[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CD2PY33WED8A7KTF1PBBEM":3,"skill-same:01M6CD2PY33WED8A7KTF1PBBEM":75},{"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":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M6CD2PY33WED8A7KTF1PBBEM","python-performance-optimization",null,"Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.","cercova-studios\u002F10x-swe","cercova-studios","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F217108198?v=4","01M6CCYGHXTH80N3BXRW6KESTE","Cercova Studios","01M6CCYGPADH5MJDR0VNNR775C",1,"MIT","https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002F10x-swe\u002Fskills\u002Fpython-performance-optimization\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"performance","python performance","Optimization","performance optimization","python","python performance optimization","profile","optimize python","when","SWE","cProfile","python code","application","optimize","use","10x","best practices","code","debugging","plugin","memory","2026-11-10 10:12:30",false,"---\nname: python-performance-optimization\ndescription: Profile and optimize Python code using cProfile, memory profilers, and\n  performance best practices. Use when debugging slow Python code, optimizing bottlenecks,\n  or improving application performance.\nmetadata:\n  skill-graph-id: plugin\u002F10x-swe\u002Fpython-performance-optimization\n  skill-graph-uses: ''\n  skill-graph-used-by: ''\n---\n\n# Python Performance Optimization\n\nUse this skill to find and reduce measured Python bottlenecks. Preserve behavior and compare representative workloads before claiming improvement.\n\n## Workflow\n\n1. Define the performance problem and target: latency distribution, throughput, peak memory, CPU use, or I\u002FO wait. Record runtime, hardware, input size, and workload characteristics.\n2. Establish a repeatable baseline. Separate initialization, steady-state work, and cache effects. Use `timeit` for small operations and appropriate end-to-end measurements for user-visible performance.\n3. Locate the bottleneck: `cProfile` for call costs, line profiling for a confirmed hot function, `tracemalloc` for allocation changes, and sampling tools for running processes. Inspect production only within authorized scope.\n4. Improve algorithms and data layout before micro-optimizing. Consider indexes and batching for databases, streaming for large inputs, bounded caches for repeated work, and vectorization for suitable numeric workloads.\n5. Choose concurrency for the bottleneck. Async helps overlapping I\u002FO; processes can parallelize CPU work but add serialization and startup costs. Threads benefit blocking I\u002FO and workloads that release the GIL.\n6. Change one hypothesis at a time. Re-run correctness checks and identical benchmarks. Report absolute results and variance, not only a speedup ratio.\n7. Check tradeoffs: memory, cache invalidation, contention, startup time, transaction semantics, and maintainability. Keep the simpler implementation when the gain is not material.\n\n## Reference routing\n\nOpen the relevant section of [detailed patterns](references\u002Fpatterns.md):\n\n- Profiling Tools: CPU, line, memory, and sampling examples.\n- Optimization Patterns: collections, generators, strings, lookups, and call overhead.\n- Advanced Optimization: NumPy, caching, slots, processes, and async I\u002FO.\n- Database Optimization and Memory Optimization: batching, query plans, allocation snapshots, streaming, and weak caches.\n- Benchmarking Tools: timing and pytest-benchmark examples.\n\nPreserved examples are experiments, not performance guarantees. `map` is not always faster than a comprehension; NumPy includes allocation costs and fixed-width arithmetic semantics. `sys.getsizeof()` does not measure referenced objects or total retained memory. The leak sketch drops its local references on return and does not demonstrate a retained leak. Validate these claims against the actual workload.\n\n## Acceptance\n\nProvide the baseline, final measurement, relevant environment, correctness results, and remaining limits. Do not introduce caches, dependencies, schema changes, or production profiling permissions merely because they appear in an example.\n","# Python Performance Optimization\n\nUse this skill to find and reduce measured Python bottlenecks. Preserve behavior and compare representative workloads before claiming improvement.