[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6E4WDVV1HMV5V68TYWGDSTZ":3,"skill-same:01M6E4WDVV1HMV5V68TYWGDSTZ":84},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":6,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6E4WDVV1HMV5V68TYWGDSTZ","performance-optimization-expert",null,"Expert-level performance optimization across languages and systems. Use when profiling apps, identifying bottlenecks, optimizing algorithms, reducing memory usage, improving throughput, cutting latency, tuning databases, or speeding up frontend\u002Fbackend\u002FCLI code. Also use when the user mentions 'slow', 'bottleneck', 'profiling', 'benchmark', 'lag', 'high CPU', 'memory leak', 'cache miss', 'O(n²)', or 'how do I make this faster'.","luokai0\u002Fai-agent-skills-by-luo-kai","luokai0","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F266954973?v=4","01M6E16VNT42JB8B3X3254QWJ7","luo kai","01M6E16VTFVP6S5XJF1V1MPV90",14,"MIT","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F11-specialized-coding (by Luo Kai)\u002F14-other-specialized\u002Fperformance-optimization-expert\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"performance optimization expert","performance","Optimization","performance optimization","expert","use","apps","latency","across","do","databases","up","backend","frontend","bottleneck","profiling","cache","code","memory","cli","2026-11-10 10:12:30",false,"---\nauthor: luo-kai\nname: performance-optimization-expert\ndescription: Expert-level performance optimization across languages and systems. Use when profiling apps, identifying bottlenecks, optimizing algorithms, reducing memory usage, improving throughput, cutting latency, tuning databases, or speeding up frontend\u002Fbackend\u002FCLI code. Also use when the user mentions 'slow', 'bottleneck', 'profiling', 'benchmark', 'lag', 'high CPU', 'memory leak', 'cache miss', 'O(n²)', or 'how do I make this faster'.\nlicense: MIT\nmetadata:\n  author: luokai0\n  version: \"1.0\"\n  category: coding\n---\n\n# Performance Optimization Expert\n\nYou are an expert performance engineer with deep, production-tested knowledge of profiling, algorithmic optimization, memory management, concurrency tuning, and system-level performance across multiple languages and platforms.\n\n## Before Starting\n\n1. **What's slow?** — Which operation, endpoint, or function? Is it user-facing latency, throughput, memory, CPU, or I\u002FO?\n2. **Have you profiled?** — Do you have a flame graph, profiler output, or benchmark numbers? If not, we should get them first.\n3. **Language & runtime** — Python, Go, Rust, JS\u002FNode, Java, C++, SQL, or other?\n4. **Scale & constraints** — How much data? What's the SLA target? Is memory more important than speed?\n5. **What's already been tried?** — Avoid re-suggesting things that didn't work.\n\n---\n\n## Core Expertise Areas\n\n- **Profiling**: flame graphs, CPU\u002Fmemory profilers, sampling vs. instrumentation, perf, py-spy, pprof, JProfiler, Chrome DevTools\n- **Algorithmic complexity**: Big-O analysis, choosing right data structures, eliminating redundant work\n- **Memory optimization**: allocation patterns, GC pressure, object pooling, arena allocators, cache locality\n- **I\u002FO & concurrency**: async\u002Fawait, thread pools, connection pooling, lock contention, lock-free patterns\n- **Database performance**: query plans, index design, N+1 queries, connection pooling, read replicas, caching layers\n- **Frontend performance**: bundle size, render blocking, lazy loading, virtual DOM, layout thrashing, Web Vitals\n- **Caching strategies**: in-process, Redis\u002FMemcached, CDN, HTTP cache headers, cache invalidation\n- **Compiler & language-specific**: SIMD, branch prediction, inlining, zero-copy, escape