systematic-debugging
Skill1 starsChanged 21 days agoMITView on GitHub
Diagnose a defect, failing test, or regression before fixing it. Use whenever behaviour differs from expectation. Requires reproduction, evidence, one tested root-cause hypothesis at a time, and a fix driven by a failing test. After three failed hypotheses, stop and question the design with the user.
190 other projects use a version of this.
Adopted by
Most recently taken up by
- DemonDamon/AgenticXtheir own version, first seen Nov 9, 2026
- chrysogonus/power-agentstheir own version, first seen Nov 8, 2026
- willy92wins/DayZ-Modding-Knowledge-Packtheir own version, first seen Nov 8, 2026
- roundpilot/superpowers-antigravitytheir own version, first seen Nov 8, 2026
- REPOZY/superpowers-optimizedtheir own version, first seen Nov 8, 2026
- lingyired/status-triotheir own version, first seen Nov 8, 2026
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.
Leodorareluctant259Skillchinese-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
Leodorareluctant259Skillchinese-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
obraSkilldiagnosing-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
mjunaidcaSkillbuilding-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
mjunaidcaSkillbuilding-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
Other versions190 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 190
- obra/superpowers★ 314ksame name
- NousResearch/hermes-agent★ 262ksame name
- open-metadata/OpenMetadata★ 16ksame name
- jnMetaCode/superpowers-zh★ 8.4ksame name
- vudovn/ag-kit★ 8.2ksame name
- vudovn/antigravity-kit★ 8.2ksame name
- openai/plugins★ 7.8ksame name
- ChrisWiles/claude-code-showcase★ 6.1ksame name
- farm-fe/farm★ 5.6ksame name
- microsoft/FluidFramework★ 4.9ksame name
180 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository10 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- brainstormingSkill
- development-workflowSkill
- exploringSkill
- finishing-developmentSkill
- requesting-reviewSkill
- test-driven-developmentSkill
- writing-plansSkill
- writing-skillsSkill
The file
We split this skill into the rules it gives an agent
8 rules. 0 are common, 7 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 177 edited versions we read keep these.
7
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 8
Run this as the implementer.
- Distinctive: line 12
No fix without a reproduction, evidence, and a tested root cause.
- Distinctive: line 21
Read the full error, stack trace, and nearby logs.
- Distinctive: line 25
If it will not reproduce, add a temporary diagnostic and read it back.
- Distinctive: line 32
Use it to locate one causal difference.
- Distinctive: line 41
Write it down: "The cause is X, because evidence Y predicts Z."
- Distinctive: line 42
Run the smallest experiment that changes one variable.
- Distinctive: line 43
If it is disproved, revert the experiment and form a new hypothesis.
“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.
Files
Systematic Debugging
Run this as the implementer. Send an
explorer when you cannot find where a behaviour
lives or what a locked dependency really does.
No fix without a reproduction, evidence, and a tested root cause. Do not edit production code while guessing. Guards, retries, delays, and several speculative changes at once are guessing.
1. Reproduce
- State the expected and the observed behaviour exactly.
- Reproduce with the smallest reliable command. Record inputs, command, output, and how often it happens.
- Read the full error, stack trace, and nearby logs.
- Trace the bad value back to where it first goes wrong, not where it shows. At a boundary - process, service, database, queue - check both sides.
If it will not reproduce, add a temporary diagnostic and read it back. Do not fix a theory.
2. Compare
Find the closest working example in the project and list every difference - configuration, inputs, lifecycle order, state, transaction boundaries, timing. Use it to locate one causal difference.
Common hiding places: side effects in callbacks, work enqueued inside an open transaction, a query returning empty instead of nothing, repeated per-item queries, an input filter dropping a key, an unapplied migration, a cache that outlives its data.
3. Test one hypothesis
- Write it down: "The cause is X, because evidence Y predicts Z."
- Run the smallest experiment that changes one variable.
- If it is disproved, revert the experiment and form a new hypothesis. Never stack changes.
After three failed hypotheses, stop. Present the evidence and question the design or the assumptions with the user.
4. Fix through a failing test
- Unplanned bug: the diagnosis is evidence for a plan, not permission to
edit. Go through
brainstormingand an approved plan, then fix it as a unit. - Inside an approved unit: continue.
Under test-driven-development: write a
test that reproduces the root cause and watch it fail, make the smallest causal
fix, remove temporary diagnostics, then run the full specs and lint once and
re-run the original reproduction. Fix the source of the bad state, not only the
symptom.
Review findings are evidence, not orders
Reproduce a behavioural finding before you fix it. Check a quality finding
against the code and the project's rules. If fresh output shows a finding is
wrong, report it rejected with that output. A wrong fix is worse than the
finding.
Done when
The failure reproduced (or the limit is recorded), evidence supports the root cause, a regression test failed before the fix and passes after, the final specs and lint run passed, and the report explains why the fix addresses the cause.
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
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.