\n\n## Workflow\n\n1. Define the performance problem and target: latency distribution, throughput, peak memory, CPU use, or I\u002FO wait. Record runtime, hardware, input size, and workload characteristics.\n2. Establish a repeatable baseline. Separate initialization, steady-state work, and cache effects. Use `timeit` for small operations and appropriate end-to-end measurements for user-visible performance.\n3. Locate the bottleneck: `cProfile` for call costs, line profiling for a confirmed hot function, `tracemalloc` for allocation changes, and sampling tools for running processes. Inspect production only within authorized scope.\n4. Improve algorithms and data layout before micro-optimizing. Consider indexes and batching for databases, streaming for large inputs, bounded caches for repeated work, and vectorization for suitable numeric workloads.\n5. Choose concurrency for the bottleneck. Async helps overlapping I\u002FO; processes can parallelize CPU work but add serialization and startup costs. Threads benefit blocking I\u002FO and workloads that release the GIL.\n6. Change one hypothesis at a time. Re-run correctness checks and identical benchmarks. Report absolute results and variance, not only a speedup ratio.\n7. Check tradeoffs: memory, cache invalidation, contention, startup time, transaction semantics, and maintainability. Keep the simpler implementation when the gain is not material.\n\n## Reference routing\n\nOpen the relevant section of [detailed patterns](references\u002Fpatterns.md):\n\n- Profiling Tools: CPU, line, memory, and sampling examples.\n- Optimization Patterns: collections, generators, strings, lookups, and call overhead.\n- Advanced Optimization: NumPy, caching, slots, processes, and async I\u002FO.\n- Database Optimization and Memory Optimization: batching, query plans, allocation snapshots, streaming, and weak caches.\n- Benchmarking Tools: timing and pytest-benchmark examples.\n\nPreserved examples are experiments, not performance guarantees. `map` is not always faster than a comprehension; NumPy includes allocation costs and fixed-width arithmetic semantics. `sys.getsizeof()` does not measure referenced objects or total retained memory. The leak sketch drops its local references on return and does not demonstrate a retained leak. Validate these claims against the actual workload.\n\n## Acceptance\n\nProvide the baseline, final measurement, relevant environment, correctness results, and remaining limits. Do not introduce caches, dependencies, schema changes, or production profiling permissions merely because they appear in an example.\n",{"name":5,"metadata":45,"description":7},{"skill-graph-id":46,"skill-graph-uses":47,"skill-graph-used-by":47},"plugin\u002F10x-swe\u002Fpython-performance-optimization","","main","12441d84a8a292fcc94f47bc98877cbefd9b52e2","plugins\u002F10x-swe\u002Fskills\u002Fpython-performance-optimization",0,"https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe","https:\u002F\u002Fgithub.com\u002Fcercova-studios","2026-11-12 07:39:44",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add cercova-studios\u002F10x-swe --skill python-performance-optimization",{"tool":61,"command":62},"bunx","bunx skills add cercova-studios\u002F10x-swe --skill python-performance-optimization",{"tool":64,"command":65},"pnpm","pnpm dlx skills add cercova-studios\u002F10x-swe --skill python-performance-optimization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002F10x-swe\u002Fskills\u002Fpython-performance-optimization\u002FSKILL.md",true,[69],{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Fpatterns.md","REFERENCE","参考文档",21263,"https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002F10x-swe\u002Fskills\u002Fpython-performance-optimization\u002Freferences\u002Fpatterns.md",[76,114,150,170,188,200],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":80,"licenseFileUrl":81,"sourceHtmlUrl":82,"categories":83,"keywords":89,"publishedAt":40},"01M6CD3T6EQ9N5KPNXCM6ABHVA","turnstile-spin","Set up Cloudflare Turnstile end-to-end in a project. Scan the codebase, create the widget via the Cloudflare API, embed it where user requests need bot verification (form submissions, SPA actions, API endpoints, download links, comment or vote submissions, etc.), wire canonical server-side siteverify in the customer's existing backend, validate, and persist the skill. Load this when a user asks to add Turnstile, set up CAPTCHA, protect a form or endpoint from bots, or fix a Turnstile integration. Uses Cloudflare's documented Turnstile contract.