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/smallnest/autoresearch/agents-mdgit clone --depth 1 https://github.com/smallnest/autoresearchWrote 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/smallnest/autoresearch/agents-md)<a href="https://agentmods.dev/instructions/smallnest/autoresearch/agents-md"><img src="https://agentmods.dev/badge/instructions/smallnest/autoresearch/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 | $0.01710 | $0.01710 |
| Opus 5 | $0.00855 | $0.00855 |
| Sonnet 5 | $0.00342 | $0.00342 |
| Haiku 4.5 | $0.00171 | $0.00171 |
Grade A, and why
autoresearch 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 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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- autoresearch CLAUDE.md — 94% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This file provides guidance to Codex (Codex.ai/code) when working with code in this repository.
Project Overview
autoresearch is a fully automated software development tool: given a GitHub Issue number, it implements the feature through multi-agent iterative review, then auto-creates a PR, merges it, and closes the Issue. The entire runtime is a single Bash script (run.sh).
Running
# Process Issue #N in the current directory's project
./run.sh 42
# With project path, max iterations, or continue mode
./run.sh -p /path/to/project 42 16
./run.sh -c 42 10 # Continue from last interrupted iteration
# Skip archiving of previous workflows (useful for debugging)
./run.sh --no-archive 42
There are no build/test/lint commands for autoresearch itself. Shell-based regression tests live under tests/, including:
tests/test_extract_score.shtests/test_agent_logic.shtests/test_archive.shtests/test_context_overflow.shtests/test_cleanup.sh
Architecture
Archiving Mechanism
Before a new run starts (and not in continue mode), run.sh automatically archives old workflow data:
- Scans
.autoresearch/workflows/forissue-*directories. - Moves non-current issue directories to
.autoresearch/archive/YYYY-MM-DD-issue-N/. - If the target archive directory already exists, it appends a numeric suffix (e.g.,
-1,-2).
Iteration Loop
Planning Phase: First agent reads Issue → outputs tasks.json with subtask breakdown
Iteration 1: Agent implements current subtask → tests run
Iteration 2+: Agent round-robin review + fix per subtask
→ Score >= 85? → mark subtask passed → advance to next subtask
→ Score < 85? → agent fixes based on review feedback → next iteration
→ All subtasks passed? → auto commit/PR/merge/close Issue
If planning fails (no tasks.json generated), falls back to original mode: implement entire Issue in one go.
Agent rotation (from iteration 2): (iter - 1) % N where N = number of agents.
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 · 148 lines · 1,710 tokens per session scan A f196d827eaed
autoresearch AGENTS.md is an instructions file published in the GitHub repository smallnest/autoresearch (570 stars, last pushed 1mo ago), licensed MIT. It adds 1,710 tokens to every session, about $0.0086 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 2026-08-30.
Other instructions, from other repositories
chatgpt-cli CLAUDE.md
Instructions for kardolus/chatgpt-cli, covering chatgpt-cli — release runbook, prerequisites, 1. cut the release, 2. publish the github release + binaries and 3. update the homebrew tap.
best-claude-hud AGENTS.md
Instructions for GaoSSR/best-claude-hud, covering project agent instructions and release work.
openrouter-mcp-multimodal AGENTS.md
Instructions for stabgan/openrouter-mcp-multimodal, covering agent instructions, before you ship, releasing (read this before publishing), short version and version files (must all match package.json).
cetus AGENTS.md
Instructions for drewnekota/cetus, covering repository instructions and releases.
OpenMicro AGENTS.md
Instructions for stephenleo/OpenMicro, covering openmicro — project conventions and releases.
roamcode AGENTS.md
Instructions for burakgon/roamcode, covering repository instructions for coding agents, public-repository safety, stable release and ota contract, releasing a stable version and ota changes.