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 jimezsa/opencolab --skill autoresearchgit clone --depth 1 https://github.com/jimezsa/opencolabWrote 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/jimezsa/opencolab/autoresearch)<a href="https://agentmods.dev/skills/jimezsa/opencolab/autoresearch"><img src="https://agentmods.dev/badge/skills/jimezsa/opencolab/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/jimezsa/opencolab/autoresearch"><img src="https://agentmods.dev/badge/skills/jimezsa/opencolab/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 | $0.00049 | $0.01148 |
| Opus 5 | $0.00024 | $0.00574 |
| Sonnet 5 | $0.00010 | $0.00230 |
| Haiku 4.5 | $0.00005 | $0.00115 |
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 8d 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch Skill
Use this skill for iterative experiment loops over one explicitly configured repo.
This is a normal shared OpenColab skill. Any agent may use it when it is the right tool, but the built-in autoresearch specialist is the default owner for sustained experiment-loop work.
Required Repo Contract
Do not start the loop until these are explicit:
repo_path: local path to the experiment repoeditable_file_path: the only file you may modifyrun_command: the command that launches one experiment runmetric_rule: how to extract the metric and whether higher or lower is better
Optional:
results_file: repo-local results table or summary logbranch_prefix: disposable experiment branch prefix
Default failure rule:
- a run fails when the command exits non-zero or the metric cannot be extracted
Do not assume:
- the editable file is
train.py - the run command is
uv run train.py
Those are upstream examples, not OpenColab requirements.
Core Rules
- Work only inside the configured repo.
- Edit only the configured editable file unless the human explicitly changes the repo contract.
- Treat the configured metric rule as the source of truth for keep/discard decisions.
- Keep changes narrow and reviewable.
- Run one bounded experiment at a time.
- Keep artifacts in the experiment repo unless the user asks for a different handoff.
- If remote GPU execution is needed, combine this workflow with
projects/SKILLS/runpod-job/SKILL.md.
Git Safety Boundary
Use a dedicated disposable branch or worktree for this loop.
Inside that dedicated experiment branch or worktree:
- keep or discard decisions may use branch rewinds
- discard operations are pre-approved for this workflow
Outside that dedicated experiment branch or worktree:
- normal OpenColab safety rules still apply
- do not perform destructive git actions without approval
Minimal Loop
- Confirm the repo contract.
- Enter the configured repo and inspect the current baseline.
- Ensure you are working in the dedicated disposable experiment branch or worktree.
- Make one narrow change in the configured editable file.
- Run the configured experiment command.
- Extract the metric using the configured metric rule.
- Decide keep or discard.
- Record the outcome in the configured results file when one exists.
- Summarize the result and next useful step.
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.
- 8d ago First seen · 147 lines · 49 tokens per session scan A e4cc0d26e4fe
autoresearch is a skill published in the GitHub repository jimezsa/opencolab (11 stars, last pushed 26d ago), licensed MIT. It adds 49 tokens to every session and 1,148 once invoked, about $0.0002 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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