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_predict.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_predict)<a href="https://agentmods.dev/commands/uditgoenka/autoresearch/autoresearch_predict"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/autoresearch_predict/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_predict"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/autoresearch_predict.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 | $0.00012 | $0.00930 |
| Opus 5 | $0.00006 | $0.00465 |
| Sonnet 5 | $0.00002 | $0.00186 |
| Haiku 4.5 | $0.00001 | $0.00093 |
Grade A, and why
autoresearch_predict 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 9d 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
98% identical to autoresearch:predict — 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 — 95 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 analyzeGoal:or--goal— focus area for analysisDepth:or--depth— shallow (3 personas, 1 round), standard (5, 2), deep (8, 3)--personas N— override persona count (3-8)--rounds N— override debate rounds (1-3)--adversarial— use hostile reviewer personas instead of default--budget N— max findings across all personas (default 40)--fail-on <severity>— CI gate: exit non-zero if findings at/above threshold--incremental— reuse existing knowledge files, update only changed files--chain,--<subcommand>
Remaining text not matching flags = goal description.
Setup (if Scope or Goal missing)
question (single batch): Q1 (Scope): "Which files to analyze?" — suggested globs + entire codebase Q2 (Goal): "What should personas focus on?" — code quality, security, performance, architecture, all Q3 (Depth): "How deep?" — shallow (3 personas, 1 round), standard (5, 2 — recommended), deep (8, 3) Q4 (Chain): "After analysis, chain to?" — debug, security, fix, ship, scenario, no chain If all provided → skip.
Phase 1: Reconnaissance
Scan all in-scope files. Build structured knowledge:
- File inventory with purpose annotations
- Dependency graph (imports/exports)
- API surface (routes, handlers, types)
- Data flow (inputs → processing → outputs → storage)
- Existing test coverage map
Phase 2: Persona Generation
Load references/predict-personas.md for persona definitions.
Default set (5): Architect, Security Analyst, Performance Engineer, Reliability Engineer, Devil's Advocate. Adversarial set (--adversarial): Breaker, Cheater, Scaler, Newbie, Malicious Insider.
Each persona receives: task description + codebase knowledge + their specific evaluation criteria. Personas are isolated — no shared context between them.
Phase 3: Independent Analysis
Each persona analyzes the codebase independently:
- Read relevant code through their lens
- Produce findings with: title, severity, confidence (0-100%), file:line, recommendation
- Max findings per persona: budget / persona_count
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.
- 9d ago First seen · 95 lines · 12 tokens per session scan A 88ff8ff66ae4
autoresearch_predict is a command published in the GitHub repository uditgoenka/autoresearch (6,214 stars, last pushed 27d ago), licensed MIT. It adds 12 tokens to every session and 930 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to autoresearch:predict, 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.