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002F10x-swe\u002Fskills\u002Fturnstile-spin\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002F10x-swe\u002Fskills\u002Fturnstile-spin\u002FSKILL.md",[84],{"categoryNo":85,"categoryName":86,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[90,91,92,93,94,95,96,97,98,99,100,101,102,103,38,104,105,106,107,108,109,110,111,112,113,28,27],"turnstile spin","SET","nextjs","actions","up","fix","cloudflare","bot","api","permissions","create","astro","server","verification","endpoints","customer","backend","sveltekit","integration","app","bots","spa","project","end",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":80,"licenseFileUrl":118,"sourceHtmlUrl":119,"categories":120,"keywords":132,"publishedAt":40},"01M6CD0BJYRNV610HYYPX933RE","frontend-design","Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.","https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002F10x-swe\u002Fskills\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002F10x-swe\u002Fskills\u002Ffrontend-design\u002FSKILL.md",[121,124,127],{"categoryNo":122,"categoryName":123,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":125,"categoryName":126,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":128,"categoryName":129,"taxonomyType":130,"taxonomyTypeDesc":131},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[133,134,135,100,136,137,138,139,140,141,33,36,142,143,144,145,146,27,147,148,149],"frontend design","frontend","design","web","quality","applications","creative","web components","Production","components","user","generic","aesthetics","pages","with","high","build",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":156,"publishedAt":40},"01M6CD4QX8C4DST98AY21HEYCM","source-insights","Prepare inspected source evidence, preserve exact citations, and publish reviewed insights into an explicit local library.","https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002Fknowledge\u002Fskills\u002Fsource-insights\u002FSKILL.md",[],[157,158,159,160,161,162,163,164,165,166,167,168,169],"source insights","insights","source","publish","Citations","evidence","library","workflows","prepare","session","global","local","preserve",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":179,"publishedAt":40},"01M6CD4N1HVNC1EXPEBZT7YS31","skill-graph","Discover skills and workflows through independent lexical search, relationship paths and a terminal graph.","https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002Fknowledge\u002Fskills\u002Fskill-graph\u002FSKILL.md",[176],{"categoryNo":177,"categoryName":178,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000023","Skill与智能体开发",[180,181,38,164,182,34,183,167,184,185,186,28,187],"graph","skill graph","terminal","search","discover skills","discover","paths","relationship",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":194,"publishedAt":40},"01M6CD4HQKXNZ57YEGD8QVK6Z6","session-search","Recall public session turns with exact provenance, bounded excerpts and independent piped exports.","https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002Fknowledge\u002Fskills\u002Fsession-search\u002FSKILL.md",[],[183,166,195,196,164,197,198,167,147,158,159,199],"session search","public","provenance","recall","bounded",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":212,"publishedAt":40},"01M6CD4ET7DVNBYPDAMAYMSK2T","newsletter","Produce a private local AI newsletter from bounded public research, with cited deep dives, HTML\u002FMarkdown output, Dagu scheduling and seven-day retention.","https:\u002F\u002Fgithub.com\u002Fcercova-studios\u002F10x-swe\u002Fblob\u002Fmain\u002Fplugins\u002Fknowledge\u002Fskills\u002Fnewsletter\u002FSKILL.md",[206,209],{"categoryNo":207,"categoryName":208,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":210,"categoryName":211,"taxonomyType":87,"taxonomyTypeDesc":88},"01JBSEEDCAT000000000000021","资料检索与研究",[202,213,214,215,216,196,164,217,218,219,220,167,147,221,222,168,199,223],"local ai","dagu","produce","research","markdown","scheduling","html","daily","deep","retention","output"]