Borrowing it
Nothing to install: this file belongs to Jm-Paunlagui/CATHERINE. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Jm-Paunlagui/CATHERINE/main/.claude/skills/fleet-init/SKILL.mdgit clone --depth 1 https://github.com/Jm-Paunlagui/CATHERINEWrote 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/jm-paunlagui/catherine/fleet-init)<a href="https://agentmods.dev/skills/jm-paunlagui/catherine/fleet-init"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/fleet-init.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 | $0.00153 | $0.01697 |
| Opus 5 | $0.00077 | $0.00848 |
| Sonnet 5 | $0.00031 | $0.00339 |
| Haiku 4.5 | $0.00015 | $0.00170 |
Grade A, and why
fleet-init 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 yesterday.
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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fleet Init
Two modes. Decide which from the request, and say which you are running.
init— no usableproject-profile.mdfor this repo, or it describes a different project. Build one from evidence.doctor— a profile exists. Verify every claim in it against the repo, and report drift.
Both modes end with a written profile and a short report. Neither mode edits a skill file. If a project fact seems to require a skill edit, that is a finding about the skill, not a licence to edit it.
The rule this skill exists to enforce
A profile written from assumption is worse than no profile, because every agent downstream will trust it and report defects from it. Every line you write into the profile must come from a file you actually opened. Where you could not confirm something, write unverified next to it rather than a plausible guess.
Mode: init
1. Establish the surfaces
A surface is an independently buildable unit — it has its own manifest. Find them:
package.json,pyproject.toml/setup.py,go.mod,Cargo.toml,*.csproj/*.sln,pom.xml/build.gradle,Gemfile,composer.json
One manifest at the root means one surface. Several in subdirectories means a multi-surface repo — profile each separately.
2. Read the stack from the manifest, not from folder names
Take framework and major version from the dependency list, not from a README. Record what you actually found:
| Signal | Tells you |
|---|---|
express, fastify, koa, @nestjs/core |
Node HTTP framework and version |
next |
App Router — check for an app/ directory before assuming |
react + vite with no next |
Vite + React, not Next.js — this distinction routes work to a different agent |
tailwindcss v4 |
Tokens live in @theme; there is correctly no tailwind.config.js |
oracledb, mongodb/mongoose, pg, mysql2, prisma |
Database and access layer |
vitest, jest, mocha, pytest, xunit |
Test framework — then find where tests actually live |
torch, tensorflow, scikit-learn, xgboost |
Which AI-group agent applies, if any |
@anthropic-ai/sdk, openai, langchain |
An LLM pipeline exists |
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.
- yesterday First seen · 123 lines · 153 tokens per session scan A fce6fc88cc16
fleet-init is a skill published in the GitHub repository Jm-Paunlagui/CATHERINE (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 153 tokens to every session and 1,697 once invoked, about $0.0008 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 2026-09-05.
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