Codex Autoresearch is an autonomous experimentation system for Codex that repeatedly changes a repository, measures the result, and keeps or reverts each change. It is for optimizing measurable outcomes such as test failures, coverage, type errors, warnings, latency, binary size, or reproducible security findings. The catalogue entry provides the Codex skill that runs and manages these experiment loops.
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 leo-lilinxiao/codex-autoresearch --skill codex-autoresearchgit clone --depth 1 https://github.com/leo-lilinxiao/codex-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/skills/leo-lilinxiao/codex-autoresearch/codex-autoresearch)<a href="https://agentmods.dev/skills/leo-lilinxiao/codex-autoresearch/codex-autoresearch"><img src="https://agentmods.dev/badge/skills/leo-lilinxiao/codex-autoresearch/codex-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/leo-lilinxiao/codex-autoresearch/codex-autoresearch"><img src="https://agentmods.dev/badge/skills/leo-lilinxiao/codex-autoresearch/codex-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 | $0.00039 | $0.00506 |
| Opus 5.5 | $0.00016 | $0.00202 |
| Sonnet 5.5 | $0.00008 | $0.00101 |
| Haiku 4.5 | $0.00004 | $0.00051 |
Grade A, and why
codex-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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex Autoresearch
Improve a repository through repeated, reversible experiments:
hypothesize -> change -> measure -> learn -> keep or revert -> repeat
Codex chooses hypotheses and makes code changes. The control script owns measurement, commits, rollback, and the event history.
Respond To The Request
Resolve <control> to this skill's own scripts/autoresearch.py; do not assume it is installed in the target repository.
For a status or results request, run the corresponding command directly:
| Request | Command |
|---|---|
| Status | python3 <control> status --repo <repo> |
| History | python3 <control> history --repo <repo> |
| TSV export | python3 <control> history --repo <repo> --format tsv |
| HTML report | python3 <control> report --repo <repo> |
Report the requested result without starting or resuming experiments. Return the generated path for an HTML report.
Before starting or resuming an experiment, read the applicable workflow unless it is already in context:
- Workflow: starting, continuing, resuming, or changing an experiment.
- Background: launching or controlling a detached run.
Experiment Boundary
- One run owns one Git repository, one numeric metric, one confirmed target, and approved path scopes.
- For a new foreground run, initialize successfully before creating its Goal; the Goal identifies the returned run id. If initialization reports
complete, no Goal is needed. - Use
finishto finalize each coherent experiment. Do not manually commit, revert, or edit run artifacts during an active run. - Only a verified target can mean
complete; an iteration limit, error, or external blocker has its own status. - Validate state through the control script on entry or resume. Use recorded experiments rather than guessing from conversation.
- Surface command and state errors with their diagnostic paths. Never bypass validation, fabricate metrics, or reconstruct missing events.
What ships with it
9 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.
- agents/openai.yaml 301 B
- references/background.md 2.2 KB
- references/workflow.md 7.3 KB
- scripts/autoresearch_core.py 46 KB runs code
- scripts/autoresearch_report.py 23 KB runs code
- scripts/autoresearch.py 56 KB runs code
- scripts/run_contributor_gate.sh 608 B runs code
- scripts/run_skill_e2e.sh 12 KB runs code
- scripts/validate_skill_structure.sh 198 B 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.
- 1mo ago Changed · -86 lines · -41 tokens per session eb8a3453edfd
- 1mo ago First seen · 132 lines · 80 tokens per session scan A 9bf0d6936d50
codex-autoresearch is a skill published in the GitHub repository leo-lilinxiao/codex-autoresearch (3,040 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 506 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 2026-08-30.
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