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 skills add proyecto26/autoresearch-ai-plugin --skill autoresearchgit 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/skills/proyecto26/autoresearch-ai-plugin/autoresearch)<a href="https://agentmods.dev/skills/proyecto26/autoresearch-ai-plugin/autoresearch"><img src="https://agentmods.dev/badge/skills/proyecto26/autoresearch-ai-plugin/autoresearch/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/skills/proyecto26/autoresearch-ai-plugin/autoresearch"><img src="https://agentmods.dev/badge/skills/proyecto26/autoresearch-ai-plugin/autoresearch.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 | $0.00209 | $0.05490 |
| Opus 5 | $0.00105 | $0.02745 |
| Sonnet 5 | $0.00042 | $0.01098 |
| Haiku 4.5 | $0.00021 | $0.00549 |
Grade A, and why
autoresearch 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 — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch: Autonomous Experiment Loop
An autonomous optimization loop where Claude edits code, runs a benchmark, measures a metric, and keeps improvements or reverts — repeating forever until stopped.
Core Concept
The loop is simple: edit → commit → run → measure → keep or discard → repeat.
- Primary metric is king. Lower (or higher, depending on direction) is better. Improved → keep the commit. Equal or worse →
git revert. - State survives context resets via
autoresearch.jsonl(append-only log) andautoresearch.md(living session document). - Domain-agnostic. Works for any measurable target: test speed, bundle size, LLM training loss, Lighthouse scores, build times, etc.
- Be careful not to overfit to the benchmarks and do not cheat on the benchmarks. Optimize the real workload, not the measurement harness.
Setup Phase
When the user triggers autoresearch, gather the following (ask if not provided). If $ARGUMENTS is provided, use it as the optimization goal:
- Goal — what to optimize (e.g., "reduce unit test runtime") — use
$ARGUMENTSif provided - Command — the benchmark to run (e.g.,
pnpm test,uv run train.py) - Primary metric — name, unit, and direction (
lowerorhigheris better) - Secondary metrics — optional additional metrics to track for tradeoff monitoring (e.g., memory, compile time)
- Files in scope — which files can be modified
- Constraints — time budget, off-limits files, correctness requirements
Optionally check for .claude/autoresearch-ai-plugin.local.md in the project root for persistent configuration:
---
enabled: true
max_iterations: 50
working_dir: "/path/to/project"
benchmark_timeout: 600
checks_timeout: 300
---
# Autoresearch Configuration
Additional context or notes for this project's autoresearch setup.
enabled— whether autoresearch is active (default: true)max_iterations— stop after N experiments (default: 0 = unlimited)working_dir— override directory for experiment files (default: current directory)benchmark_timeout— benchmark timeout in seconds (default: 600)checks_timeout— correctness checks timeout in seconds (default: 300)
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/autoresearch.checks.sh 498 B runs code
- examples/autoresearch.md 1.4 KB
- examples/autoresearch.sh 2.4 KB runs code
- references/best-practices.md 8.5 KB
- references/confidence-scoring.md 2.5 KB
- scripts/log-experiment.sh 4.4 KB runs code
- scripts/parse-metrics.sh 889 B runs code
- scripts/session-status.sh 4.9 KB runs code
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 · 382 lines · 209 tokens per session scan A 4e2b77718a92
autoresearch is a skill published in the GitHub repository proyecto26/autoresearch-ai-plugin (12 stars, last pushed 1mo ago), licensed MIT. It adds 209 tokens to every session and 5,490 once invoked, about $0.0010 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 skills, from other repositories
arbor-agent-setup-intake
Setup, intake, preflight, and launch-contract phase for open-source Arbor runs. Use when confirming a target project, metric, baseline, dev/test split, config/plugin settings, branch guard, session directory, or when translating a user goal into the precise contract consumed by the coordinator.
arbor-research-agent
Public entrypoint for the Arbor skill suite. Use when a user wants to run an Arbor-style autonomous research or optimization workflow from a natural-language goal, including initial clarification of objective, target project, data, metric, evaluation, permissions, budget, run mode, and then automatic bootstrapping…
arbor-agent-executor
Executor-dispatch phase for Arbor. Use when implementing an Idea Tree node through RunExecutor or RunExecutorParallel semantics: isolated git worktree, executor prompt construction, eval metadata injection, RunTraining policy, smoke/full evaluation, report parsing, artifact persistence, tree update, and insight…
arbor-agent-ideate
Strict IDEATE-stage skill for Arbor. Use immediately after TreeView(format="constraints") when drafting Idea Tree nodes, enforcing the ideadrafting and firstprinciplesprobe behavior, depth-aware idea levels, four-line TreeAddNode hypotheses, conflict checks, and self-filtering against shallow tweaks.
arbor-agent-merge-eval
Merge and evaluation discipline for Arbor. Use for TreeSetMeta metadata, Bdev/Btest separation, eval command templates, score parsing, GitMergeBranch behavior, protected paths, required outputs, metricdirection, trunk/test score updates, medal detection, and final evaluation before stopping.
arbor-agent-plugins-hitl-budget
Domain adaptation, human-in-the-loop, and budget policy phase for Arbor. Use when a run mentions plugins, plugin profiles, mlekaggle, evalcontract, protectedpaths, requiredoutputs, lifecycle hooks, convergence, budgetpolicy, RunTraining stages, AskUser, or interaction modes auto/direction/review/collaborative.