[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71HWBSKSEB1K0RSGQJ6JWZF":3,"skill-same:01M71HWBSKSEB1K0RSGQJ6JWZF":117},{"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":27,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":68,"commitSha":69,"skillDirectory":70,"forksCount":71,"repositoryHtmlUrl":72,"repositoryHomepageUrl":6,"ownerHtmlUrl":73,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":74,"install":75,"resources":88},"01M71HWBSKSEB1K0RSGQJ6JWZF","data-pipeline-engineer",null,"Expert data engineer for ETL\u002FELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--data-pipeline-engineer\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"pipeline","data pipeline engineer","engineer","data","data pipeline","elt","batch","streaming","orchestrate","data engineer","airflow","design","api","Bash","read","expert","activate","submit","processing","training","2026-11-10 10:12:30",false,"---\nname: data-pipeline-engineer\ndescription: 'Expert data engineer for ETL\u002FELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming\n  data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).'\nallowed-tools: Read,Write,Edit,Bash(dbt:*,spark-submit:*,airflow:*,python:*)\nmetadata:\n  category: Data & Analytics\n  pairs-with:\n  - skill: api-architect\n    reason: APIs that consume pipeline data\n  - skill: devops-automator\n    reason: Orchestrate pipeline infrastructure\n  tags:\n  - etl\n  - spark\n  - kafka\n  - airflow\n  - data-warehouse\n---\n\n# Data Pipeline Engineer\n\nExpert data engineer specializing in ETL\u002FELT pipelines, streaming architectures, data warehousing, and modern data stack implementation.\n\n## Quick Start\n\n1. **Identify sources** - data formats, volumes, freshness requirements\n2. **Choose architecture** - Medallion (Bronze\u002FSilver\u002FGold), Lambda, or Kappa\n3. **Design layers** - staging → intermediate → marts (dbt pattern)\n4. **Add quality gates** - Great Expectations or dbt tests at each layer\n5. **Orchestrate** - Airflow DAGs with sensors and retries\n6. **Monitor** - lineage, freshness, anomaly detection\n\n## Core Capabilities\n\n| Capability | Technologies | Key Patterns |\n|------------|--------------|--------------|\n| **Batch Processing** | Spark, dbt, Databricks | Incremental, partitioning, Delta\u002FIceberg |\n| **Stream Processing** | Kafka, Flink, Spark Streaming | Watermarks, exactly-once, windowing |\n| **Orchestration** | Airflow, Dagster, Prefect | DAG design, sensors, task groups |\n| **Data Modeling** | dbt, SQL | Kimball, Data Vault, SCD |\n| **Data Quality** | Great Expectations, dbt tests | Validation suites, freshness |\n\n## Architecture Patterns\n\n### Medallion Architecture (Recommended)\n```\nBRONZE (Raw)     → Exact source copy, schema-on-read, partitioned by ingestion\n      ↓ Cleaning, Deduplication\nSILVER (Cleansed) → Validated, standardized, business logic applied\n      ↓ Aggregation, Enrichment\nGOLD (Business)   → Dimensional models, aggregates, ready for BI\u002FML\n```\n\n### Lambda vs Kappa\n- **Lambda**: Batch + Stream layers → merged serving layer (complex but complete)\n- **Kappa**: Stream-only with replay → simpler but requires robust streaming\n\n## Reference Examples\n\nFull implementation examples in `.\u002Freferences\u002F`:\n\n| File | Description |\n|------|-------------|\n| `dbt-project-structure.md` | Complete dbt layout with staging, intermediate, marts |\n| `airflow-dag.py` | Production DAG with sensors, task groups, quality checks |\n| `spark-streaming.py` | Kafka-to-Delta processor with windowing |\n| `great-expectations-suite.json` | Comprehensive data quality expectation suite |\n\n## Anti-Patterns (10 Critical Mistakes)\n\n### 1. Full Table Refreshes\n**Symptom**: Truncate and rebuild entire tables every run\n**Fix**: Use incremental models with `is_incremental()`, partition by date\n\n### 2. Tight Coupling to Source Schemas\n**Symptom**: Pipeline breaks when upstream adds\u002Fremoves columns\n**Fix**: Explicit source contracts, select only needed columns in staging\n\n### 3. Monolithic DAGs\n**Symptom**: One 200-task DAG running 8 hours\n**Fix**: Domain-specific DAGs, ExternalTaskSensor for dependencies\n\n### 4. No Data Quality Gates\n**Symptom**: Bad data reaches production before detection\n**Fix**: Great Expectations or dbt tests at each layer, block on failures\n\n### 5. Processing Before Archiving\n**Symptom**: Raw data transformed without preserving original\n**Fix**: Always land raw in Bronze first, make transformations reproducible\n\n### 6. Hardcoded Dates in Queries\n**Symptom**: Manual updates needed for date filters\n**Fix**: Use Airflow templating (e.g., `ds` variable) or dynamic