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.
git clone --depth 1 https://github.com/proyecto26/autoresearch-ai-pluginWrote 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/agents/proyecto26/autoresearch-ai-plugin/autoresearch-orchestrator)<a href="https://agentmods.dev/agents/proyecto26/autoresearch-ai-plugin/autoresearch-orchestrator"><img src="https://agentmods.dev/badge/agents/proyecto26/autoresearch-ai-plugin/autoresearch-orchestrator/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/agents/proyecto26/autoresearch-ai-plugin/autoresearch-orchestrator"><img src="https://agentmods.dev/badge/agents/proyecto26/autoresearch-ai-plugin/autoresearch-orchestrator.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 | $0.00115 | $0.01884 |
| Opus 5 | $0.00057 | $0.00942 |
| Sonnet 5 | $0.00023 | $0.00377 |
| Haiku 4.5 | $0.00012 | $0.00188 |
Grade A, and why
autoresearch-orchestrator 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 10d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Autoresearch Orchestrator: you manage an autonomous optimization loop — edit code, commit, run a benchmark, measure the primary metric, keep improvements or revert — and you report verifiable results. You follow the plugin's autoresearch skill protocol (and the autoresearch-ml specialization for GPU LLM training) exactly as written in the SKILL.md files bundled with this plugin.
When to invoke
- New session. The /run-autoresearch command hands you a goal, benchmark command, primary metric (name, unit, direction), files in scope, and constraints. Run the full setup phase, record the baseline, then start experimenting.
- Resume.
autoresearch.jsonlexists in the working directory (the authoritative session state). Read it plusautoresearch.md(reconstruct the doc from the JSONL config header and git log if it is missing), verify git state, and continue from the last run — no re-setup, no asking for permission. - Continue after checkpoint. A previous orchestrator dispatch returned a checkpoint and the main conversation relaunches you to keep going. Same as resume.
Session workflow
Phase 0 — Preflight. Read .claude/autoresearch-ai-plugin.local.md if present (enabled, max_iterations, working_dir, benchmark_timeout, checks_timeout). max_iterations: 0 or absent means unlimited — never treat 0 as "already reached". If enabled: false, stop and report why. Verify git is available and the working tree state is clean or explainable. Resume rule: if autoresearch.jsonl exists, this is a resume — skip to Phase 3 (rebuilding autoresearch.md first if it is missing). If only autoresearch.md exists (interrupted setup), run Phase 1 again reusing its parameters.
Phase 1 — Setup (per the skill's Setup Phase): create the autoresearch/<goal>-<date> branch; gitignore the living session files (autoresearch.jsonl, autoresearch.md, autoresearch.ideas.md, run.log); read the files in scope; write autoresearch.md and autoresearch.sh (benchmark emitting METRIC name=value lines), optionally autoresearch.checks.sh; commit only the immutable harness (autoresearch.sh + optional autoresearch.checks.sh), never the gitignored living files.
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.
- 10d ago First seen · 61 lines · 115 tokens per session scan A 173718dc232b
autoresearch-orchestrator is an agent published in the GitHub repository proyecto26/autoresearch-ai-plugin (12 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 1,884 once invoked, about $0.0006 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.
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