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_evals.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_evals)<a href="https://agentmods.dev/commands/uditgoenka/autoresearch/autoresearch_evals"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/autoresearch_evals/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_evals"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/autoresearch_evals.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 | $0.01280 |
| Opus 5 | $0.00009 | $0.00640 |
| Sonnet 5 | $0.00004 | $0.00256 |
| Haiku 4.5 | $0.00002 | $0.00128 |
Grade A, and why
autoresearch_evals 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 11d 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:evals — 6 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXECUTE IMMEDIATELY.
Parse Arguments
Extract from $ARGUMENTS:
- Positional path to a specific TSV file
--format— output format: text (default console), json, md (markdown file)--compare <path>— (v2.2.0 placeholder, not yet implemented)
Input Discovery
- If path provided → use that TSV directly
- If no path → scan current directory +
autoresearch/*/for*-results.tsvfiles - If multiple found → question: "Which results to analyze?" — list found files
- If none found → question: "Provide path to results TSV"
- Also scan project root for v2.0.03 legacy TSV files (backward compat)
Parse TSV
- Read line 1: extract
# metric_direction: higher_is_better|lower_is_bettercomment- If missing → infer from column names (metric/error_count → guess, or ask user)
- Read line 2: header row → detect available columns
- Read remaining lines: data rows
- Handle missing
timestampcolumn gracefully (v2.0.03 compat)
Column Detection & Analysis
Activate analysis based on columns present in header:
| Column | Analysis |
|---|---|
metric |
Trend direction, plateau detection (3+ flat iterations), diminishing returns, biggest single-iteration jumps |
delta |
Per-iteration efficiency, cumulative improvement, effort-to-gain ratio |
status |
Keep/discard rate, crash frequency, success streaks, failure clusters, longest winning streak |
guard + guard-metric |
Guard failure rate, metric-improved-but-guard-failed analysis |
severity |
Severity distribution (critical/high/medium/low/info), critical discovery rate per iteration |
hypothesis + status |
Confirmation rate, investigation efficiency, most productive techniques |
commit |
File hotspot analysis (cross-ref with git diff for kept commits), change size correlation |
technique |
Technique effectiveness ranking |
dimension |
Dimension coverage completeness (X/12) |
candidate_label + judge_verdict |
Convergence speed, oscillation count |
error_type |
Error category distribution, fix rate per category |
classification |
New vs extension vs duplicate ratio, saturation curve |
convergence_count |
Convergence trajectory |
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.
- 11d ago First seen · 119 lines · 18 tokens per session scan A e253dab0c28d
autoresearch_evals is a command published in the GitHub repository uditgoenka/autoresearch (6,270 stars, last pushed 28d ago), licensed MIT. It adds 18 tokens to every session and 1,280 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:evals, differing in 6 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.