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/saviaWrote 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/savia/adoption-assess)<a href="https://agentmods.dev/commands/gonzalezpazmonica/savia/adoption-assess"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/savia/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/savia/adoption-assess"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/savia/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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- adoption-assess — 100% identical, 0 lines differ
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.
- 6d ago First seen · 87 lines · 21 tokens per session scan A 70714a7110ae
adoption-assess is a command published in the GitHub repository gonzalezpazmonica/savia (50 stars, last pushed yesterday), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-06.
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