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_scenario.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_scenario)<a href="https://agentmods.dev/commands/uditgoenka/autoresearch/autoresearch_scenario"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/autoresearch_scenario/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_scenario"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/autoresearch_scenario.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 | $0.00016 | $0.01075 |
| Opus 5 | $0.00008 | $0.00537 |
| Sonnet 5 | $0.00003 | $0.00215 |
| Haiku 4.5 | $0.00002 | $0.00108 |
Grade A, and why
autoresearch_scenario 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 12d 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
97% identical to autoresearch:scenario — 4 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXECUTE IMMEDIATELY.
Parse Arguments
Extract from $ARGUMENTS:
Scenario:— seed scenario description (or full $ARGUMENTS text if no keyword)Domain:or--domain— web, mobile, API, CLI, data pipeline, infrastructureScope:or--scope— file globs for codebase contextFocus:or--focus— specific dimension to prioritize--depth— shallow (10), standard (20), deep (40+)--format— markdown (default), json, gherkinIterations:or--iterations— default 20. "unlimited" for unbounded.--evals,--evals-interval N,--chain,--<subcommand>
Setup (if Scenario or Domain missing)
question (single batch): Q1 (Scenario): "Describe the feature/flow to explore" Q2 (Domain): "What domain?" — web app, mobile app, API, CLI, data pipeline, infrastructure Q3 (Scope): "Which files for context?" — suggested globs + entire codebase Q4 (Depth): "How deep?" — quick (10), standard (20), deep (40+), unlimited If all provided → skip.
12 Dimensions
| # | Dimension | Explores |
|---|---|---|
| 1 | Happy path | Normal successful flows |
| 2 | Validation | Input boundaries, types, formats |
| 3 | Permissions | Auth, roles, access control |
| 4 | Concurrency | Race conditions, deadlocks, ordering |
| 5 | State | Invalid transitions, corruption |
| 6 | Scale | High volume, large data, many users |
| 7 | Failure | Network errors, timeouts, partial failures |
| 8 | Security | Injection, abuse, bypass attempts |
| 9 | Integration | Third-party failures, API contract violations |
| 10 | Data | Null, empty, unicode, injection, overflow |
| 11 | UX | Confusion, misuse, accessibility |
| 12 | Recovery | Retry, rollback, idempotency |
Establish Baseline
- Read seed scenario + codebase context
- Create output directory:
autoresearch/scenario-{YYMMDD}-{HHMM}/ - TSV header:
iteration\ttimestamp\tscenario\tdimension\tclassification\tseverity\tdescription - No metric_direction comment (exploration, not optimization)
Iteration 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.
- 12d ago First seen · 107 lines · 16 tokens per session scan A d28baada12c5
autoresearch_scenario is a command published in the GitHub repository uditgoenka/autoresearch (6,292 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 1,075 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to autoresearch:scenario, differing in 4 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.
onboard
/anty:onboard — QUEST-Based Conversational Interview.
plan
/anty:plan — Strategy Kernel Generation.
review
/anty:review — 5-Question Review Engine.