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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/uditgoenka/autoresearch/evalsgit 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/evals)<a href="https://agentmods.dev/commands/uditgoenka/autoresearch/evals"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/evals.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.01285 |
| Opus 5 | $0.00010 | $0.00642 |
| Sonnet 5 | $0.00004 | $0.00257 |
| 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 6d 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_evals — 97% identical, 6 lines differ
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 → AskUserQuestion: "Which results to analyze?" — list found files
- If none found → AskUserQuestion: "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.
- 6d ago First seen · 119 lines · 19 tokens per session scan A 22582ed7a8bb
autoresearch:evals is a command published in the GitHub repository uditgoenka/autoresearch (6,116 stars, last pushed 23d ago), licensed MIT. It adds 19 tokens to every session and 1,285 once invoked, about $0.0001 per session on Opus 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
onboard
/anty:onboard — QUEST-Based Conversational Interview.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.