agentleFS
Ai-assistant-KIRAAi-assistant-KIRA/antigravity-harness/skills/systematic-debugging/SKILL.md

systematic-debugging

Skill1 starsChanged 18 days agoApache-2.0View on GitHub

Use when debugging a crash, failing test, flaky behavior, regression, build failure, or unexpected tool output. Enforces reproduction, evidence, root cause analysis, and verification before a fix, instead of shotgun changes.

190 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use chinese-code-review, chinese-commit-conventions and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • Leodorareluctant259Skill

    chinese-code-review

    中文代码审查规范——在保持专业严谨的同时,用符合国内团队文化的方式给出有效反馈

    Also in 8 projects that use this

  • Leodorareluctant259Skill

    chinese-commit-conventions

    中文 Git 提交规范 — 适配国内团队的 commit message 规范和 changelog 自动化

    Also in 8 projects that use this

  • obraSkill

    diagnosing-superpowers

    Use when a superpowers session went wrong and your human partner wants to know why — repeated work, ignored plans, stumbles, poor results, a skill that didn't fire, "it took too long", "why is it so expensive", "what is it doing" — or wants to build a bug report for the superpowers maintainers, for the current session or a past one identified by id or path, on any harness.

    Also in 20 projects that use this

  • mjunaidcaSkill

    building-chatgpt-apps

    Guides creation of ChatGPT Apps with interactive widgets using OpenAI Apps SDK and MCP servers. Use when building ChatGPT custom apps with visual UI components, embedded widgets, or rich interactive experiences. Covers widget architecture, MCP server setup with FastMCP, response metadata, and Developer Mode configuration. NOT when building standard MCP servers without widgets (use building-mcp-servers skill instead).

    Also in 4 projects that use this

  • mjunaidcaSkill

    building-chat-interfaces

    Build AI chat interfaces with custom backends, authentication, and context injection. Use when integrating chat UI with AI agents, adding auth to chat, injecting user/page context, or implementing httpOnly cookie proxies. Covers ChatKitServer, useChatKit, and MCP auth patterns. NOT when building simple chatbots without persistence or custom agent integration.

    Also in 4 projects that use this

  • +2 more from Leodorareluctant259
  • +5 more from mjunaidca

The file

We split this skill into the rules it gives an agent

10 rules. 0 are common, 10 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 177 edited versions we read keep these.

  • 10

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Write down the exact command, environment, inputs, and complete error.

    line 18
  2. Distinctive:

    Check whether a clean checkout or empty cache changes the result.

    line 23
  3. Distinctive:

    If the failure will not reproduce, keep the logs and say so.

    line 32
  4. Distinctive:

    Start at the deepest stack frame that points to project code.

    line 37
  5. Distinctive:

    Test one hypothesis per experiment.

    line 41
  6. Distinctive:

    Write the chain in plain language:

    line 47
  7. Distinctive:

    Run the original reproduction.

    line 57
  8. Distinctive:

    Run nearby tests, the type checker, the linter, and the build.

    line 58
  9. Distinctive:

    Check the behavior around the change for side effects.

    line 59
  10. Distinctive:

    Remove temporary logs, flags, and scratch files.

    line 60

“Common” is measured: kept by at least 6 in 10 of the 177 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Systematic Debugging

The rule

Do not patch code until you can explain the failure. A change that happens to work hides the cause and creates the next regression.

1. Capture the failure

  1. Write down the exact command, environment, inputs, and complete error.
  2. Decide whether the failure is consistent, intermittent, or tied to one machine or cache.
  3. Reduce it to the smallest reproduction. When possible, encode it as a test that fails for the expected reason.
  4. Check whether a clean checkout or empty cache changes the result.
# Use the project's real commands, not these examples.
python -m pytest tests/test_feature.py -x -vv
npm test -- --runInBand path/to/test
cargo test failing_test -- --nocapture

If the failure will not reproduce, keep the logs and say so. A guess is not a diagnosis.

2. Narrow the search

  • Start at the deepest stack frame that points to project code.
  • Compare with recent work: git diff, git log -p -- <path>, or git bisect.
  • Split the problem by input, feature flag, environment variable, or dependency.
  • Turn off one variable at a time and write down each result.
  • Test one hypothesis per experiment. State the evidence that would prove it wrong before running the experiment.
  • Separate the first error from errors that only follow it.

3. Prove the cause

Write the chain in plain language:

X happens, so Y receives Z. The code expects W, and therefore fails with E.

Prove each link with a small experiment. If a link is missing, go back and narrow further. Do not change production code during this step.

4. Fix, then verify

  1. Make the smallest change that removes the proven cause.
  2. Run the original reproduction. It must pass.
  3. Run nearby tests, the type checker, the linter, and the build.
  4. Check the behavior around the change for side effects.
  5. Remove temporary logs, flags, and scratch files.
  6. Record the failure and its verified recovery so the lesson is available later.
bin\harness.cmd reflect --workspace . --force

Common mistakes

  • Editing several files and hoping one change works.
  • Running the same command again without reading the error or changing an input.
  • Catching the exception, widening a handler, or skipping the test.
  • Saying "fixed" without a passing reproduction.
  • Treating a timeout or rate limit as a permanent code bug.
  • Adding a package before proving the current one is at fault.

Report

Finish with: root cause, evidence, fix, checks run, and remaining risk. If something is still unverified, name it.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.