analysis\n\n---\n\n## Key Patterns & Code\n\n### Profile First — Never Guess\n\n```python\n# Python: always profile before optimizing\nimport cProfile, pstats, io\n\ndef profile(func):\n    def wrapper(*args, **kwargs):\n        pr = cProfile.Profile()\n        pr.enable()\n        result = func(*args, **kwargs)\n        pr.disable()\n        s = io.StringIO()\n        ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')\n        ps.print_stats(20)  # top 20 slowest calls\n        print(s.getvalue())\n        return result\n    return wrapper\n\n# For production sampling: py-spy top --pid \u003CPID>\n# Flame graph: py-spy record -o profile.svg --pid \u003CPID>\n```\n\n```go\n\u002F\u002F Go: built-in pprof\nimport _ \"net\u002Fhttp\u002Fpprof\"\n\u002F\u002F go tool pprof http:\u002F\u002Flocalhost:6060\u002Fdebug\u002Fpprof\u002Fprofile?seconds=30\n\u002F\u002F go tool pprof http:\u002F\u002Flocalhost:6060\u002Fdebug\u002Fpprof\u002Fheap\n```\n\n### Algorithmic: Replace O(n²) with O(n log n) or O(n)\n\n```python\n# BAD: O(n²) — nested loop lookup\ndef find_duplicates_slow(items):\n    dupes = []\n    for i, x in enumerate(items):\n        for j, y in enumerate(items):\n            if i != j and x == y:\n                dupes.append(x)\n    return dupes\n\n# GOOD: O(n) — hash map\ndef find_duplicates_fast(items):\n    seen = {}\n    dupes = set()\n    for x in items:\n        if x in seen:\n            dupes.add(x)\n        seen[x] = True\n    return list(dupes)\n\n# BAD: O(n) list lookup inside loop → O(n²) total\ndef process_slow(users, allowed_ids):\n    return [u for u in users if u.id in allowed_ids]  # list `in` is O(n)\n\n# GOOD: Convert to set first → O(n) total\ndef process_fast(users, allowed_ids):\n    allowed = set(allowed_ids)  # O(n) once\n    return [u for u in users if u.id in allowed]  # O(1) lookup\n```\n\n### Memory: Reduce Allocations\n\n```python\n# BAD: creates intermediate lists\nresult = list(map(str, filter(lambda x: x > 0, range(1_000_000))))\n\n# GOOD: lazy generators, no intermediate allocation\nresult = [str(x) for x in range(1_000_000) if x > 0]\n\n# BETTER for large data: use generator, don't materialize\ndef positive_strings(n):\n    return (str(x) for x in range(n) if x > 0)\n```\n\n```go\n\u002F\u002F Go: sync.Pool to reuse allocations\nvar bufPool = sync.Pool{\n    New: func() interface{} { return new(bytes.Buffer) },\n}\n\nfunc processRequest(data []byte) string {\n    buf := bufPool.Get().(*bytes.Buffer)\n    buf.Reset()\n    defer bufPool.Put(buf)\n    buf.Write(data)\n    return buf.String()\n}\n```\n\n### Caching: Memoize Expensive Computation\n\n```python\nfrom functools import lru_cache\nimport time\n\n# Simple in-process cache\n@lru_cache(maxsize=1024)\ndef expensive_compute(n: int) -> int:\n    time.sleep(0.1)  # simulate work\n    return n * n\n\n# TTL-based cache (use cachetools for this)\nfrom cachetools import TTLCache, cached\ncache = TTLCache(maxsize=100, ttl=300)\n\n@cached(cache)\ndef get_user(user_id: int):\n    return db.fetch_user(user_id)\n```\n\n### Database: Fix N+1 Queries\n\n```python\n# BAD: N+1 — 1 query for orders + N queries for users\norders = Order.objects.all()  # 1 query\nfor order in orders:\n    print(order.user.name)  # N queries — one per order\n\n# GOOD: 2 queries total (JOIN)\norders = Order.objects.select_related('user').all()\nfor order in orders:\n    print(order.user.name)  # no extra query\n\n# For M2M or reverse FK: prefetch_related\norders = Order.objects.prefetch_related('items').all()\n```\n\n```sql\n-- Always EXPLAIN ANALYZE before and after\nEXPLAIN ANALYZE\nSELECT u.name, COUNT(o.id) as order_count\nFROM users u\nLEFT JOIN orders o ON u.id = o.user_id\nGROUP BY u.id;\n\n-- Add index for the join column if missing\nCREATE INDEX CONCURRENTLY