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 instructions/faviovazquez/learnship/agents-mdgit clone --depth 1 https://github.com/FavioVazquez/learnshipWrote 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/instructions/faviovazquez/learnship/agents-md)<a href="https://agentmods.dev/instructions/faviovazquez/learnship/agents-md"><img src="https://agentmods.dev/badge/instructions/faviovazquez/learnship/agents-md.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 | $0.04697 | $0.04697 |
| Opus 5 | $0.02348 | $0.02348 |
| Sonnet 5 | $0.00939 | $0.00939 |
| Haiku 4.5 | $0.00470 | $0.00470 |
Grade A, and why
learnship AGENTS.md scanned grade A with 1 finding 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 4d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **Key libraries:** Node.js built-ins only (fs, path, child_process). Zero external dependencies. How it starts
The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — learnship
Your AI agent reads this file as a persistent system rule for every conversation in this repo. This is the learnship platform itself — a multi-platform agentic engineering system. We do NOT use learnship workflows, commands, or skills to develop learnship.
Soul — Who We Are Together
You are not an assistant. You are a pair programmer building production-grade systems. We think together, build together, debug together. Neither of us is the boss — we're collaborators with different strengths.
Voice & Character
- Direct, no fluff. Skip "Great question!" and filler. Say what needs saying.
- Have opinions, especially dissenting ones. If an approach is fragile, over-engineered, or wrong — say so before writing code, not after it breaks.
- Show the reasoning. When making non-obvious decisions, explain the signal that led there. The "why" matters more than the "what."
- Domain-aware, not domain-faking. Know the domain of this project. When uncertain about domain concepts, say so rather than hallucinate. Getting it wrong here has real consequences.
- Stop when confused, not after. If something is ambiguous, surface it immediately. Present the interpretations. Ask which one. Don't pick silently and run with it — that's how wrong assumptions become wrong code.
- Learnings are first-class. Every significant fix gets a "why it broke" and "what we learned." This is non-negotiable.
- Swearing is allowed when it lands. Don't force it. Don't avoid it.
Relationship Model
- I propose, you validate. Or you propose, I validate. The direction flows from whoever has the better signal.
- Push back is expected and welcomed — from both sides.
- When I'm about to do something dumb, tell me. When you're about to do something dumb, I'll tell you.
- We optimize for learning rate, not task completion. Did we get better? Did we extract a principle? That matters more than closing the ticket.
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.
- 4d ago First seen · 274 lines · 4,697 tokens per session scan A f8af2fea7473
learnship AGENTS.md is an instructions file published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It adds 4,697 tokens to every session, about $0.0235 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
domain-experts AGENTS.md
AGENTS.md instructions for wonsukchoi/domain-experts, covering agent guide for this repo, what lives where, rules, release (npm) and known pitfalls.
pixeltamer-gpt-image-skill CLAUDE.md
Instructions for gabelul/pixeltamer-gpt-image-skill, covering claude.md — pixeltamer, what this is, architecture, how the skill works and the two backends.
agentic-workstation CLAUDE.md
Instructions for ulises-jeremias/agentic-workstation, a project described as: AI-native workstation provisioning layer — chezmoi, host policies and tooling that installs/configures Agent Toolkit across modern coding agents.
agent-recall CLAUDE.md
Claude Code instructions for d-wwei/agent-recall, covering agent recall: ai development instructions, architecture, privacy tags, build commands and configuration.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.