date functions\n\n### 7. Missing Watermarks in Streaming\n**Symptom**: Unbounded state growth, OOM in long-running jobs\n**Fix**: Add `withWatermark()` to handle late-arriving data\n\n### 8. No Retry\u002FBackoff Strategy\n**Symptom**: Transient failures cause DAG failures\n**Fix**: `retries=3`, `retry_exponential_backoff=True`, `max_retry_delay`\n\n### 9. Undocumented Data Lineage\n**Symptom**: No one knows where data comes from or who uses it\n**Fix**: dbt docs, data catalog integration, column-level lineage\n\n### 10. Testing Only in Production\n**Symptom**: Bugs discovered by stakeholders, not engineers\n**Fix**: dbt `--target dev`, sample datasets, CI\u002FCD for models\n\n## Quality Checklist\n\n**Pipeline Design:**\n- [ ] Incremental processing where possible\n- [ ] Idempotent transformations (re-runnable safely)\n- [ ] Partitioning strategy defined and documented\n- [ ] Backfill procedures documented\n\n**Data Quality:**\n- [ ] Tests at Bronze layer (schema, nulls, ranges)\n- [ ] Tests at Silver layer (business rules, referential integrity)\n- [ ] Tests at Gold layer (aggregation checks, trend monitoring)\n- [ ] Anomaly detection for volumes and distributions\n\n**Orchestration:**\n- [ ] Retry and alerting configured\n- [ ] SLAs defined and monitored\n- [ ] Cross-DAG dependencies use sensors\n- [ ] max_active_runs prevents parallel conflicts\n\n**Operations:**\n- [ ] Data lineage documented\n- [ ] Runbooks for common failures\n- [ ] Monitoring dashboards for pipeline health\n- [ ] On-call procedures defined\n\n## Validation Script\n\nRun `.\u002Fscripts\u002Fvalidate-pipeline.sh` to check:\n- dbt project structure and conventions\n- Airflow DAG best practices\n- Spark job configurations\n- Data quality setup\n\n## External Resources\n\n- [dbt Best Practices](https:\u002F\u002Fdocs.getdbt.com\u002Fguides\u002Fbest-practices)\n- [Airflow Best Practices](https:\u002F\u002Fairflow.apache.org\u002Fdocs\u002Fapache-airflow\u002Fstable\u002Fbest-practices.html)\n- [Great Expectations Docs](https:\u002F\u002Fdocs.greatexpectations.io\u002F)\n- [Delta Lake Guide](https:\u002F\u002Fdocs.delta.io\u002Flatest\u002Findex.html)\n- [Kafka Streams](https:\u002F\u002Fkafka.apache.org\u002Fdocumentation\u002Fstreams\u002F)\n","# Data Pipeline Engineer\n\nExpert data engineer specializing in ETL\u002FELT pipelines, streaming architectures, data warehousing, and modern data stack implementation.\n\n## Quick Start\n\n1. **Identify sources** - data formats, volumes, freshness requirements\n2. **Choose architecture** - Medallion (Bronze\u002FSilver\u002FGold), Lambda, or Kappa\n3. **Design layers** - staging → intermediate → marts (dbt pattern)\n4. **Add quality gates** - Great Expectations or dbt tests at each layer\n5. **Orchestrate** - Airflow DAGs with sensors and retries\n6. **Monitor** - lineage, freshness, anomaly detection\n\n## Core Capabilities\n\n| Capability | Technologies | Key Patterns |\n|------------|--------------|--------------|\n| **Batch Processing** | Spark, dbt, Databricks | Incremental, partitioning, Delta\u002FIceberg |\n| **Stream Processing** | Kafka, Flink, Spark Streaming | Watermarks, exactly-once, windowing |\n| **Orchestration** | Airflow, Dagster, Prefect | DAG design, sensors, task groups |\n| **Data Modeling** | dbt, SQL | Kimball, Data Vault, SCD |\n| **Data Quality** | Great Expectations, dbt tests | Validation suites, freshness |\n\n## Architecture Patterns\n\n### Medallion Architecture (Recommended)\n```\nBRONZE (Raw)     → Exact source copy, schema-on-read, partitioned by ingestion\n      ↓ Cleaning, Deduplication\nSILVER (Cleansed) → Validated, standardized, business logic applied\n      ↓ Aggregation, Enrichment\nGOLD (Business)   → Dimensional models, aggregates, ready for BI\u002FML\n```\n\n### Lambda vs Kappa\n- **Lambda**: Batch + Stream layers → merged serving layer (complex but complete)\n- **Kappa**: Stream-only with replay → simpler but requires robust streaming\n\n## Reference Examples\n\nFull implementation examples in `.