AutoresearchSAFE
Teams-first Multi-agent orchestration for Claude Code
Overview
Teams-first Multi-agent orchestration for Claude Code
23b879c5f631OBSERVED · 2026-09-29Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| claude-code | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: autoresearch
description: Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior
argument-hint: "[--mission-dir <path>] [--max-runtime <duration>] [--cron <spec>] [--resume <run-id>]"
level: 4
---
<Purpose>
Autoresearch is a stateful skill for bounded, evaluator-driven iterative improvement. It owns one mission at a time, keeps iterating through non-passing results, records each evaluation and decision as durable artifacts, and stops only when an explicit max-runtime ceiling or another explicit terminal condition is reached.
</Purpose>
<Use_When>
- You already have a mission and evaluator from `/deep-interview --autoresearch`
- You want persistent single-mission improvement with strict evaluation
- You need durable experiment logs under `.omc/autoresearch/`
- You want a supported path for periodic reruns via Claude Code native cron
</Use_When>
<Do_Not_Use_When>
- You need evaluator generation at runtime — use `/deep-interview --autoresearch` first
- You need multiple missions orchestrated together — v1 forbids that
- You want the deprecated `omc autoresearch` CLI flow — it is no longer authoritative
</Do_Not_Use_When>
<Contract>
- Single-mission only in v1
- Mission setup/evaluator generation stays in `deep-interview --autoresearch`
- Evaluator output must be structured JSON with required boolean `pass` and optional numeric `score`
- Non-passing iterations do **not** stop the run
- Stop conditions are explicit and bounded, with max-runtime as the primary strict stop hook
</Contract>
<Required_Artifacts>
Canonical persistent storage lives under `.omc/autoresearch/<mission-slug>/` and/or `.omc/logs/autoresearch/<run-id>/`.
Minimum required artifacts:
- mission spec
- evaluator script or command reference
- per-iteration evaluation JSON
- markdown decision logs
Recommended canonical shape:
```text
.omc/autoresearch/<mission-slug>/
mission.md
evaluator.json
runs/<run-id>/
evaluations/
iteration-0001.json
iteration-0002.json
decision-log.md
```
Reuse existing runtime artifacts when available rather than duplicating them unnecessarily.
</Required_Artifacts>
<Workflow>
1. Confirm a single mission exists and evaluator setup is already available.
2. Ensure mode/state is active for `autoresearch` and records:
- mission slug/dir
- evaluator reference
- iteration count
- started/updated timestamps
- explicit max-runtime or deadline
3. On every iteration:
- run exactly one experiment/change cycle
- run the evaluator
- persist machine-readable evaluation JSON
- append a human-readable markdown decision log entry
- continue even when evaluation does not pass
4. Stop when:
- max-runtime ceiling is reached
- user explicitly cancels
- another explicit terminal condition is recorded by the runtime
</Workflow>
<Cron_Integration>
Claude Code native cron is a supported integration point for periodic mission enhancement. In v1, prefer documenting/configuring cron inputs over building a large scheduler UI.
If cron is used:
- keep one mission per scheduled job
- preserve the same mission/evaluator contract
- append new run artifacts rather than overwriting prior experiments
</Cron_Integration>
<Execution_Policy>
- Do not hand execution back to `omc autoresearch`
- Do not create multi-mission orchestration
- Prefer reusing `src/autoresearch/*` runtime/schema helpers where they already match the stricter contract
- Keep logs useful to humans, not only machines
</Execution_Policy>Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
23b879c5f631full audit observations/trust-audit/skill/yeachan-heo__autoresearch.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-09-29 | 23b879c5f631 | SAFE | B | 89 | first audit |
Questions
What does the Autoresearch skill do?
Teams-first Multi-agent orchestration for Claude Code
Is Autoresearch safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Autoresearch access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Autoresearch work with?
Its documentation mentions claude-code. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (23b879c5f631), read on 2026-09-29. The repository is watched, and a new audit runs when it changes — this is the first audit.