atomic-debug

A debugging workflow that investigates bugs, crashes, test failures, and unexpected behavior by forming and testing hypotheses. It starts with the exact symptom and tests the cheapest likely explanation first.

In plain words
What is it for?
Use it after an error, regression, failing test, crash, or report that something does not work, especially when the affected code or cause is unclear.
Why use it?
It reduces guesswork and discourages temporary fixes that hide the real cause. The process narrows the failure to its source before proposing a change.

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/damusix/atomic-claude/atomic-debug
Any agent
npx skills add damusix/atomic-claude --skill atomic-debug
Clone the repo
git clone --depth 1 https://github.com/damusix/atomic-claude

Made for: Claude Code, Codex.

Per session 114 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,496 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 $0.00114 $0.01496
Opus 5 $0.00057 $0.00748
Sonnet 5 $0.00023 $0.00299
Haiku 4.5 $0.00011 $0.00150

Measured 3d ago against content hash becc4cd2545a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

atomic-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 3d 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.

context/skills/atomic-debug/SKILL.md · 125 lines

How it starts

The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Debug by hypothesis, not by guessing. Cheapest test first. Root cause, not symptom.

Auto-trigger on:

  • Error message or stack trace pasted
  • "broken", "doesn't work", "failing", "crash", "regression", "flaky"
  • Test failure output

Explicit: /atomic-debug or "let's debug X".

Five steps

1. State the symptom exactly

One sentence. Quote the exact error. Note what changed recently (commit, dep, env) if known.

Symptom: `POST /users` returns 500 since commit abc1234. Stack: `TypeError: Cannot read 'id' of undefined at users.ts:42`.

1b. Locate the surface (when not already in context)

Code-intel first (when an index is present). Before dispatching an agent, try atomic code explore "<symptom as a natural-language query>" for a one-shot digest of the failure neighborhood, or atomic code callers <suspect-fn> when the symptom names a symbol. This is a single shell command — cheaper than spawning an investigator and it returns caller-graph context grep would have to reconstruct. Fall through to the investigator dispatch below only when the index is absent or the query returns nothing useful.

If the suspect code isn't already mapped in the conversation (no file:line references, no recent reads of the relevant module), dispatch atomic-investigator BEFORE forming the hypothesis table. Haiku-backed and read-only, so it's cheap. Give it a focused brief:

Locate <symptom-relevant surface>. Report file:line table.

Example: "Locate the request pipeline for POST /users — middleware order, body parsing, auth check. Report file:line table."

Use its file:line — what table as the evidence base for the hypothesis table. The investigator spends Haiku tokens so the main context (running this skill) doesn't burn Sonnet/Opus on grep work.

Skip this step when: the symptom names the exact file:line (e.g. the stack trace pinpoints it), the bug is in code already in context, or the symptom is too abstract to map (e.g. "the build is slow" — there's no surface yet, hypotheses come first).

Read the full file on GitHub · 125 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. 3d ago First seen · 125 lines · 114 tokens per session scan A becc4cd2545a

Subscribe to this mod's changes

atomic-debug is a skill published in the GitHub repository damusix/atomic-claude (83 stars, last pushed 9d ago), licensed MIT. It adds 114 tokens to every session and 1,496 once invoked, about $0.0006 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.

Related

Other skills, from other repositories

rulesync

Generates and syncs AI rule configuration files (.cursorrules, CLAUDE.md, copilot-instructions.md) across 20+ coding tools from a single source. Use when syncing AI rules, running rulesync commands, importing or generating rule files, or managing shared AI coding configurations.

dyoshikawa/rulesync · 64 tokens

agent-workspace-linux

Use when a task needs an isolated hidden Linux desktop or workspace-owned browser: GUI app QA, web/browser/shopping automation, sandboxed app observation, or stale workspace cleanup. Routes agent-workspace-linux MCP tools on demand. Does NOT apply to host desktop/Chrome control, generic MCP setup, or pure code/file…

ilysenko/codex-desktop-linux · 70 tokens

ss-component

Generate a new UI component following the StyleSeed design conventions.

bitjaru/styleseed · 14 tokens

loongsuite-pilot-insight

基于 LoongSuite Pilot / AI Coding Agent 日志生成事件洞察、组织洞察、数据质量、研发效能和 AI Native 使用类 SLS 报表时使用;包含 AI Coding 事件表语义,以及团队报表可选的部门维表、deptuser 组织关系、指标口径和公共 CTE,通常与 sls-dashboard-builder 一起使用。.

alibaba/loongsuite-pilot · 91 tokens

map-review

Interactive 4-section code review using monitor, predictor, and evaluator agents plus the user and maintainer role reviewers on current changes. Use when reviewing a diff, PR, or staged work before merge. Do NOT use to plan or implement; use map-plan or map-efficient.

azalio/map-framework · 58 tokens

runjam-defaults

Default constraints for every RunJam session. Defines output path conventions, dependency checking rules, fallback strategies, and file management discipline. This skill is auto-injected into every session — do not remove. Current session working directory: /Users/guizhan/work/code/runjam.

peintune/runjam · 59 tokens