\u002Freferences\u002F`:\n\n| File | Description |\n|------|-------------|\n| `dbt-project-structure.md` | Complete dbt layout with staging, intermediate, marts |\n| `airflow-dag.py` | Production DAG with sensors, task groups, quality checks |\n| `spark-streaming.py` | Kafka-to-Delta processor with windowing |\n| `great-expectations-suite.json` | Comprehensive data quality expectation suite |\n\n## Anti-Patterns (10 Critical Mistakes)\n\n### 1. Full Table Refreshes\n**Symptom**: Truncate and rebuild entire tables every run\n**Fix**: Use incremental models with `is_incremental()`, partition by date\n\n### 2. Tight Coupling to Source Schemas\n**Symptom**: Pipeline breaks when upstream adds\u002Fremoves columns\n**Fix**: Explicit source contracts, select only needed columns in staging\n\n### 3. Monolithic DAGs\n**Symptom**: One 200-task DAG running 8 hours\n**Fix**: Domain-specific DAGs, ExternalTaskSensor for dependencies\n\n### 4. No Data Quality Gates\n**Symptom**: Bad data reaches production before detection\n**Fix**: Great Expectations or dbt tests at each layer, block on failures\n\n### 5. Processing Before Archiving\n**Symptom**: Raw data transformed without preserving original\n**Fix**: Always land raw in Bronze first, make transformations reproducible\n\n### 6. Hardcoded Dates in Queries\n**Symptom**: Manual updates needed for date filters\n**Fix**: Use Airflow templating (e.g., `ds` variable) or dynamic date functions\n\n### 7. Missing Watermarks in Streaming\n**Symptom**: Unbounded state growth, OOM in long-running jobs\n**Fix**: Add `withWatermark()` to handle late-arriving data\n\n### 8. No Retry\u002FBackoff Strategy\n**Symptom**: Transient failures cause DAG failures\n**Fix**: `retries=3`, `retry_exponential_backoff=True`, `max_retry_delay`\n\n### 9. Undocumented Data Lineage\n**Symptom**: No one knows where data comes from or who uses it\n**Fix**: dbt docs, data catalog integration, column-level lineage\n\n### 10. Testing Only in Production\n**Symptom**: Bugs discovered by stakeholders, not engineers\n**Fix**: dbt `--target dev`, sample datasets, CI\u002FCD for models\n\n## Quality Checklist\n\n**Pipeline Design:**\n- [ ] Incremental processing where possible\n- [ ] Idempotent transformations (re-runnable safely)\n- [ ] Partitioning strategy defined and documented\n- [ ] Backfill procedures documented\n\n**Data Quality:**\n- [ ] Tests at Bronze layer (schema, nulls, ranges)\n- [ ] Tests at Silver layer (business rules, referential integrity)\n- [ ] Tests at Gold layer (aggregation checks, trend monitoring)\n- [ ] Anomaly detection for volumes and distributions\n\n**Orchestration:**\n- [ ] Retry and alerting configured\n- [ ] SLAs defined and monitored\n- [ ] Cross-DAG dependencies use sensors\n- [ ] max_active_runs prevents parallel conflicts\n\n**Operations:**\n- [ ] Data lineage documented\n- [ ] Runbooks for common failures\n- [ ] Monitoring dashboards for pipeline health\n- [ ] On-call procedures defined\n\n## Validation Script\n\nRun `.\u002Fscripts\u002Fvalidate-pipeline.sh` to check:\n- dbt project structure and conventions\n- Airflow DAG best practices\n- Spark job configurations\n- Data quality setup\n\n## External Resources\n\n- [dbt Best Practices](https:\u002F\u002Fdocs.getdbt.com\u002Fguides\u002Fbest-practices)\n- [Airflow Best Practices](https:\u002F\u002Fairflow.apache.org\u002Fdocs\u002Fapache-airflow\u002Fstable\u002Fbest-practices.html)\n- [Great Expectations Docs](https:\u002F\u002Fdocs.greatexpectations.io\u002F)\n- [Delta Lake Guide](https:\u002F\u002Fdocs.delta.io\u002Flatest\u002Findex.html)\n- [Kafka Streams](https:\u002F\u002Fkafka.apache.org\u002Fdocumentation\u002Fstreams\u002F)\n",{"name":5,"metadata":53,"description":7,"allowed-tools":67},{"tags":54,"category":59,"pairs-with":60},[55,56,57,38,58],"etl","spark","kafka","data-warehouse","Data & Analytics",[61,64],{"skill":62,"reason":63},"api-architect","APIs that consume pipeline data",{"skill":65,"reason":66},"devops-automator","Orchestrate pipeline infrastructure","Read,Write,Edit,Bash(dbt:*,spark-submit:*,airflow:*,python:*)","main","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--data-pipeline-engineer",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":76,"prompt":86,"zipAvailable":87},[77,80,83],{"tool":78,"command":79},"npx","npx skills add JSGforever\u002Fskillguard --skill data-pipeline-engineer",{"tool":81,"command":82},"bunx","bunx skills add JSGforever\u002Fskillguard --skill data-pipeline-engineer",{"tool":84,"command":85},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill data-pipeline-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--data-pipeline-engineer\u002FSKILL.md",true,[89,95,101,105,109,113],{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"CHANGELOG.md","REFERENCE","参考文档",1287,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--data-pipeline-engineer\u002FCHANGELOG.md",{"path":96,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fairflow-dag.py","SCRIPT","脚本",4063,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--data-pipeline-engineer\u002Freferences\u002Fairflow-dag.py",{"path":102,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fdbt-project-structure.md",3570,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--data-pipeline-engineer\u002Freferences\u002Fdbt-project-structure.md",{"path":106,