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.
npx agentmods add skills/prendstapart/plugin-claude-mcp-braindcode-/planning-equipenpx skills add PrendsTaPart/Plugin-Claude-MCP-BraindCode- --skill planning-equipegit clone --depth 1 https://github.com/PrendsTaPart/Plugin-Claude-MCP-BraindCode-Wrote 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/skills/prendstapart/plugin-claude-mcp-braindcode-/planning-equipe)<a href="https://agentmods.dev/skills/prendstapart/plugin-claude-mcp-braindcode-/planning-equipe"><img src="https://agentmods.dev/badge/skills/prendstapart/plugin-claude-mcp-braindcode-/planning-equipe.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 | $0.00071 | $0.01307 |
| Opus 5 | $0.00036 | $0.00654 |
| Sonnet 5 | $0.00014 | $0.00261 |
| Haiku 4.5 | $0.00007 | $0.00131 |
Grade A, and why
planning-equipe 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning équipe (restaurant)
Étape 0 — Références et établissement (obligatoire)
- Charger
${CLAUDE_PLUGIN_ROOT}/reference/directives-outils.mdet appliquer ses règles pendant toute l'exécution (IDs, confirmations, données, formats, erreurs). - S'assurer d'avoir l'
establishment_id(le demander si absent) avant tout appel.
Workflow
- Vue de la semaine —
list_plannings(establishment_id,week= une date dans la semaine voulue, défaut aujourd'hui) : shifts, heures et coût estimé — TOUJOURS l'afficher à l'utilisateur après toute création ou modification (règle : masse salariale ≤ 35 % du CA, seuil maison de./rapido-kb/processus-internes.mdprioritaire). - Congés AVANT les shifts —
list_leaves: demandes en attente et congés approuvés de la période.- RÈGLE DURE : ne JAMAIS créer un shift pour un employé sur un congé
APPROUVÉ — vérifier avant chaque
create_shift. - Traiter les demandes en attente :
approve_leave/reject_leave(establishment_id,leave_id,comments) — décision de l'utilisateur, jamais la vôtre ; un refus mérite un commentaire.
- RÈGLE DURE : ne JAMAIS créer un shift pour un employé sur un congé
APPROUVÉ — vérifier avant chaque
- Créer les shifts —
create_shift(establishment_id,professional_idvialist_employees,dayYYYY-MM-DD,start/endHH:MM,role_label∈ cuisine, salle, plonge, bar, management, livraison ;break_minutes,note). Le shift est publié immédiatement : valider le récapitulatif (qui, quand, quel poste) avant l'appel. - Contrôler les pointages —
list_attendances(establishment_id,date_from/date_to, défaut mois en cours) : croiser pointages vs shifts planifiés et signaler les écarts (retards récurrents, heures sup non planifiées) — factuellement, sans jugement de personne. - Planning hebdomadaire type —
update_employee_schedule(establishment_id,employee_id,schedules=[{day, start_time, end_time, break_duration}]) : ⚠️ REMPLACE ENTIÈREMENT le planning type de l'employé (pas un ajout) — récapituler l'ancien et le nouveau, confirmation obligatoire (hook en filet). - Affecter une tâche —
assign_task(establishment_id,professional_id,name;date,timeHH:MM,priority∈ basse, normale, haute, urgente,category) : la consigne du jour rattachée à la bonne personne.
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.
- 5d ago First seen · 88 lines · 71 tokens per session scan A 3911245a0dbd
planning-equipe is a skill published in the GitHub repository PrendsTaPart/Plugin-Claude-MCP-BraindCode- (8 stars, last pushed 12d ago), licensed Apache-2.0. It adds 71 tokens to every session and 1,307 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…