agentleFS
EstebanT1112EstebanT1112/codex-engineering-workflow/skills/systematic-debugging/SKILL.md

systematic-debugging

Skill0 starsChanged 2 months agoMITView on GitHub

Investigate bugs, test failures, build failures, performance regressions, and unexpected behavior by reproducing the issue, tracing evidence, testing one hypothesis, and fixing the root cause. Use before speculative fixes; do not use for planned feature work without a failure.

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

Show its rules and text

We split this skill into the rules it gives an agent

16 rules. 0 are common, 16 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 177 edited versions we read keep these.

  • 16

    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:

    Read the complete useful error, stack trace, status code, and affected path.

    line 15
  2. Distinctive:

    Check current Git changes, relevant recent changes, configuration, dependency versions, and runtime assumptions.

    line 18
  3. Distinctive:

    If reproduction is unsafe or requires production, stop before the effect and build the strongest safe diagnostic path.

    line 21
  4. Distinctive:

    Add temporary diagnostics only when existing evidence is insufficient.

    line 27
  5. Distinctive:

    never dump the full environment.

    line 29
  6. Distinctive:

    never print tokens, credentials, cookies, personal data, or connection strings.

    line 30
  7. Distinctive:

    prefer presence, type, length, hash, status, or bounded identifiers.

    line 31
  8. Distinctive:

    remove temporary diagnostics unless they are justified as lasting observability.

    line 32
  9. Distinctive:

    If rejected, remove speculative changes and form a new hypothesis from the new evidence.

    line 40
  10. Distinctive:

    Do not stack several possible fixes into one experiment.

    line 41
  11. Distinctive:

    After two failed correction attempts against the same symptom, stop patching and reassess the reproduction, assumptions, architecture, and …

    line 42
  12. Distinctive:

    Ask the user only if a product decision, inaccessible environment, credential, or privileged action is required to continue.

    line 44
  13. Distinctive:

    Add the smallest useful regression test or reproduction harness when practical.

    line 48
  14. Distinctive:

    Run the focused reproduction and relevant surrounding checks.

    line 51
  15. Distinctive:

    If the cause is external or environmental, implement only the handling justified by evidence: a clear error, validated retry, timeout, …

    line 54
  16. Distinctive:

    Never report a fix when only the symptom disappeared once or when verification used a different environment without explaining the boundary.

    line 66

“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

Find and verify the cause before changing behavior. A plausible explanation is a hypothesis until evidence supports it.

1. Establish the failure

  • Read the complete useful error, stack trace, status code, and affected path.
  • Reproduce with the smallest reliable command or sequence.
  • Record whether the failure is deterministic, intermittent, environment-specific, or external.
  • Check current Git changes, relevant recent changes, configuration, dependency versions, and runtime assumptions.
  • Preserve unrelated user changes.

If reproduction is unsafe or requires production, stop before the effect and build the strongest safe diagnostic path.

2. Locate the failing boundary

Trace inputs and outputs across the shortest relevant path. Inspect the point where expected state first becomes incorrect.

Add temporary diagnostics only when existing evidence is insufficient. Diagnostics must be targeted and redacted:

  • never dump the full environment;
  • never print tokens, credentials, cookies, personal data, or connection strings;
  • prefer presence, type, length, hash, status, or bounded identifiers;
  • remove temporary diagnostics unless they are justified as lasting observability.

Compare with a working path in the same repository when available. Identify meaningful differences without assuming every difference is causal.

3. Test one hypothesis

State one falsifiable hypothesis and the evidence that would confirm or reject it. Test the smallest variable possible.

  • If rejected, remove speculative changes and form a new hypothesis from the new evidence.
  • Do not stack several possible fixes into one experiment.
  • After two failed correction attempts against the same symptom, stop patching and reassess the reproduction, assumptions, architecture, and external documentation.

Ask the user only if a product decision, inaccessible environment, credential, or privileged action is required to continue.

4. Implement the root-cause fix

  • Add the smallest useful regression test or reproduction harness when practical.
  • Make one coherent fix at the source of the faulty behavior.
  • Avoid opportunistic refactors.
  • Run the focused reproduction and relevant surrounding checks.
  • Inspect the final diff and remove temporary artifacts.

If the cause is external or environmental, implement only the handling justified by evidence: a clear error, validated retry, timeout, fallback, or observability. Do not disguise an unknown cause as fixed.

Completion evidence

Report:

  • the observed root cause;
  • the evidence that isolated it;
  • the correction;
  • the regression or reproduction result;
  • relevant checks and remaining uncertainty.

Never report a fix when only the symptom disappeared once or when verification used a different environment without explaining the boundary.

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.