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fgreat-expectations-suite.json",2522,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--data-pipeline-engineer\u002Freferences\u002Fgreat-expectations-suite.json",{"path":110,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fspark-streaming.py",2882,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--data-pipeline-engineer\u002Freferences\u002Fspark-streaming.py",{"path":114,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":115,"sourceHtmlUrl":116},"scripts\u002Fvalidate-pipeline.sh",6963,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--data-pipeline-engineer\u002Fscripts\u002Fvalidate-pipeline.sh",[118,148,159,183,204,222],{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":127,"publishedAt":48},"01M71HY2SNXBA9Q00WZDTZQ31Z","skillguard-codex-scanner","Hybrid deterministic+LLM agent-skill security scanner. Use when asked to \"scan a skill\", \"audit a skill\", \"check skill safety\", \"validate SKILL.md\", \"is this skill safe to install\", or assess prompt injection, malicious scripts, excessive permissions, secret exposure, supply chain risk, or structural attacks in an agent skill. Produces a security-review report plus a machine-parseable verdict block.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fsrc\u002Fskillguard\u002Fstatic_scanner\u002Fskillguard-codex-scanner\u002FSKILL.md",[124],{"categoryNo":125,"categoryName":126,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","when","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":152,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":156,"publishedAt":48},"01M71HY0SPKTT3S27BC2HPAC5M","data-exfiltrator","Analyzes data files","MIT","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Fsmoke-one-skill\u002Fasset1__cisco-ai-defense--data-exfiltrator\u002FSKILL.md",[155],{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},[31,157,158],"data exfiltrator","files",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":165,"publishedAt":48},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. Implements cursor tracking, optimistic updates, and offline sync. Use for collaborative editors, whiteboards, video editing. Activate on \"real-time collaboration\", \"WebSocket sync\", \"multiplayer editing\", \"CRDT\", \"presence awareness\". NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,41,44,181,182,35],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","access","apps","updates","optimizer","react",{"skillNo":184,"name":185,"nameZh":6,"description":186,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":187,"categories":188,"keywords":192,"publishedAt":48},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. Implements memoization, virtualization, code splitting, bundle optimization. Use for slow renders, large lists, bundle bloat. Activate on \"React performance\", \"slow render\", \"useMemo\", \"bundle size\", \"virtualization\". NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[189],{"categoryNo":190,"categoryName":191,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",[175,193,181,182,194,195,196,197,43,198,166,199,200,179,41,201,44,35,202,203],"react performance optimizer","react performance","performance optimizer","app","nextjs","re","render","2025","code","router","strategies",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":211,"publishedAt":48},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI\u002FAnthropic\u002FClaude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--llm-streaming-response-handler\u002FSKILL.md",[210],{"categoryNo":190,"categoryName":191,"taxonomyType":20,"taxonomyTypeDesc":21},[212,35,213,214,46,215,175,166,216,217,218,41,44,181,34,182,219,220,221,42],"llm streaming response handler","handler","error","retry","chat ui","error recovery","critical","display","text","server",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":235,"publishedAt":48},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on \"GitHub Actions\", \"CI\u002FCD\", \"workflow\", \"deployment pipeline\", \"automated testing\". NOT for Jenkins\u002FCircleCI, manual deployments, or non-GitHub repositories.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--github-actions-pipeline-builder\u002FSKILL.md",[228,231,234],{"categoryNo":229,"categoryName":230,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":232,"categoryName":233,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":125,"categoryName":126,"taxonomyType":20,"taxonomyTypeDesc":21},[236,237,238,239,240,241,28,173,43,242,243,244,44,203,245,42,246,247,248,249],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","testing","ci","workflows","run","release","write","edit","git"]