[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71SPTHRMQ1AMEEEF2C6KES3":3,"skill-same:01M71SPTHRMQ1AMEEEF2C6KES3":72},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":41,"repositoryHtmlUrl":42,"repositoryHomepageUrl":6,"ownerHtmlUrl":43,"ownerBlogUrl":6,"ownerDescription":44,"updatedAt":45,"install":46,"resources":59},"01M71SPTHRMQ1AMEEEF2C6KES3","diagnosing-bugs",null,"Diagnosis loop for hard bugs and performance regressions. Use when the user says \"diagnose\"\u002F\"debug this\", or reports something broken\u002Fthrowing\u002Ffailing\u002Fslow.","sanurb\u002F.dotfiles","sanurb","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F60886336?v=4","01M71SKJS17F5YPRE27CVMEH1X","Santiago U","01M71SKJSTGEXEQ82HMAQP4ZAF",2,"MIT","https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fdiagnosing-bugs\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32],"bugs","diagnosing bugs","broken","diagnose","for","performance","failing","use","loop","debug","when","reports","diagnosis","user","2026-11-10 13:02:02",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 `CONTEXT.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## 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 — try them 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) — drives the UI, 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 — 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 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** (paste the invocation and its output), 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 — 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 — 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.\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 + post-mortem\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\n**Then ask: what would have prevented this bug?** If the answer involves architectural change (no good test seam, tangled callers, hidden coupling) hand off to the `\u002Fimprove-codebase-architecture` skill with the specifics. Make the recommendation **after** the fix is in, not before — you have more information now than when you started.\n","# Diagnosing Bugs\n\nA discipline for hard bugs. Skip phases only when explicitly justified.\n\nWhen exploring the codebase, read `CONTEXT.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## 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 — try them 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) — drives the UI, 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 — 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 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** (paste the invocation and its output), 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 — 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 — 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.\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 + post-mortem\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\n**Then ask: what would have prevented this bug?** If the answer involves architectural change (no good test seam, tangled callers, hidden coupling) hand off to the `\u002Fimprove-codebase-architecture` skill with the specifics. Make the recommendation **after** the fix is in, not before — you have more information now than when you started.\n",{"name":5,"description":7},"main","5acbba39180bbea80204d1a58db40ccbd813a13a","config\u002Fpi\u002Fagent\u002Fskills\u002Fdiagnosing-bugs",0,"https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles","https:\u002F\u002Fgithub.com\u002Fsanurb","Building things","2026-11-08 21:22:35",{"source":8,"skillName":5,"commands":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add sanurb\u002F.dotfiles --skill diagnosing-bugs",{"tool":52,"command":53},"bunx","bunx skills add sanurb\u002F.dotfiles --skill diagnosing-bugs",{"tool":55,"command":56},"pnpm","pnpm dlx skills add sanurb\u002F.dotfiles --skill diagnosing-bugs","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fdiagnosing-bugs\u002FSKILL.md",true,[60,66],{"path":61,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":64,"sourceHtmlUrl":65},"agents\u002Fopenai.yaml","REFERENCE","参考文档",103,"https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fdiagnosing-bugs\u002Fagents\u002Fopenai.yaml",{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"scripts\u002Fhitl-loop.template.sh","SCRIPT","脚本",1164,"https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fdiagnosing-bugs\u002Fscripts\u002Fhitl-loop.template.sh",[73,99,127,155,172,199],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":89,"publishedAt":33},"01M71SS19VAM2YZ2SA7HD0PT2P","writing-great-skills","Reference for writing and editing skills well — the vocabulary and principles that make a skill predictable.","https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fwriting-great-skills\u002FSKILL.md",[79,84],{"categoryNo":80,"categoryName":81,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":85,"categoryName":86,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[90,91,92,23,93,94,95,96,97,98],"writing great skills","writing","principles","editing","a skill","true","reference","make","vocabulary",{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":108,"publishedAt":33},"01M71SRXKJN6RYXPN9GFRVYSR3","write-discoverable-code","Rules for writing code that coding agents (and humans) can find and understand through\nplain-text search. Apply whenever writing or renaming code: functions, types, constants,\nfiles, error messages, doc comments.\n\nGrounded in measurement: agents navigate by plain-text search, not by AST or\nlanguage server, so every identifier is a search query and every search miss\ncosts wasted reads.","https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fwrite-discoverable-code\u002FSKILL.md",[105],{"categoryNo":106,"categoryName":107,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000030","开发工程师",[109,110,111,112,113,114,115,116,23,117,118,119,120,121,91,122,123,124,125,126],"write discoverable code","code","discoverable code","write","measurement","error","types","error messages","search","messages","constants","plain-text","files","Rules","query","apply","comments","can",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":136,"publishedAt":33},"01M71SRT760335ES5WSEAAQDN2","wrangler","Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.","https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fwrangler\u002FSKILL.md",[133],{"categoryNo":134,"categoryName":135,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000010","后端与接口",[129,137,138,139,140,141,142,143,144,145,146,147,148,23,149,150,151,152,153,154],"cloudflare","d1","knowledge","workflows","cli","secrets","commands","syntax","cloudflare workers","r2","queues","running","containers","correct","load","workers","best practices","store",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":161,"publishedAt":33},"01M71SRPHE452V400HD2EZC4XG","workers-best-practices","Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge.","https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fworkers-best-practices\u002FSKILL.md",[],[162,152,153,163,164,165,23,137,166,139,167,129,91,151,29,110,168,169,170,171],"workers best practices","observability","reviews","promises","worker","new","common","streaming","state","anti-patterns",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":181,"publishedAt":33},"01M71SRJ36HWMTQ9ZMQ2RB008S","web-perf","Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.","https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fweb-perf\u002FSKILL.md",[178],{"categoryNo":179,"categoryName":180,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000023","Skill与智能体开发",[182,183,184,185,186,187,24,188,189,190,139,191,29,192,193,194,195,196,197,198],"web perf","perf","web","resources","vitals","chrome devtools","render","issues","devtools","MCP","caching","documentation","optimize","layout","audit","dependency","network",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":205,"publishedAt":33},"01M71SREWXFKFN6DM80YCCQZ46","wayfinder","Plan a huge chunk of work — more than one agent session can hold — as a shared map of decision tickets on your issue tracker, and resolve them one at a time until the way to the destination is clear.","https:\u002F\u002Fgithub.com\u002Fsanurb\u002F.dotfiles\u002Fblob\u002Fmain\u002Fconfig\u002Fpi\u002Fagent\u002Fskills\u002Fwayfinder\u002FSKILL.md",[],[201,206,207,208,209,210,211,95,212,213,214,215,126,216,217,218,219,220,221,222],"shared","as","chunk","time","session","decision","more","map","one","one at a time","agent session","work","plan","tracker","tickets","at","destination"]