[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CNWMYT2SAP1MWRQZCXKGZ1":3,"skill-same:01M6CNWMYT2SAP1MWRQZCXKGZ1":724},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":15,"sourceHtmlUrl":16,"categories":17,"keywords":29,"publishedAt":58,"linkOnly":59,"markdownContent":60,"markdownBody":61,"frontMatter":62,"refName":65,"commitSha":66,"skillDirectory":67,"forksCount":68,"repositoryHtmlUrl":69,"repositoryHomepageUrl":70,"ownerHtmlUrl":71,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":72,"install":73,"resources":86},"01M6CNWMYT2SAP1MWRQZCXKGZ1","archify",null,"Create validated architecture, workflow, sequence, data-flow, lifecycle\u002Fstate, entity-relationship, class\u002Finterface, tree, timeline, and execution waterfall diagrams as explorable standalone HTML with inline SVG, dark\u002Flight themes, optional motion, and image\u002FSVG\u002FWebM export. Accept plain-language requirements or Mermaid; inspect repository evidence for code-backed diagrams. Use when visualizing infrastructure, cloud\u002Fsecurity networks, API calls, data pipelines, ETL\u002FELT, state machines, database schemas, class contracts, module hierarchies, incident\u002Frelease histories, or request and agent-run timings. Also use for everyday subjects with steps, parts, relationships, or states: leave and travel plans, applications and approvals, renting conversations, money or document flows, and order progress. Not for numeric charts or dashboards.","Pranilash\u002FAttendance_App_v1","Pranilash","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F144217030?v=4","01M6CNPZWH85X4VYN2BAYGYJB8","01M6CNQ00RSRN75P38AMWGB4ZV",0,"MIT","https:\u002F\u002Fgithub.com\u002FPranilash\u002FAttendance_App_v1\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Farchify\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FPranilash\u002FAttendance_App_v1\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Farchify\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57],"api","when","inspect","standalone","conversations","interface","flows","states","themes","incident","relationship","diagrams","export","Charts","image","flow","repository","etl","elt","data pipelines","database","release","code","run","create","mermaid","architecture","workflow","2026-11-10 10:12:30",false,"---\nname: diagnosing-bugs\ndescription: Diagnosis loop for hard bugs and performance regressions. Use when the user says \"diagnose\"\u002F\"debug this\", or reports something broken\u002Fthrowing\u002Ffailing\u002Fslow.\n---\n\n# Diagnosing Bugs\n\nA discipline for hard bugs. Skip phases only when explicitly justified.\n\nWhen exploring the codebase, read `GLOSSARY.md` (if it exists) to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.\n\n## Redact\n\nThis skill has you show commands, outputs and captured artifacts. **Redact every secret first**: write `\u003CREDACTED>` in its place. Build loops against env vars, so the credential stays in the environment rather than in what you show. Captured artifacts carry auth headers: quote only the lines that carry the signal.\n\nIf the redacted output is not enough to diagnose the bug, say so and ask the user.\n\n## Phase 1: Build a feedback loop\n\n**This is the skill.** Everything else is mechanical. If you have a **tight** pass\u002Ffail signal for the bug (one that goes red on _this_ bug), you will find the cause; bisection, hypothesis-testing, and instrumentation all just consume it. If you don't have one, no amount of staring at code will save you.\n\nSpend disproportionate effort here. **Be aggressive. Be creative. Refuse to give up.**\n\n### Ways to construct one, in roughly this order\n\n1. **Failing test** at whatever seam reaches the bug: unit, integration, e2e.\n2. **Curl \u002F HTTP script** against a running dev server.\n3. **CLI invocation** with a fixture input, diffing stdout against a known-good snapshot.\n4. **Headless browser script** (Playwright \u002F Puppeteer) that drives the UI and asserts on DOM\u002Fconsole\u002Fnetwork.\n5. **Replay a captured trace.** Save a real network request \u002F payload \u002F event log to disk; replay it through the code path in isolation.\n6. **Throwaway harness.** Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.\n7. **Property \u002F fuzz loop.** If the bug is \"sometimes wrong output\", run 1000 random inputs and look for the failure mode.\n8. **Bisection harness.** If the bug appeared between two known states (commit, dataset, version), automate \"boot at state X, check, repeat\" so you can `git bisect run` it.\n9. **Differential loop.