Borrowing it
Nothing to install: this file belongs to selftune-dev/selftune. 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/selftune-dev/selftune/main/AGENTS.mdgit clone --depth 1 https://github.com/selftune-dev/selftuneWrote 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/selftune-dev/selftune/agents-md)<a href="https://agentmods.dev/instructions/selftune-dev/selftune/agents-md"><img src="https://agentmods.dev/badge/instructions/selftune-dev/selftune/agents-md/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/instructions/selftune-dev/selftune/agents-md"><img src="https://agentmods.dev/badge/instructions/selftune-dev/selftune/agents-md.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 | $0.03409 | $0.03409 |
| Opus 5.5 | $0.01364 | $0.01364 |
| Sonnet 5.5 | $0.00682 | $0.00682 |
| Haiku 4.5 | $0.00341 | $0.00341 |
Grade A, and why
selftune AGENTS.md 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 1mo 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Repository Overview
selftune — Self-improving skills for AI agents. Watches real sessions, learns how users actually work, and evolves skill descriptions to match. Supports Claude Code, Codex, OpenCode, OpenClaw, and Pi.
Stack: TypeScript on Bun for the CLI, Drizzle over Bun SQLite for operational/product state, a separate DuckDB observability-analytics domain, legacy/export JSONL recovery paths, Effect for owned runtime lifecycles, and a local React/Vite dashboard SPA.
Vendored Effect Reference
.repos/effectis the pinned Effect 4 source used as read-only reference material.- Do not edit or import from
.repos/effect; application code must continue importing normal package dependencies. - Before writing Effect code, read
.repos/effect/LLMS.mdand inspect matching source, tests, and API signatures. - Editor search, file watching, and auto-imports must remain excluded from
.repos/**.
Agent-First Architecture
selftune is a skill consumed by AI agents, not a CLI tool used by humans directly.
The user's interaction model is:
- Install the skill:
npx skills add selftune-dev/selftune - Tell their agent: "set up selftune" / "improve my skills" / "how are my skills doing?"
- The agent reads
skill/SKILL.md, routes to the correct workflow, and runs CLI commands
The selftune binary composed in apps/cli/ is the agent's API. The skill definition (skill/SKILL.md) is the product surface. Workflow docs (skill/workflows/) are the agent's instruction manual. Users rarely if ever run selftune commands directly — their coding agent does it for them.
When developing selftune:
- Changes to CLI behavior must be reflected in the corresponding
skill/workflows/*.mddoc - New CLI commands need a workflow doc and a routing entry in
skill/SKILL.md - Error messages should guide the agent, not the human (e.g., suggest the next CLI command, not "check the docs")
- The SKILL.md routing table and trigger keywords are as important as the CLI code itself — they determine whether the agent can find and use the feature
skill/SKILL.mdandskill/workflows/*.mdare shipped product surface for users' agents. Do not put repo-local contributor commands or monorepo workflow there (for example:cd oss/selftune,bun run dev, Vite/HMR details). Put contributor guidance inREADME.md,AGENTS.md, or internal docs instead.
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.
- 1mo ago First seen · 196 lines · 3,409 tokens per session scan A 7929c6ceef88
selftune AGENTS.md is an instructions file published in the GitHub repository selftune-dev/selftune (18 stars, last pushed 2d ago), licensed MIT. It adds 3,409 tokens to every session, about $0.0136 per session on Opus 5.5. 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.
Other instructions, from other repositories
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