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/cache-warm)<a href="https://agentmods.dev/commands/gonzalezpazmonica/pm-workspace/cache-warm"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/pm-workspace/cache-warm.svg" alt="Measured on agentmods" 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 | $0.00832 |
| Opus 5 | $0.00009 | $0.00416 |
| Sonnet 5 | $0.00003 | $0.00166 |
| Haiku 4.5 | $0.00002 | $0.00083 |
Grade A, and why
cache-warm 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 4d 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 cache-warm — 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cache-warm
🦉 Savia pre-calienta el caché de forma predictiva — por hora, por rol, por sprint.
Cargar perfil de usuario
Grupo: Context Engineering — cargar:
identity.md— rol del usuarioworkflow.md— hora de actividad, eventos del sprint
Ver docs/rules/domain/context-map.md.
Parámetros
--role Pre-calentar contexto del rol actual
--time auto|HH:MM Asumir hora (auto = ahora, o especificar hora)
--lang es|en Idioma del output
Flujo
Paso 1 — Detectar hora y rol
- Leer hora actual o parámetro
--time - Leer rol de
identity.md(PM, Tech Lead, Developer, QA, etc.) - Leer
workflow.mdpara saber si hay eventos hoy (sprint-planning, retro, etc.)
Paso 2 — Mapear contexto probable por hora
Morning (08:00-10:30)
- Daily standup context → sprint status, team workload
- Load:
workflow.md(rutina diaria), team status
Late Morning (10:30-12:00)
- Focus time → project context
- Load: proyecto activo, specs, decisions
Afternoon (13:00-15:00)
- Refinement/grooming
- Load: backlog, reglas negocio, estimación
End of Day (15:00-17:00)
- Reporting → DORA, KPIs
- Load: histórico sprints, métricas
Friday PM (16:00-18:00)
- Sprint review + retro
- Load: sprint items, decision-log, retrospective patterns
Paso 3 — Mapear contexto por rol
PM/Scrum Master
- Always: workflow.md, sprint status, team capacity
- If daily: standup context
- If friday: review + retro context
- If wednesday: backlog grooming context
Tech Lead
- Always: tech-radar, team skills matrix
- If code-focused day: PR reviews, specs, architecture
- If friday: incident postmortems
Developer
- Always: my-sprint, my-focus (current task)
- If morning: standup, task context
- If afternoon: refocus on task, code patterns
QA Engineer
- Always: qa-dashboard
- If pre-release: test plans, bug triage
- If regression day: test cases, known issues
Paso 4 — Ejecutar warm-up
- Identificar ficheros a cargar (máx 5 fragmentos)
- Cargar fragmentos en caché (sin mostrar contenido)
- Registrar warm-up en context-tracking
- Mostrar banner:
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.
- 4d ago First seen · 126 lines · 17 tokens per session scan A f15079fe1e3a
cache-warm is a command published in the GitHub repository gonzalezpazmonica/pm-workspace (49 stars, last pushed 2d ago), licensed MIT. It adds 17 tokens to every session and 832 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 cache-warm, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
setup-context
Set up your Skill Memory — a pm-context.md every skill reads so outputs come back tailored to you.
brain
Set up or use your Professional Brain — init, ingest, recall, or review.
context-stats
Display context window usage and token statistics.
meetings-list
List recent meetings and voice memos. Use when the user asks "what meetings did I have", "show my recent recordings", "any meetings today", "list my voice memos", or wants an overview of their meeting history. Also use when they need to find a specific meeting by browsing rather than searching.
meetings-search
Search meeting transcripts and voice memos for people, topics, decisions, commitments, or remembered ideas. Use when the user asks what was discussed, decided, or said across their meeting history. Not for preparing an upcoming meeting; use $meetings-prep.
capturar
Capturar ideas y notas sin interrumpir el flujo de trabajo. Usa cuando el usuario dice "capturar idea", "quick note", "nota rápida", "grab this", "take a note", "guardar idea", "note to self", "capture thought", "I should remember", "guardar esto". Guarda en memoria persistente sin romper el contexto actual.