** Run the same input through old-version vs new-version (or two configs) and diff outputs.\n10. **HITL bash script.** Last resort. If a human must click, drive _them_ with `scripts\u002Fhitl-loop.template.sh` so the loop is still structured. Captured output feeds back to you.\n\nBuild the right feedback loop, and the bug is 90% fixed.\n\n### Tighten the loop\n\nTreat the loop as a product. Once you have _a_ loop, **tighten** it:\n\n- Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)\n- Can I make the signal sharper? (Assert on the specific symptom, not \"didn't crash\".)\n- Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)\n\nA 30-second flaky loop is barely better than no loop; a 2-second deterministic one is tight, a debugging superpower.\n\n### Non-deterministic bugs\n\nThe goal is not a clean repro but a **higher reproduction rate**. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not, so keep raising the rate until it's debuggable.\n\n### When you genuinely cannot build a loop\n\nStop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a redacted captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do **not** proceed to hypothesise without a loop.\n\n### Completion criterion: a tight loop that goes red\n\nPhase 1 is done when the loop is **tight** and **red-capable**: you can name **one command** (a script path, a test invocation, a curl) that you have **already run at least once** (show the invocation and its output, redacted), and that is:\n\n- [ ] **Red-capable**: it drives the actual bug code path and asserts the **user's exact symptom**, so it can go red on this bug and green once fixed. Not \"runs without erroring\"; it must be able to _catch this specific bug_.\n- [ ] **Deterministic**: same verdict every run (flaky bugs: a pinned, high reproduction rate, per above).\n- [ ] **Fast**: seconds, not minutes.\n- [ ] **Agent-runnable**: you can run it unattended; a human in the loop only via `scripts\u002Fhitl-loop.template.sh`.\n\nIf you catch yourself reading code to build a theory before this command exists, **stop: jumping straight to a hypothesis is the exact failure this skill prevents.** No red-capable command, no Phase 2.\n\n## Phase 2: Reproduce + minimise\n\nRun the loop. Watch it go red as the bug appears.\n\nConfirm:\n\n- [ ] The loop produces the failure mode the **user** described, not a different failure that happens to be nearby. Wrong bug = wrong fix.\n- [ ] The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).\n- [ ] You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.\n\n### Minimise\n\nOnce it's red, shrink the repro to the **smallest scenario that still goes red**. Cut inputs, callers, config, data, and steps **one at a time**, re-running the loop after each cut, and keep only what's load-bearing for the failure.\n\nWhy bother: a minimal repro shrinks the hypothesis space in Phase 3 (fewer moving parts left to suspect) and becomes the clean regression test in Phase 5.\n\nDone when **every remaining element is load-bearing**: removing any one of them makes the loop go green.\n\nDo not proceed until you have reproduced **and** minimised.\n\n## Phase 3: Hypothesise\n\nGenerate **3–5 ranked hypotheses** before testing any of them. Single-hypothesis generation anchors on the first plausible idea.\n\nEach hypothesis must be **falsifiable**: state the prediction it makes.\n\n> Format: \"If \u003CX> is the cause, then \u003Cchanging Y> will make the bug disappear \u002F \u003Cchanging Z> will make it worse.\"\n\nIf you cannot state the prediction, the hypothesis is a vibe: discard or sharpen it.\n\n**Show the ranked list to the user before testing.** They often have domain knowledge that re-ranks instantly (\"we just deployed a change to #3\"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it; proceed with your ranking if the user is AFK.\n\n## Phase 4: Instrument\n\nEach probe must map to a specific prediction from Phase 3. **Change one variable at a time.**\n\nTool preference:\n\n1. **Debugger \u002F REPL inspection** if the env supports it. One breakpoint beats ten logs.\n2. **Targeted logs** at the boundaries that distinguish hypotheses.\n3. Never \"log everything and grep\".\n\n**Tag every debug log** with a unique prefix, e.g. `[DEBUG-a4f2]`. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.\n\n**Perf branch.** For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, `performance.now()`, profiler, query plan), then bisect. Measure first, fix second.