debug-systematically

A structured method for finding the cause of a bug, unexpected result, or test failure.

In plain words
What is it for?
It helps debug software through small experiments, targeted hypotheses, tests, and verification of related behavior.
Why use it?
It prevents random changes by requiring the problem to be reproduced, isolated, tested, fixed, and checked again.

Skill for Claude CodeCodex

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 skills/aiming-lab/metaclaw/debug-systematically
Any agent
npx skills add aiming-lab/MetaClaw --skill debug-systematically
Clone the repo
git clone --depth 1 https://github.com/aiming-lab/MetaClaw

Made for: Claude Code, Codex.

Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 216 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 $0.00040 $0.00216
Opus 5 $0.00020 $0.00108
Sonnet 5 $0.00008 $0.00043
Haiku 4.5 $0.00004 $0.00022

Measured 2d ago against content hash be01e4c65043, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

debug-systematically 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 2d 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.

memory_data/skills/debug-systematically/SKILL.md · 23 lines

What it actually says

Debug Systematically

Process:

  1. Reproduce the bug with the smallest possible input.
  2. Isolate — narrow down which component/function causes it.
  3. Hypothesize — form a specific, falsifiable hypothesis.
  4. Test — verify or disprove the hypothesis with a minimal experiment.
  5. Fix — address the root cause, not just the symptom.
  6. Verify — re-run the failing test and related tests.

Useful tools: print/logging, pdb/ipdb, unit tests, git bisect.

Anti-patterns:

  • Changing multiple things at once and not knowing what fixed it.
  • Ignoring related test failures.
  • Commenting out code instead of understanding it.
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. 2d ago First seen · 23 lines · 40 tokens per session scan A be01e4c65043

Subscribe to this mod's changes

debug-systematically is a skill published in the GitHub repository aiming-lab/MetaClaw (3,494 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 216 once invoked, about $0.0002 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.

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