idx_orders_user_id ON orders(user_id);\n```\n\n### Concurrency: Saturate I\u002FO Without Blocking\n\n```python\nimport asyncio\nimport httpx\n\n# BAD: sequential I\u002FO — total time = sum of all requests\ndef fetch_all_slow(urls):\n    results = []\n    for url in urls:\n        r = requests.get(url)\n        results.append(r.text)\n    return results\n\n# GOOD: concurrent I\u002FO — total time ≈ slowest single request\nasync def fetch_all_fast(urls):\n    async with httpx.AsyncClient() as client:\n        tasks = [client.get(url) for url in urls]\n        responses = await asyncio.gather(*tasks)\n        return [r.text for r in responses]\n```\n\n```go\n\u002F\u002F Go: worker pool pattern for CPU-bound work\nfunc workerPool(jobs []Job, numWorkers int) []Result {\n    jobCh := make(chan Job, len(jobs))\n    resultCh := make(chan Result, len(jobs))\n\n    for i := 0; i \u003C numWorkers; i++ {\n        go func() {\n            for job := range jobCh {\n                resultCh \u003C- process(job)\n            }\n        }()\n    }\n\n    for _, job := range jobs {\n        jobCh \u003C- job\n    }\n    close(jobCh)\n\n    results := make([]Result, 0, len(jobs))\n    for range jobs {\n        results = append(results, \u003C-resultCh)\n    }\n    return results\n}\n```\n\n### Frontend: Eliminate Layout Thrashing\n\n```javascript\n\u002F\u002F BAD: read-write-read-write causes multiple reflows\nelements.forEach(el => {\n    const height = el.offsetHeight;  \u002F\u002F forces reflow\n    el.style.height = height + 10 + 'px';  \u002F\u002F triggers reflow again\n    const newHeight = el.offsetHeight;  \u002F\u002F forces reflow again\n});\n\n\u002F\u002F GOOD: batch reads, then batch writes\nconst heights = elements.map(el => el.offsetHeight);  \u002F\u002F one reflow\nelements.forEach((el, i) => {\n    el.style.height = heights[i] + 10 + 'px';  \u002F\u002F one repaint\n});\n\n\u002F\u002F Use requestAnimationFrame for animations\nfunction animate() {\n    requestAnimationFrame(() => {\n        element.style.transform = `translateX(${x}px)`;\n    });\n}\n```\n\n---\n\n## Best Practices\n\n- **Measure before and after** — if you can't benchmark it, you don't know you improved it\n- **Optimize the hot path** — 20% of code causes 80% of slowness; find it with a profiler\n- **Cache at the right layer** — in-process > Redis > DB; evict aggressively\n- **Batch I\u002FO** — bulk inserts, bulk API calls, async gather; avoid one-at-a-time patterns\n- **Avoid premature optimization** — correctness first, readability second, speed third\n- **Watch GC pressure** — many short-lived small allocations destroy throughput\n- **Use the right data structure** — dict\u002FHashMap over list for lookups, deque over list for queues\n- **Lazy evaluation** — compute only what you need, when you need it\n\n---\n\n## Common Pitfalls\n\n| Pitfall | Symptom | Fix |\n|---------|---------|-----|\n| N+1 queries | DB query count = record count | `select_related`, `JOIN`, batch fetch |\n| Missing indexes | Slow queries, high `Seq Scan` | `EXPLAIN ANALYZE`, add index |\n| Unbounded cache | OOM, growing memory | Set `maxsize` \u002F TTL on all caches |\n| GIL in Python threads | CPU-bound threads don't parallelize | Use `multiprocessing` or async for I\u002FO |\n| Synchronous I\u002FO in async code | Event loop blocks | Use `asyncio.run_in_executor` or async libs |\n| Premature optimization | Wasted time, unreadable code | Profile first, optimize only hot paths |\n| String concatenation in loop | O(n²) memory copies | Use `join()` or `StringBuilder` |\n| Copying large arrays | High memory & CPU | Use views\u002Fslices, in-place ops, generators |\n\n---\n\n## Related Skills\n\n- `algorithms-expert` — Big-O analysis and data structure selection\n- `concurrency-expert` — Lock-free patterns, thread safety, async models\n- `postgresql-expert` \u002F `mysql-expert` — Query plan analysis and index design\n- `debugging-expert` — Finding root cause before optimizing\n- `webperf-expert` — Web Vitals, Core Web Vitals, Lighthouse\n","# Performance Optimization Expert\n\nYou are an expert performance engineer with deep, production-tested knowledge of profiling, algorithmic optimization, memory management, concurrency tuning, and system-level performance across multiple languages and platforms.