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 scalarian/oh-my-codex --skill autoresearchgit clone --depth 1 https://github.com/scalarian/oh-my-codexWrote 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/scalarian/oh-my-codex/autoresearch)<a href="https://agentmods.dev/skills/scalarian/oh-my-codex/autoresearch"><img src="https://agentmods.dev/badge/skills/scalarian/oh-my-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/scalarian/oh-my-codex/autoresearch"><img src="https://agentmods.dev/badge/skills/scalarian/oh-my-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 | $0.00024 | $0.00372 |
| Opus 5 | $0.00012 | $0.00186 |
| Sonnet 5 | $0.00005 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
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.
What it actually says
Autoresearch
Purpose
Use $autoresearch when the work is not ready for direct implementation and needs a mission loop with durable artifacts, logs, and a launch boundary.
Use When
- you need to investigate patterns before committing to an implementation
- you want a durable optimization or discovery pass
- there is a clear mission, evaluator, or keep policy to refine
Execution Policy
- Define the mission in plain language before launching anything.
- Treat launch readiness as a gate, not a vibe.
- Keep the log useful: phase changes, findings, blockers, next decision.
- Write artifacts the next pass can consume without rereading the whole chat.
Workflow
- Clarify the mission:
- topic
- why it matters
- what counts as a good result
- what should be kept or discarded
- If intent is still fuzzy, route through
$deep-interviewfirst. - Initialize the mission:
omx autoresearch init <mission>
- Record the canonical artifacts:
.omx/research/summary.md.omx/plans/<mission>-requirements.md.omx/logs/autoresearch.log
- Log every real state transition with
omx autoresearch log. - Hand off to
$plan,$team, or direct execution only when the mission is decision-ready.
Deliverables
- a clear mission statement
- research summary with findings and open questions
- explicit launch / refine-further decision
Final Check
- the mission is concrete
- the logs are current
- the next decision is obvious
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 · 53 lines · 24 tokens per session scan A 5d84b97644bc
autoresearch is a skill published in the GitHub repository scalarian/oh-my-codex (73 stars, last pushed 5mo ago), licensed MIT. It adds 24 tokens to every session and 372 once invoked, about $0.0001 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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