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/adoption-assess)<a href="https://agentmods.dev/commands/gonzalezpazmonica/pm-workspace/adoption-assess"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/pm-workspace/adoption-assess/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/adoption-assess"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/pm-workspace/adoption-assess.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 | $0.00021 | $0.00831 |
| Opus 5 | $0.00010 | $0.00415 |
| Sonnet 5 | $0.00004 | $0.00166 |
| Haiku 4.5 | $0.00002 | $0.00083 |
Grade A, and why
adoption-assess 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 9d 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 adoption-assess — 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/adoption-assess
🦉 Diagnosticar dónde está el equipo en su viaje de adopción de Savia.
Basado en el modelo ADKAR (Awareness, Desire, Knowledge, Ability, Reinforcement).
Modelo ADKAR (5 Dimensiones)
- Awareness — ¿El equipo sabe que existe Savia y cómo puede ayudar?
- Desire — ¿Quieren usarla? ¿Ven valor en adoptar IA?
- Knowledge — ¿Saben cómo usar los comandos? ¿Conocen las reglas?
- Ability — ¿Pueden usarla en su flujo diario sin fricción?
- Reinforcement — ¿Se refuerza el hábito? ¿Hay celebraciones de éxito?
Flujo
Paso 1 — Recopilar datos (encuesta rápida)
- Preguntar al PM sobre cada dimensión ADKAR
- ¿Cómo está el equipo ahora? (1-5 escala)
- Ejemplos de fricción o resistencia
- Equipos que adoptan bien vs. equipos rezagados
Paso 2 — Scoring
- Cada dimensión: 1 (muy bajo) a 5 (excelente)
- Identificar la dimensión más débil (cuello de botella)
- Calcular score ADKAR global (promedio)
Paso 3 — Análisis
- ¿Cuál es el principal blocante? Ej: falta Knowledge → necesita capacitación
- ¿Hay "early adopters" que pueden mentorar? Reinforce→ estrategia peer-learning
- ¿Qué comando del team usar primero para ganar rápida victoria?
Paso 4 — Intervenciones personalizadas
Recomendar acciones específicas por dimensión débil:
- Awareness baja →
/adoption-plan --awareness(crear storytelling de casos de uso) - Desire baja → demo personal con ROI: "Esto te ahorra 1h/día"
- Knowledge baja →
/adoption-sandbox --learn(entorno seguro de práctica) - Ability baja →
/adoption-track --friction(identificar puntos de dolor) - Reinforcement baja → crear rituales: "Cada viernes, un equipo comparte su victoria con Savia"
Paso 5 — Propuesta de roadmap
- Secuencia de intervenciones en 4-12 semanas
- Hitos: "Semana 2: 50% equipo usa /sprint-status" → "Semana 6: Primer spec con SDD"
- Métrica de éxito por hito
AI Competency Assessment (opcional: --ai-skills)
Extiende ADKAR con 6 competencias AI-era: @docs/rules/domain/ai-competency-framework.md
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.
- 9d ago First seen · 87 lines · 21 tokens per session scan A 70714a7110ae
adoption-assess is a command published in the GitHub repository gonzalezpazmonica/pm-workspace (49 stars, last pushed 6d ago), licensed MIT. It adds 21 tokens to every session and 831 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 adoption-assess, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
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clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.