\n\n## Phase 5: Fix + regression test\n\nWrite the regression test **before the fix**, but only if there is a **correct seam** for it.\n\nA correct seam is one where the test exercises the **real bug pattern** as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.\n\n**If no correct seam exists, that itself is the finding.** Note it. The codebase architecture is preventing the bug from being locked down. Flag this for the next phase.\n\nIf a correct seam exists:\n\n1. Turn the minimised repro into a failing test at that seam.\n2. Watch it fail. If you forced the red by mutating code or a fixture, `diff` against a pristine copy to prove the mutation landed before you trust it.\n3. Apply the fix.\n4. Watch it pass.\n5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.\n\n## Phase 6: Cleanup\n\nRequired before declaring done:\n\n- [ ] Original repro no longer reproduces (re-run the Phase 1 loop)\n- [ ] Regression test passes (or absence of seam is documented)\n- [ ] All `[DEBUG-...]` instrumentation removed (`grep` the prefix)\n- [ ] Throwaway prototypes deleted (or moved to a clearly-marked debug location)\n- [ ] The hypothesis that turned out correct is stated in the commit \u002F PR message, so the next debugger learns\n","# Diagnosing Bugs\n\nA discipline for hard bugs. Skip phases only when explicitly justified.\n\nWhen exploring the codebase, read `GLOSSARY.md` (if it exists) to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.\n\n## Redact\n\nThis skill has you show commands, outputs and captured artifacts. **Redact every secret first**: write `\u003CREDACTED>` in its place. Build loops against env vars, so the credential stays in the environment rather than in what you show. Captured artifacts carry auth headers: quote only the lines that carry the signal.\n\nIf the redacted output is not enough to diagnose the bug, say so and ask the user.\n\n## Phase 1: Build a feedback loop\n\n**This is the skill.** Everything else is mechanical. If you have a **tight** pass\u002Ffail signal for the bug (one that goes red on _this_ bug), you will find the cause; bisection, hypothesis-testing, and instrumentation all just consume it. If you don't have one, no amount of staring at code will save you.\n\nSpend disproportionate effort here. **Be aggressive. Be creative. Refuse to give up.**\n\n### Ways to construct one, in roughly this order\n\n1. **Failing test** at whatever seam reaches the bug: unit, integration, e2e.\n2. **Curl \u002F HTTP script** against a running dev server.\n3. **CLI invocation** with a fixture input, diffing stdout against a known-good snapshot.\n4. **Headless browser script** (Playwright \u002F Puppeteer) that drives the UI and asserts on DOM\u002Fconsole\u002Fnetwork.\n5. **Replay a captured trace.** Save a real network request \u002F payload \u002F event log to disk; replay it through the code path in isolation.\n6. **Throwaway harness.** Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.\n7. **Property \u002F fuzz loop.** If the bug is \"sometimes wrong output\", run 1000 random inputs and look for the failure mode.\n8. **Bisection harness.** If the bug appeared between two known states (commit, dataset, version), automate \"boot at state X, check, repeat\" so you can `git bisect run` it.\n9. **Differential loop.** Run the same input through old-version vs new-version (or two configs) and diff outputs.\n10. **HITL bash script.** Last resort. If a human must click, drive _them_ with `scripts\u002Fhitl-loop.template.sh` so the loop is still structured. Captured output feeds back to you.\n\nBuild the right feedback loop, and the bug is 90% fixed.\n\n### Tighten the loop\n\nTreat the loop as a product. Once you have _a_ loop, **tighten** it:\n\n- Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)\n- Can I make the signal sharper? (Assert on the specific symptom, not \"didn't crash\".)\n- Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)\n\nA 30-second flaky loop is barely better than no loop; a 2-second deterministic one is tight, a debugging superpower.\n\n### Non-deterministic bugs\n\nThe goal is not a clean repro but a **higher reproduction rate**. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not, so keep raising the rate until it's debuggable.\n\n### When you genuinely cannot build a loop\n\nStop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a redacted captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do **not** proceed to hypothesise without a loop.