalezpazmonica/pm-workspace (49 stars, last pushed 5d ago), licensed MIT. It adds 17 tokens to every session and 848 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-incident, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
qa-changes
This skill should be used when the user asks to "QA a pull request", "test PR changes", "verify a PR works", "functionally test changes", or when an automated workflow triggers QA validation of code changes. Provides a structured methodology for setting up the environment, exercising changed behavior, and reporting…
doctor
Diagnosticar y reparar problemas del framework Don Cheli, git y entorno. Usa cuando el usuario dice "doctor", "problemas del framework", "don cheli no funciona", "repair Don Cheli", "debug setup", "setup broken", "framework broken", "reparar entorno". Detecta y repara issues de configuración, git y dependencias…
fix
Universal debugging and fix application with semantic code analysis.
doctor
Badi configuration validation. Checks all Badi components and produces a diagnostic report.
http-service
Build, review or debug a Bun HTTP service. Loads the http-service skill, then works the task through its workflow.
gh-issue-use-cypress
Like /gh-issue-use-browser, but pinned to the Cypress MCP — use when your project runs the Cypress MCP for browser automation. Example — /gh-issue-use-cypress "Composer > Save" saving toasts failure but the record persists.