autoresearch is an agent workflow that repeatedly changes a project, verifies a measurable result, keeps or discards the change, and continues iterating toward a goal. It is for autonomous improvement tasks in Claude Code, OpenCode, and OpenAI Codex across domains with mechanical success measures. The catalogue contains its commands, hooks, skills, plugin, agent, and instruction.
Borrowing it
Nothing to install: this file belongs to uditgoenka/autoresearch. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/uditgoenka/autoresearch/master/.claude/commands/autoresearch/security.mdgit clone --depth 1 https://github.com/uditgoenka/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/commands/uditgoenka/autoresearch/security)<a href="https://agentmods.dev/commands/uditgoenka/autoresearch/security"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/security/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/commands/uditgoenka/autoresearch/security"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/security.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 | $0.00018 | $0.01263 |
| Opus 5.5 | $0.00007 | $0.00505 |
| Sonnet 5.5 | $0.00004 | $0.00253 |
| Haiku 4.5 | $0.00002 | $0.00126 |
Grade A, and why
autoresearch:security 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- autoresearch_security — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXECUTE IMMEDIATELY.
Parse Arguments
Extract from $ARGUMENTS:
Scope:or--scope— file globs to auditFocus:— specific area (auth, API, data handling, etc.)Depth:or--depth— quick (5 iterations), standard (15), deep (30+)Iterations:or--iterations— default 15. "unlimited" for unbounded.--diff— delta mode: only audit files changed since last audit--fix— after audit, auto-fix Critical/High findings (chains to fix)--fail-on <severity>— exit non-zero if findings at/above threshold (CI gate)--evals,--evals-interval N,--chain,--<subcommand>
Setup (if required context missing)
If Scope missing and no --diff:
- Scan codebase for tech stack, frameworks, API routes
- AskUserQuestion (single batch): Q1 (Scope): "What to audit?" — entire codebase, API + middleware, auth, external-facing Q2 (Depth): "How thorough?" — quick (5), standard (15), deep (30+), unlimited Q3 (Action): "What to do with findings?" — report only, report + auto-fix, report + CI gate If all provided → skip.
Setup Phase (once, before loop)
- Reconnaissance — scan: package.json/requirements.txt (deps), .env.example (secrets), Dockerfile (infra), API route files (attack surface), auth/middleware (trust boundaries), DB schemas (data assets), CI/CD configs (supply chain)
- Asset Identification — catalog data stores, auth systems, external services, user inputs
- Trust Boundary Mapping — browser↔server, public↔authenticated, user↔admin, CI↔prod
- STRIDE Threat Model — generate threats per category. Load
references/security-checklist.mdfor checklist. - Attack Surface Map — entry points, data flows, abuse paths
- Baseline — count known issues, initialize coverage tracking
Create output directory: autoresearch/security-{YYMMDD}-{HHMM}/
Write: overview.md, threat-model.md, attack-surface-map.md
TSV header: # metric_direction: higher_is_better\niteration\ttimestamp\tfinding\tseverity\towasp\tstride\tevidence\tfile_line
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 First seen · 102 lines · 18 tokens per session scan A 3a8aba600475
autoresearch:security is a command published in the GitHub repository uditgoenka/autoresearch (6,763 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 1,263 once invoked, about $0.0001 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.
Other commands, from other repositories
run-autoresearch
Run an autonomous experiment loop via the Autoresearch Orchestrator agent.
next-issue
Fetch the next ready-for-agent issue by priority.
review
/anty:review — 5-Question Review Engine.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.