\n\n### Completion criterion: a tight loop that goes red\n\nPhase 1 is done when the loop is **tight** and **red-capable**: you can name **one command** (a script path, a test invocation, a curl) that you have **already run at least once** (show the invocation and its output, redacted), and that is:\n\n- [ ] **Red-capable**: it drives the actual bug code path and asserts the **user's exact symptom**, so it can go red on this bug and green once fixed. Not \"runs without erroring\"; it must be able to _catch this specific bug_.\n- [ ] **Deterministic**: same verdict every run (flaky bugs: a pinned, high reproduction rate, per above).\n- [ ] **Fast**: seconds, not minutes.\n- [ ] **Agent-runnable**: you can run it unattended; a human in the loop only via `scripts\u002Fhitl-loop.template.sh`.\n\nIf you catch yourself reading code to build a theory before this command exists, **stop: jumping straight to a hypothesis is the exact failure this skill prevents.** No red-capable command, no Phase 2.\n\n## Phase 2: Reproduce + minimise\n\nRun the loop. Watch it go red as the bug appears.\n\nConfirm:\n\n- [ ] The loop produces the failure mode the **user** described, not a different failure that happens to be nearby. Wrong bug = wrong fix.\n- [ ] The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).\n- [ ] You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.\n\n### Minimise\n\nOnce it's red, shrink the repro to the **smallest scenario that still goes red**. Cut inputs, callers, config, data, and steps **one at a time**, re-running the loop after each cut, and keep only what's load-bearing for the failure.\n\nWhy bother: a minimal repro shrinks the hypothesis space in Phase 3 (fewer moving parts left to suspect) and becomes the clean regression test in Phase 5.\n\nDone when **every remaining element is load-bearing**: removing any one of them makes the loop go green.\n\nDo not proceed until you have reproduced **and** minimised.\n\n## Phase 3: Hypothesise\n\nGenerate **3–5 ranked hypotheses** before testing any of them. Single-hypothesis generation anchors on the first plausible idea.\n\nEach hypothesis must be **falsifiable**: state the prediction it makes.\n\n> Format: \"If \u003CX> is the cause, then \u003Cchanging Y> will make the bug disappear \u002F \u003Cchanging Z> will make it worse.\"\n\nIf you cannot state the prediction, the hypothesis is a vibe: discard or sharpen it.\n\n**Show the ranked list to the user before testing.** They often have domain knowledge that re-ranks instantly (\"we just deployed a change to #3\"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it; proceed with your ranking if the user is AFK.\n\n## Phase 4: Instrument\n\nEach probe must map to a specific prediction from Phase 3. **Change one variable at a time.**\n\nTool preference:\n\n1. **Debugger \u002F REPL inspection** if the env supports it. One breakpoint beats ten logs.\n2. **Targeted logs** at the boundaries that distinguish hypotheses.\n3. Never \"log everything and grep\".\n\n**Tag every debug log** with a unique prefix, e.g. `[DEBUG-a4f2]`. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.\n\n**Perf branch.** For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, `performance.now()`, profiler, query plan), then bisect. Measure first, fix second.\n\n## Phase 5: Fix + regression test\n\nWrite the regression test **before the fix**, but only if there is a **correct seam** for it.\n\nA correct seam is one where the test exercises the **real bug pattern** as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.\n\n**If no correct seam exists, that itself is the finding.** Note it. The codebase architecture is preventing the bug from being locked down. Flag this for the next phase.\n\nIf a correct seam exists:\n\n1. Turn the minimised repro into a failing test at that seam.\n2. Watch it fail. If you forced the red by mutating code or a fixture, `diff` against a pristine copy to prove the mutation landed before you trust it.\n3. Apply the fix.\n4. Watch it pass.\n5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.\n\n## Phase 6: Cleanup\n\nRequired before declaring done:\n\n- [ ] Original repro no longer reproduces (re-run the Phase 1 loop)\n- [ ] Regression test passes (or absence of seam is documented)\n- [ ] All `[DEBUG-...]` instrumentation removed (`grep` the prefix)\n- [ ] Throwaway prototypes deleted (or moved to a clearly-marked debug location)\n- [ ] The hypothesis that turned out correct is stated in the commit \u002F PR message, so the next debugger learns\n",{"name":63,"description":64},"diagnosing-bugs","Diagnosis loop for hard bugs and performance regressions. 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