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/josemerca/mercadona-user-story-toolkitWrote 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/josemerca/mercadona-user-story-toolkit/prioritize)<a href="https://agentmods.dev/commands/josemerca/mercadona-user-story-toolkit/prioritize"><img src="https://agentmods.dev/badge/commands/josemerca/mercadona-user-story-toolkit/prioritize/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/josemerca/mercadona-user-story-toolkit/prioritize"><img src="https://agentmods.dev/badge/commands/josemerca/mercadona-user-story-toolkit/prioritize.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 | $0.00016 | $0.00461 |
| Opus 5 | $0.00008 | $0.00230 |
| Sonnet 5 | $0.00003 | $0.00092 |
| Haiku 4.5 | $0.00002 | $0.00046 |
Grade A, and why
prioritize 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 11d 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.
What it actually says
Paso 0 (obligatorio): Cargar ground-rules. Antes de proceder, lee
shared-config.mdy aplica:
- §Filosofía del Plugin — modo copiloto, NO inventar, preguntar antes de generar
- §Estilo de Escritura — frases ≤30 palabras, sin adjetivos sin datos, NUNCA inventar métricas
- §Antipatrones Compartidos — los 7 antipatrones a detectar
Si no puedes leer el fichero, detén la ejecución y reporta el problema.
Ejecuta /story-prioritization para priorizar stories validadas y generar batches de entrega iterativos.
Input necesario: Stories validadas con scoring 6 dimensiones (del paso anterior del pipeline o pegadas).
Proceso:
- Leer el SKILL.md de
skills/story-prioritization/ - Cargar stories validadas
- Evaluar cada story con 5 lentes (Value 30%, Learning 25%, Dependencies 20%, Risk of Delay 15%, Inv. Complexity 10%) usando
scripts/score_priority.py - Construir grafo de dependencias → Checkpoint con usuario
- Generar 2 ordenamientos alternativos (cast-wide):
- Plan Value-first: prioriza Priority Score puro
- Plan Learning-first: adelanta stories con Learning≥4 aunque su Priority Score sea menor — útil si hay incertidumbre técnica o de mercado
- Presentar ambos con trade-offs (qué se entrega antes vs. qué se aprende antes)
- Checkpoint con el usuario para elegir antes de generar batches finales
- Generar batches iterativos del plan elegido (2-4 stories por batch)
- Validar reglas anti-waterfall (AW-1 a AW-5)
- Generar reporte de priorización
Siguiente paso: Llevar Batch 1 a tu issue tracker como Sprint/Iteración (o pasarlo a Superpowers para implementación con /superpowers:writing-plans).
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.
- 11d ago First seen · 32 lines · 16 tokens per session scan A aa3ea119655f
prioritize is a command published in the GitHub repository josemerca/mercadona-user-story-toolkit (25 stars, last pushed 4mo ago), licensed MIT. It adds 16 tokens to every session and 461 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 2026-08-30.
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