agentleFS
Sign inStart free
rakaarwakyrakaarwaky/agents-arwaky/skills/code-quality/systematic-debugging/SKILL.md

systematic-debugging

Skill0 starsChanged 4 days agoMITView on GitHub

Systematic root-cause debugging in 7 step files (0–6) covering TypeScript/Node, Python, and Rust: read errors and build a red-capable feedback loop, trace data flow, analyze patterns, form ranked falsifiable hypotheses, implement one fix via TDD (RED-GREEN-REFACTOR), verify, and guard against rationalizations. Includes language-variant debugger/verify steps (3a/3b/3c, 5a/5b/5c). Observed phrasing: /systematic-debugging, fix this bug, test is failing, performance regression.

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

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 177 edited versions we read keep these.

  • 1

    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 only the winning lane's references.

    line 50

“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

For any technical issue — test failures, production bugs, build or performance problems — find the root cause before touching a fix. This file is the lane identification step only; the working method lives in references/hermes/ and references/grok/.

Global Rules

The lane is determined from a physical signal, not by self-identification. Each agent's launcher stamps a fixed attribution env var into every command it runs; the model cannot set these.

if [[ -n "${HERMES_AGENT:-}" ]]; then echo HERMES
elif [[ -n "${GROK_AGENT:-}" ]]; then echo GROK
else echo UNSET; fi
  • HERMES → Hermes lane. Read references/hermes/. Stop.
  • GROK → Grok lane. Read references/grok/. Stop.
  • UNSET → stop and report. Do not guess the lane.

Marker provenance:

  • Hermes re-exports AI_AGENT=hermes-agent and HERMES_AGENT=true on every command.
  • Grok Build runs with GROK_AGENT=1.

Read only the winning lane's references. Do not read the other lane's.

Routing

Hermes lane (Steps 0–3)

StepReferencePurpose
0references/hermes/step-0-plan-and-spawn.mdDetect lang; plan the brief (symptom, repro, phase, constraints, acceptance criteria); create the Orca worktree with --agent grok; drop /systematic-debugging <issue-or-error>
1references/hermes/step-1-wait-grok.mdWait for Grok to finish the pipeline; read its debug summary and commit
2references/hermes/step-2-verify-and-ship.mdVerify the iron law (regression test present), re-run the gate on the clean tree; commit/push/PR as appropriate
3references/hermes/step-3-final-report.mdFinal report to the user (root cause, fix, verification, open decisions, commit/PR, worktree teardown)

Grok lane (Steps 0–6, run by Grok Build)

StepReferencePurpose
0. Intakereferences/grok/step-0-intake.mdSymptom statement, target lang, repro command, run state; iron-law gate; invocation.
1. Investigatereferences/grok/step-1-investigate.mdRead errors, build a tight red-capable loop, check recent changes, gather multi-component evidence, trace data flow.
2. Patternreferences/grok/step-2-pattern.mdMinimize the repro, find working examples, compare against references, identify differences, understand dependencies.
3. Hypothesisreferences/grok/step-3-hypothesis.mdForm 3–5 ranked falsifiable hypotheses; test minimally, one variable at a time; pick the language debugger variant.
3a/3b/3c. Language debuggerstep-3a-python-debugger.md, step-3b-node-debugger.md, step-3c-rust-debugger.mdPython (pdb + debugpy), Node/TS (node inspect + CDP, profiles), Rust (dbg!, RUST_BACKTRACE, rust-gdb/lldb).
4. Implementreferences/grok/step-4-implement.mdTDD cycle: RED failing test, GREEN single fix, per-language verify, REFACTOR; rule of three; question architecture at 3+ failures.
5. Verify & finishreferences/grok/step-5-verify.mdVerification checklist, language cleanup pass (delegates to 5a/5b/5c), guardrails final pass, reporting format.
5a/5b/5c. Language verifystep-5a-python-verify.md, step-5b-node-verify.md, step-5c-rust-verify.mdPer-language verification + cleanup grep.
6. Guardrailsreferences/grok/step-6-guardrails.mdRed-flags list, common-rationalizations table, TDD rationale, TDD cycle detail; cross-cutting rules applied before reporting done.

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.

Check your setup, free