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/.opencode/commands/autoresearch_improve.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/autoresearch_improve)<a href="https://agentmods.dev/commands/uditgoenka/autoresearch/autoresearch_improve"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/autoresearch_improve/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/autoresearch_improve"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/autoresearch_improve.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 | $0.00015 | $0.01501 |
| Opus 5 | $0.00008 | $0.00750 |
| Sonnet 5 | $0.00003 | $0.00300 |
| Haiku 4.5 | $0.00002 | $0.00150 |
Grade A, and why
autoresearch_improve 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.
This is a copy
95% identical to autoresearch:improve — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXECUTE IMMEDIATELY.
Parse Arguments
Extract from $ARGUMENTS:
Goal:— product area to improve (or full $ARGUMENTS if no keyword)--icporICP:— ideal customer profile description--discover— force inline codebase scan even when context exists--no-discover— skip auto-discover, warn instead--seeds <categories>— override default research category seeds--depth— shallow (5 iterations), standard (15), deep (30)--features— comma-separated feature names to pre-select for PRD generationIterations:or--iterations— default 15. "unlimited" for unbounded.--evals,--evals-interval N
If upstream handoff.json exists in CWD → read it. Map source findings to default seed categories:
- probe → ICP challenges, UX & experience
- predict → Competitor gaps, Revenue & growth
- debug/security → Competitor gaps, ICP challenges
- Override with
--seeds.
Setup (if Goal or ICP missing)
question (single batch): Q1 (Goal): "What product area to improve?" — open text Q2 (ICP): "Who is your ideal customer?" — open text describing target buyer/user Q3 (Pain points): "Top 3 pain points your customers face?" — open text Q4 (Competitors): "Key competitors?" — open text, or "skip" Q5 (Depth): "How deep?" — shallow (5 iterations, quick scan), standard (15, recommended), deep (30+, exhaustive) If all provided inline → skip.
Phase 1: Product Context
Resolve product context (priority chain):
- Learn summary (
autoresearch/learn-*/summary.md, most recent) → read it - README.md (≥500 chars, non-boilerplate) → extract product description
package.json/pyproject.toml/Cargo.tomldescription (≥10 chars) → use it- If ALL above absent AND NOT
--no-discover→ auto-discover: scan 10 key files (manifest, routes, models, config), cap 1500 tokens - If
--discover→ force scan regardless of above - If nothing found → warn: "No product context. Run
/autoresearch_learn --mode summarizefor better results."
Phase 2: Research Loop
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 · 117 lines · 15 tokens per session scan A 7d515e1b484e
autoresearch_improve is a command published in the GitHub repository uditgoenka/autoresearch (6,234 stars, last pushed 28d ago), licensed MIT. It adds 15 tokens to every session and 1,501 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to autoresearch:improve, differing in 8 lines, and is treated as a copy.
Other commands, from other repositories
run-autoresearch
Run an autonomous experiment loop via the Autoresearch Orchestrator agent.
onboard
/anty:onboard — QUEST-Based Conversational Interview.
next-issue
Fetch the next ready-for-agent issue by priority.
plan
/anty:plan — Strategy Kernel Generation.
onboard
/anty:onboard — QUEST-Based Conversational Interview.
review
/anty:review — 5-Question Review Engine.