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 agentmods add agents/ehmo/autoresearch-skill/codexgit clone --depth 1 https://github.com/ehmo/autoresearch-skillWhat 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 | $0.00000 | $0.01554 |
| Opus 5 | $0.00000 | $0.00777 |
| Sonnet 5 | $0.00000 | $0.00311 |
| Haiku 4.5 | $0.00000 | $0.00155 |
Grade A, and why
codex 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 yesterday.
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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch for Codex
Add this to your project's AGENTS.md or equivalent instruction file.
Current protocol version: 2.0.0 (semver). Record this version in every session log you create so a resumed run can detect protocol changes. Changelog at skills/autoresearch/CHANGELOG.md in the autoresearch repo.
Note: Codex runs as a single agent, so the clean-room separation between phases is weaker than in multi-agent setups like Claude Code. You'll have full context of what you found when you start fixing things. The protocol still works because the phased approach forces structured thinking, but the independent-perspective benefit is reduced.
Three modes
Pick one before starting:
- narrow — the user has a specific measurable goal. Work ranked angles in order.
- broad — the user has an aspiration. Generate 3–5 diverse hypotheses, run each on its own branch.
- sweep — general quality improvement loop (legacy default).
Record the mode in a session log so a resumed run knows which path to take.
Narrow mode protocol
Goal Gate (before anything else)
Collect and record:
- Metric name
- Measurement command (prints a parseable value)
- Baseline value (run the command, record the result)
- Target value with comparator (≥, ≤, =)
Refuse to start cycles without all four.
Collect angles (strategies to hit the goal). Each angle is a name + one-sentence hypothesis. If more than one angle is proposed, get an explicit priority ranking (1, 2, 3) from the user before starting. Do not guess.
Execution
Work angle 1 first. Run the cycle below repeatedly on angle 1 until:
- Goal met (measurement ≥/≤/= target) → stop the whole session
- Angle exhausted (two consecutive cycles with zero productive commits) → advance to angle 2
- Three consecutive regressions → revert each, mark angle exhausted, advance
Then angle 2, then angle 3.
Cycle (on current angle)
- Find problems scoped to the current angle's hypothesis. Skip unrelated issues; save them for later in an
ideas.mdbacklog. - Fix one at a time. Test. Commit (
fix(narrow/a<N>): ...) or revert. - Re-run the measurement command. Record the value. Stop if target met.
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.
- yesterday First seen · 163 lines · 0 tokens per session scan A 5004fbd3795c
codex is an agent published in the GitHub repository ehmo/autoresearch-skill (53 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,554 tokens. 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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