Borrowing it
Nothing to install: this file belongs to a-tokyo/agent-skills-harness. 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/a-tokyo/agent-skills-harness/main/AGENTS.mdgit clone --depth 1 https://github.com/a-tokyo/agent-skills-harnessWrote 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/a-tokyo/agent-skills-harness/agents-md)<a href="https://agentmods.dev/instructions/a-tokyo/agent-skills-harness/agents-md"><img src="https://agentmods.dev/badge/instructions/a-tokyo/agent-skills-harness/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/a-tokyo/agent-skills-harness/agents-md"><img src="https://agentmods.dev/badge/instructions/a-tokyo/agent-skills-harness/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 | $0.01922 | $0.01922 |
| Opus 5 | $0.00961 | $0.00961 |
| Sonnet 5 | $0.00384 | $0.00384 |
| Haiku 4.5 | $0.00192 | $0.00192 |
Grade C, and why
agent-skills-harness AGENTS.md scanned grade C 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 9d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf ../agent-skills/skills/create-skill-autoresearch How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Project Overview
Agent Skills Harness is a factory and testing ground for building production-grade agent skills. It provides a structured pipeline for creating skills that are benchmarked against gold standards, autonomously improved via autoresearch loops, and verified through multi-agent consensus.
The core output is the create-skill-autoresearch factory skill, which orchestrates the entire skill creation lifecycle.
Repository Structure
.agents/skills/ # Skills the harness USES (factory + vendored companions)
create-skill-autoresearch/ # The factory skill (main deliverable; original)
autoresearch/ # Autonomous experimentation loop (vendored)
production-grade/ # Engineering posture principles (vendored)
premortem/ # Risk analysis before execution (vendored)
handoff/ # Context preservation across sessions (vendored)
documentation-writer/ # Diataxis documentation generation (vendored)
llm-council/ # Multi-agent planning with consensus (vendored)
writing-great-skills/ # Skill-writing craft (vendored)
tribunal/ # Phase 5 verification when delegated (vendored)
design-taste-frontend/ # Anti-slop frontend/UI skill (vendored)
builds/ # Skills the harness PRODUCES (gitignored; one folder per build)
self-test/ # The factory's own regression test + worked example
site/ # Docs + landing site (Nextra → Vercel)
skills-lock.json # Provenance + version pins for vendored skills
docs/
reference/io-contract.md # What goes in / what comes out (start here)
reference/workspace-layout.md # Full file-by-file build layout
reference/ # rubric-format, metric-protocol, ...
thoughts/ # Research notes and design decisions
study/ # Case study materials (gitignored; maintainer-local)
resources/ # External reference implementations (git submodules)
usage-guide.md # How to use the factory
architecture.md # Design overview
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.
- 9d ago First seen · 118 lines · 1,922 tokens per session scan C 5a7c19505487
agent-skills-harness AGENTS.md is an instructions file published in the GitHub repository a-tokyo/agent-skills-harness (10 stars, last pushed 1mo ago), licensed MIT. It adds 1,922 tokens to every session, about $0.0096 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
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).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.