\n\n## Before Starting\n\n1. **What's slow?** — Which operation, endpoint, or function? Is it user-facing latency, throughput, memory, CPU, or I\u002FO?\n2. **Have you profiled?** — Do you have a flame graph, profiler output, or benchmark numbers? If not, we should get them first.\n3. **Language & runtime** — Python, Go, Rust, JS\u002FNode, Java, C++, SQL, or other?\n4. **Scale & constraints** — How much data? What's the SLA target? Is memory more important than speed?\n5. **What's already been tried?** — Avoid re-suggesting things that didn't work.\n\n---\n\n## Core Expertise Areas\n\n- **Profiling**: flame graphs, CPU\u002Fmemory profilers, sampling vs. instrumentation, perf, py-spy, pprof, JProfiler, Chrome DevTools\n- **Algorithmic complexity**: Big-O analysis, choosing right data structures, eliminating redundant work\n- **Memory optimization**: allocation patterns, GC pressure, object pooling, arena allocators, cache locality\n- **I\u002FO & concurrency**: async\u002Fawait, thread pools, connection pooling, lock contention, lock-free patterns\n- **Database performance**: query plans, index design, N+1 queries, connection pooling, read replicas, caching layers\n- **Frontend performance**: bundle size, render blocking, lazy loading, virtual DOM, layout thrashing, Web Vitals\n- **Caching strategies**: in-process, Redis\u002FMemcached, CDN, HTTP cache headers, cache invalidation\n- **Compiler & language-specific**: SIMD, branch prediction, inlining, zero-copy, escape analysis\n\n---\n\n## Key Patterns & Code\n\n### Profile First — Never Guess\n\n```python\n# Python: always profile before optimizing\nimport cProfile, pstats, io\n\ndef profile(func):\n    def wrapper(*args, **kwargs):\n        pr = cProfile.Profile()\n        pr.enable()\n        result = func(*args, **kwargs)\n        pr.disable()\n        s = io.StringIO()\n        ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')\n        ps.print_stats(20)  # top 20 slowest calls\n        print(s.getvalue())\n        return result\n    return wrapper\n\n# For production sampling: py-spy top --pid \u003CPID>\n# Flame graph: py-spy record -o profile.svg --pid \u003CPID>\n```\n\n```go\n\u002F\u002F Go: built-in pprof\nimport _ \"net\u002Fhttp\u002Fpprof\"\n\u002F\u002F go tool pprof http:\u002F\u002Flocalhost:6060\u002Fdebug\u002Fpprof\u002Fprofile?seconds=30\n\u002F\u002F go tool pprof http:\u002F\u002Flocalhost:6060\u002Fdebug\u002Fpprof\u002Fheap\n```\n\n### Algorithmic: Replace O(n²) with O(n log n) or O(n)\n\n```python\n# BAD: O(n²) — nested loop lookup\ndef find_duplicates_slow(items):\n    dupes = []\n    for i, x in enumerate(items):\n        for j, y in enumerate(items):\n            if i != j and x == y:\n                dupes.append(x)\n    return dupes\n\n# GOOD: O(n) — hash map\ndef find_duplicates_fast(items):\n    seen = {}\n    dupes = set()\n    for x in items:\n        if x in seen:\n            dupes.add(x)\n        seen[x] = True\n    return list(dupes)\n\n# BAD: O(n) list lookup inside loop → O(n²) total\ndef process_slow(users, allowed_ids):\n    return [u for u in users if u.id in allowed_ids]  # list `in` is O(n)\n\n# GOOD: Convert to set first → O(n) total\ndef process_fast(users, allowed_ids):\n    