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 baleen37/bstack --skill autoresearchgit clone --depth 1 https://github.com/baleen37/bstackWrote 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/baleen37/bstack/autoresearch)<a href="https://agentmods.dev/skills/baleen37/bstack/autoresearch"><img src="https://agentmods.dev/badge/skills/baleen37/bstack/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/baleen37/bstack/autoresearch"><img src="https://agentmods.dev/badge/skills/baleen37/bstack/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 | $0.00046 | $0.02443 |
| Opus 5.5 | $0.00018 | $0.00977 |
| Sonnet 5.5 | $0.00009 | $0.00489 |
| Haiku 4.5 | $0.00005 | $0.00244 |
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 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch
Autonomous experiment loop: try ideas, keep what works, revert what doesn't, and record every run.
Modeled on karpathy/autoresearch, generalized from
"optimize val_bpb in train.py" to any metric in any repo.
Setup
- Ask (or infer): Goal, Command, Metric (+ direction), Files in scope, Constraints.
- Check
git statusand the current branch. Preserve unrelated user changes, then create a fresh, unique branch such asautoresearch/<goal>-<date>; do not reuse an existing branch. - Read the source files. Understand the workload deeply before writing anything.
mkdir -p .autoresearch, then write.autoresearch/autoresearch.mdand.autoresearch/run.sh. If looping was explicitly requested, copy the bundledscripts/loop.shresource to.autoresearch/loop.shand make it executable. Commit only those setup files. Keep.autoresearch/results.jsonluntracked.- Run the unchanged baseline first. Append it as the first
results.jsonlline, validate the JSONL, and only then start experimenting.
The default operation is exactly one iteration: setup or resume, run one benchmark, keep or revert one change, append one ledger row, then stop. Repeat only when the user explicitly asks for a loop or starts .autoresearch/loop.sh.
autoresearch.md
This is the heart of the session — upstream's program.md. A fresh agent with no context should be
able to read this file and run the loop effectively. Invest time making it excellent.
# Autoresearch: <goal>
## Objective
<Specific description of what we're optimizing and the workload.>
## Metrics
- **Primary**: <name> (<unit>, lower/higher is better)
- **Secondary**: <name>, <name>, ...
## How to Run
`./.autoresearch/run.sh` — outputs `METRIC name=number` lines.
## Files in Scope
<Every file the agent may modify, with a brief note on what it does.>
## Off Limits
<What must NOT be touched.>
## Constraints
<Hard rules: tests must pass, no new deps, etc.>
## What's Been Tried
<Update as experiments accumulate. Note key wins, dead ends, and architectural
insights so the agent doesn't repeat failed approaches.>
What ships with it
1 file 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.
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 · 222 lines · 46 tokens per session scan A 8112f4cbc392
autoresearch is a skill published in the GitHub repository baleen37/bstack (4 stars, last pushed 18d ago), licensed MIT. It adds 46 tokens to every session and 2,443 once invoked, about $0.0002 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 2026-08-31.
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