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/.claude/commands/autoresearch/debug.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/debug)<a href="https://agentmods.dev/commands/uditgoenka/autoresearch/debug"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/debug/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/debug"><img src="https://agentmods.dev/badge/commands/uditgoenka/autoresearch/debug.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 | $0.00021 | $0.01022 |
| Opus 5.5 | $0.00008 | $0.00409 |
| Sonnet 5.5 | $0.00004 | $0.00204 |
| Haiku 4.5 | $0.00002 | $0.00102 |
Grade A, and why
autoresearch:debug 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 1mo 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_debug — 97% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 98 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 investigateSymptom:or--symptom— error message or behavior descriptionIterations:or--iterations— default 15. "unlimited" for unbounded.--fix— shorthand for--chain fix--severity— filter: critical, high, medium, low--technique— force specific technique--evals,--evals-interval N,--chain
Setup (if required context missing)
If Scope and Symptom both missing:
- Auto-scan: run tests, lint, typecheck to detect existing failures
- AskUserQuestion (single batch): Q1 (Issue): "What's the problem?" — hunt all bugs, specific error, failing tests, CI failure, performance Q2 (Scope): "Which files?" — suggested globs + entire codebase Q3 (Depth): "How deep?" — quick (5), standard (15), deep (30+), unlimited Q4 (After): "When bugs found?" — report only, find and fix (--chain fix), chain to other, ask each time If all provided → skip.
Investigation Techniques
| Technique | When to Use |
|---|---|
| Binary search | Know when it worked, find when it broke |
| Differential | Compare working vs broken state |
| Minimal reproduction | Simplify to smallest failing case |
| Trace | Follow execution path through code |
| Pattern search | Grep for known anti-patterns |
| Working backwards | Start from error, trace to root cause |
Establish Baseline (before loop)
- Auto-scan for failures if no symptom provided
- Create output directory:
autoresearch/debug-{YYMMDD}-{HHMM}/ - TSV header:
# metric_direction: higher_is_better\niteration\ttimestamp\thypothesis\tstatus\ttechnique\tevidence\tfile_line - Metric = cumulative confirmed findings count
Iteration Loop
Phase 1: Review Context
- Read results TSV (past findings)
- Assess: what's been tested, what vectors remain
- If no hypotheses left → early stop
Phase 2: Hypothesize
- Form ONE specific, falsifiable hypothesis
- Format: "I hypothesize that {X} because {evidence}. Test by {Y}."
- Hypothesis must be testable and different from all previous
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.
- 1mo ago First seen · 98 lines · 21 tokens per session scan A 61fe3ef78390
autoresearch:debug is a command published in the GitHub repository uditgoenka/autoresearch (6,763 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 1,022 once invoked, about $0.0001 per session on Opus 5.5. 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.
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