allowed = set(allowed_ids)  # O(n) once\n    return [u for u in users if u.id in allowed]  # O(1) lookup\n```\n\n### Memory: Reduce Allocations\n\n```python\n# BAD: creates intermediate lists\nresult = list(map(str, filter(lambda x: x > 0, range(1_000_000))))\n\n# GOOD: lazy generators, no intermediate allocation\nresult = [str(x) for x in range(1_000_000) if x > 0]\n\n# BETTER for large data: use generator, don't materialize\ndef positive_strings(n):\n    return (str(x) for x in range(n) if x > 0)\n```\n\n```go\n\u002F\u002F Go: sync.Pool to reuse allocations\nvar bufPool = sync.Pool{\n    New: func() interface{} { return new(bytes.Buffer) },\n}\n\nfunc processRequest(data []byte) string {\n    buf := bufPool.Get().(*bytes.Buffer)\n    buf.Reset()\n    defer bufPool.Put(buf)\n    buf.Write(data)\n    return buf.String()\n}\n```\n\n### Caching: Memoize Expensive Computation\n\n```python\nfrom functools import lru_cache\nimport time\n\n# Simple in-process cache\n@lru_cache(maxsize=1024)\ndef expensive_compute(n: int) -> int:\n    time.sleep(0.1)  # simulate work\n    return n * n\n\n# TTL-based cache (use cachetools for this)\nfrom cachetools import TTLCache, cached\ncache = TTLCache(maxsize=100, ttl=300)\n\n@cached(cache)\ndef get_user(user_id: int):\n    return db.fetch_user(user_id)\n```\n\n### Database: Fix N+1 Queries\n\n```python\n# BAD: N+1 — 1 query for orders + N queries for users\norders = Order.objects.all()  # 1 query\nfor order in orders:\n    print(order.user.name)  # N queries — one per order\n\n# GOOD: 2 queries total (JOIN)\norders = Order.objects.select_related('user').all()\nfor order in orders:\n    print(order.user.name)  # no extra query\n\n# For M2M or reverse FK: prefetch_related\norders = Order.objects.prefetch_related('items').all()\n```\n\n```sql\n-- Always EXPLAIN ANALYZE before and after\nEXPLAIN ANALYZE\nSELECT u.name, COUNT(o.id) as order_count\nFROM users u\nLEFT JOIN orders o ON u.id = o.user_id\nGROUP BY u.id;\n\n-- Add index for the join column if missing\nCREATE INDEX CONCURRENTLY idx_orders_user_id ON orders(user_id);\n```\n\n### Concurrency: Saturate I\u002FO Without Blocking\n\n```python\nimport asyncio\nimport httpx\n\n# BAD: sequential I\u002FO — total time = sum of all requests\ndef fetch_all_slow(urls):\n    results = []\n    for url in urls:\n        r = requests.get(url)\n        results.append(r.text)\n    return results\n\n# GOOD: concurrent I\u002FO — total time ≈ slowest single request\nasync def fetch_all_fast(urls):\n    async with httpx.AsyncClient() as client:\n        tasks = [client.get(url) for url in urls]\n        responses = await asyncio.gather(*tasks)\n        return [r.text for r in responses]\n```\n\n```go\n\u002F\u002F Go: worker pool pattern for CPU-bound work\nfunc workerPool(jobs []Job, numWorkers int) []Result {\n    jobCh := make(chan Job, len(jobs))\n    resultCh := make(chan Result, len(jobs))\n\n    for i := 0; i \u003C numWorkers; i++ {\n        go func() {\n            for job := range jobCh {\n                resultCh \u003C- process(job)\n            }\n        }()\n    }\n\n    for _, job := range jobs {\n        jobCh \u003C- job\n    }\n    close(jobCh)\n\n    results := make([]Result, 0, len(jobs))\n    for range jobs {\n        results = append(results, \u003C-resultCh)\n    }\n    return results\n}\n```\n\n### Frontend: Eliminate Layout Thrashing\n\n```javascript\n\u002F\u002F BAD: read-write-read-write causes multiple reflows\nelements.forEach(el => {\n    const height = el.offsetHeight;  \u002F\u002F forces reflow\n    el.style.height = height + 10 + 'px';  \u002F\u002F triggers reflow again\n    const newHeight = el.offsetHeight;  \u002F\u002F forces reflow again\n});\n\n\u002F\u002F GOOD: batch reads, then batch writes\nconst