autoresearch:debug

A debugging command that investigates bugs by forming hypotheses, testing them, and rejecting explanations that do not fit the evidence.

In plain words
What is it for?
Use it to scan for failures or investigate a stated symptom within selected files, with options for iteration depth, severity, techniques, and applying fixes.
Why use it?
It provides a repeatable way to narrow down the cause of an error, failing test, or unexpected behaviour through repeated experiments.

Command for Claude Code

Install

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.

agentmods
npx agentmods add commands/uditgoenka/autoresearch/debug
Clone the repo
git clone --depth 1 https://github.com/uditgoenka/autoresearch

Made for: Claude Code.

Per session 21 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,022 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00021 $0.01022
Opus 5 $0.00010 $0.00511
Sonnet 5 $0.00004 $0.00204
Haiku 4.5 $0.00002 $0.00102

Measured yesterday against content hash 61fe3ef78390, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.claude/commands/autoresearch/debug.md · 98 lines

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 investigate
  • Symptom: or --symptom — error message or behavior description
  • Iterations: 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:

  1. Auto-scan: run tests, lint, typecheck to detect existing failures
  2. 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)

  1. Auto-scan for failures if no symptom provided
  2. Create output directory: autoresearch/debug-{YYMMDD}-{HHMM}/
  3. TSV header: # metric_direction: higher_is_better\niteration\ttimestamp\thypothesis\tstatus\ttechnique\tevidence\tfile_line
  4. 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

Read the full file on GitHub · 98 lines

Changes

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.

  1. yesterday First seen · 98 lines · 21 tokens per session scan A 61fe3ef78390

Subscribe to this mod's changes

autoresearch:debug is a command published in the GitHub repository uditgoenka/autoresearch (5,966 stars, last pushed 19d ago), licensed MIT. It adds 21 tokens to every session and 1,022 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.