[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6RAE7BMHJQJRMP79E5AGTNK":3,"skill-same:01M6RAE7BMHJQJRMP79E5AGTNK":70},{"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":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":40,"repositoryHtmlUrl":41,"repositoryHomepageUrl":6,"ownerHtmlUrl":42,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":43,"install":44,"resources":57},"01M6RAE7BMHJQJRMP79E5AGTNK","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.","HNUST-Dust\u002FDust_infantry_vision","HNUST-Dust","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F242605597?v=4","01M6RAD8QP9GXRZWD7WEN7KXWZ","01M6RAD8XNF76XNGYHDPX4AV12",5,"MIT","https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fdiagnosing-bugs\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31],"diagnosing bugs","bugs","performance","debug","diagnose","loop","broken","user","use","reports","diagnosis","for","when","failing","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 `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## 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 — 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 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 — 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## 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 — 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 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 — 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","1ce4eeca9e26627f8e445d56f5abf69f372eb3dd",".codex\u002Fskills\u002Fdiagnosing-bugs",1,"https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision","https:\u002F\u002Fgithub.com\u002FHNUST-Dust","2026-11-12 13:36:09",{"source":8,"skillName":5,"commands":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add HNUST-Dust\u002FDust_infantry_vision --skill diagnosing-bugs",{"tool":50,"command":51},"bunx","bunx skills add HNUST-Dust\u002FDust_infantry_vision --skill diagnosing-bugs",{"tool":53,"command":54},"pnpm","pnpm dlx skills add HNUST-Dust\u002FDust_infantry_vision --skill diagnosing-bugs","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fdiagnosing-bugs\u002FSKILL.md",true,[58,64],{"path":59,"resourceType":60,"resourceTypeDesc":61,"sizeBytes":62,"sourceHtmlUrl":63},"agents\u002Fopenai.yaml","REFERENCE","参考文档",198,"https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fdiagnosing-bugs\u002Fagents\u002Fopenai.yaml",{"path":65,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":68,"sourceHtmlUrl":69},"scripts\u002Fhitl-loop.template.sh","SCRIPT","脚本",1319,"https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fdiagnosing-bugs\u002Fscripts\u002Fhitl-loop.template.sh",[71,96,123,141,158,180],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":77,"publishedAt":32},"01M6RAEVD39W5QTD1BTNMR8XK3","replay-auto-aim","Reproduce and isolate auto-aim detection, tracking, solving, aiming, and planning issues from recorded video in this repository. Use for false positives, missed armors, tracker state changes, aim jitter, fire decisions, or latency regressions.","https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Freplay-auto-aim\u002FSKILL.md",[],[78,79,80,81,82,83,84,85,86,87,88,89,90,26,91,92,93,94,29,95],"auto","replay","replay auto aim","planning","state","issues","repository","detection","Video","reproduce","jitter","decisions","tracking","tracker","missed","latency","false","changes",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":107,"publishedAt":32},"01M6RAER0Q716PFNKAPVXX7AVC","improve-codebase-architecture","Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.","https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fimprove-codebase-architecture\u002FSKILL.md",[102],{"categoryNo":103,"categoryName":104,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[108,109,110,111,112,113,114,115,116,117,118,119,120,121,29,122],"improve codebase architecture","architecture","improve","codebase","report","html report","pick","html","grill","scan","visual","present","as","you","deepening",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":129,"publishedAt":32},"01M6RAEE88RSKY2HX0WVN67WJN","grill-with-docs","A relentless interview to sharpen a plan or design, which also creates docs (ADR's and glossary) as we go.","https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fgrill-with-docs\u002FSKILL.md",[],[130,131,116,132,133,134,135,136,137,138,139,120,140],"docs","grill with docs","with","design","go","plan","sharpen","adr","glossary","interview","which",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":147,"publishedAt":32},"01M6RAE1HQRFJ1PQFHK3YX0AKH","diagnose-gimbal-link","Safely diagnose this repository's gimbal serial link, packet framing, CRC, timing, quaternion interpolation, and reconnect behavior. Use for \u002Fdev\u002Fgimbal failures, stale state, CRC errors, dropped packets, or incorrect control data.","https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fdiagnose-gimbal-link\u002FSKILL.md",[],[22,148,149,150,82,84,151,152,26,153,154,155,156,29,157],"link","diagnose gimbal link","dev","data","behavior","stale","control","timing","errors","interpolation",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":164,"publishedAt":32},"01M6RADMNKPPTZS63V1W0EJG1S","diagnose-camera-stack","Diagnose HikRobot, MindVision, or USB camera discovery, SDK, streaming, frame, and frame-rate failures in this repository. Use when camera initialization fails, frames are empty, FPS is wrong, or exposure\u002Fgain\u002FVID-PID settings look suspect.","https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fdiagnose-camera-stack\u002FSKILL.md",[],[22,165,166,167,168,169,84,170,171,26,172,173,174,175,176,177,178,179,30],"stack","camera","diagnose camera stack","sdk","streaming","discovery","settings","look","empty","PID","usb","initialization","frame","Wrong","rate",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":194,"publishedAt":32},"01M6RADFJ916YTFCPT88J9DDN9","codebase-design","Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.","https:\u002F\u002Fgithub.com\u002FHNUST-Dust\u002FDust_infantry_vision\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fcodebase-design\u002FSKILL.md",[186,189],{"categoryNo":187,"categoryName":188,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":190,"categoryName":191,"taxonomyType":192,"taxonomyTypeDesc":193},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[133,195,111,196,110,197,198,25,26,199,200,201,202,203,204,205,29,122,206,30],"codebase design","code","find","decide","modules","another skill","shared","deep","module","interface","more","seam"]