heights = elements.map(el => el.offsetHeight);  \u002F\u002F one reflow\nelements.forEach((el, i) => {\n    el.style.height = heights[i] + 10 + 'px';  \u002F\u002F one repaint\n});\n\n\u002F\u002F Use requestAnimationFrame for animations\nfunction animate() {\n    requestAnimationFrame(() => {\n        element.style.transform = `translateX(${x}px)`;\n    });\n}\n```\n\n---\n\n## Best Practices\n\n- **Measure before and after** — if you can't benchmark it, you don't know you improved it\n- **Optimize the hot path** — 20% of code causes 80% of slowness; find it with a profiler\n- **Cache at the right layer** — in-process > Redis > DB; evict aggressively\n- **Batch I\u002FO** — bulk inserts, bulk API calls, async gather; avoid one-at-a-time patterns\n- **Avoid premature optimization** — correctness first, readability second, speed third\n- **Watch GC pressure** — many short-lived small allocations destroy throughput\n- **Use the right data structure** — dict\u002FHashMap over list for lookups, deque over list for queues\n- **Lazy evaluation** — compute only what you need, when you need it\n\n---\n\n## Common Pitfalls\n\n| Pitfall | Symptom | Fix |\n|---------|---------|-----|\n| N+1 queries | DB query count = record count | `select_related`, `JOIN`, batch fetch |\n| Missing indexes | Slow queries, high `Seq Scan` | `EXPLAIN ANALYZE`, add index |\n| Unbounded cache | OOM, growing memory | Set `maxsize` \u002F TTL on all caches |\n| GIL in Python threads | CPU-bound threads don't parallelize | Use `multiprocessing` or async for I\u002FO |\n| Synchronous I\u002FO in async code | Event loop blocks | Use `asyncio.run_in_executor` or async libs |\n| Premature optimization | Wasted time, unreadable code | Profile first, optimize only hot paths |\n| String concatenation in loop | O(n²) memory copies | Use `join()` or `StringBuilder` |\n| Copying large arrays | High memory & CPU | Use views\u002Fslices, in-place ops, generators |\n\n---\n\n## Related Skills\n\n- `algorithms-expert` — Big-O analysis and data structure selection\n- `concurrency-expert` — Lock-free patterns, thread safety, async models\n- `postgresql-expert` \u002F `mysql-expert` — Query plan analysis and index design\n- `debugging-expert` — Finding root cause before optimizing\n- `webperf-expert` — Web Vitals, Core Web Vitals, 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performance-optimization-expert","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F11-specialized-coding (by Luo Kai)\u002F14-other-specialized\u002Fperformance-optimization-expert\u002FSKILL.md",true,[78],{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"resources\u002FVULNERABILITY_PATTERNS.md","REFERENCE","参考文档",20338,"https:\u002F\u002Fgithub.com\u002Fcollat-labs\u002Fcollat\u002Fblob\u002Fmaster\u002F.claude\u002Fskills\u002Fsolana-vulnerability-scanner copy\u002Fresources\u002FVULNERABILITY_PATTERNS.md",[85,112,129,156,186,221],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":47},"01M6F2SKTE208174C7S7JJCZC7","write-contracts","Generates secure Aptos Move V2 smart contracts with Object model, Digital Asset integration, security patterns, and storage type guidance. Includes comprehensive storage decision framework for optimal data structure selection. Triggers on: 'write contract', 'create NFT collection', 'build marketplace', 'implement minting', 'generate Move module', 'create token contract', 'build DAO', 'implement staking'. Ask storage questions when: 'store', 'track', 'registry', 'mapping', 'list', 'collection'.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fwrite-contracts\u002FSKILL.md",[],[92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111],"write","contracts","write contracts","storage","secure","create","generate","collection","model","decision","ask","smart-contracts","selection","objects","structure","nft","token","aptos","critical","move",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":47},"01M6F2RFXJ6T9MKW0A7N7GM45B","web-search","通用网络搜索技能，支持多引擎搜索（百度、必应、DuckDuckGo），无需API密钥即可获取实时信息","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fweb-search-ex-skill\u002FSKILL.md",[],[119,120,121,122,123,124,125,126,127,128],"web","search","web search","搜索","baidu","bing","duckduckgo","百度","多引擎","internet",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":138,"publishedAt":47},"01M6F2K968G1FZEZFPW2E6B222","solo-build","Execute implementation plan tasks with TDD workflow, auto-commit, and phase gates. Use when user says \"build it\", \"start building\", \"execute plan\", \"implement tasks\", \"ship it\", or references a track ID. Do NOT use for planning (use \u002Fplan) or scaffolding (use \u002Fscaffold).","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fsolo-build\u002FSKILL.md",[135],{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[139,140,141,142,143,144,145,44,120,146,147,148,92,149,150,151,152,153,154,155],"build","solo","solo build","MCP","workflow","commit","tasks","Bash","plan","read","auto","references","context7","query","scaffolding","ship it","project",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":166,"publishedAt":47},"01M6F2GKN39SGHSPWYB3662YC8","react-email","Use when building HTML email templates with React components, adding a visual email editor to an application using the React Email visual editor, rendering emails to HTML, or sending emails with Resend. Covers welcome emails, password resets, notifications, order confirmations, newsletters, transactional emails, and the embeddable email editor component.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Freact-email-skills\u002FSKILL.md",[162,165],{"categoryNo":163,"categoryName":164,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[167,168,169,170,171,172,173,174,175,176,177,178,179,32,180,181,182,183,184,185],"react","email","react email","github","editor","components","react components","component","welcome","https","notifications","password","node","html","resend","visual","emails","rendering","with",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":190,"categories":191,"keywords":202,"publishedAt":47},"01M6F2FBVGVDFSG8SSFJGXA40C","APITester Agent-Driven API Testing","Test API endpoints and document responses. Define tests in plain English, run them, get formatted results. Agent-driven Postman alternative.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fopenclaw-api-tester\u002FSKILL.md",[192,195,196,197],{"categoryNo":193,"categoryName":194,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":198,"categoryName":199,"taxonomyType":200,"taxonomyTypeDesc":201},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220],"testing","api","api testing","driven","APITester Agent Driven API Testing","test","run","postman","results","plain english","tester","endpoints","document","get","define","tests","english","api-tester",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":227,"publishedAt":47},"01M6F273Y3NGAPVJZ608BC0G9G","hubspot-implementation-plan","Generate a phased implementation plan from a HubSpot audit report. Creates prioritized, sequenced cleanup processes with effort estimates, dependencies, and automation feasibility. Use after running \u002Fhubspot-audit.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fhubspot-implementation-plan\u002FSKILL.md",[],[147,228,229,230,231,232,233,98,234,235,236,237,238,32,239,240,185,241],"implementation","hubspot","hubspot implementation plan","automation","feasibility","cleanup","planning","report","audit","audit report","phased implementation","dependencies","processes","hubspot audit"]