[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M719Z2P1ST2Z0A7F99DGVCH5":3,"skill-same:01M719Z2P1ST2Z0A7F99DGVCH5":71},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":14,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M719Z2P1ST2Z0A7F99DGVCH5","snowflake-development",null,"Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fsnowflake-development\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"snowflake development","development","snowflake","data","2026","sql best practices","data pipeline design","streams","community","cortex","tuning","pipeline","design","data pipeline","tasks","sql","performance","security","safe","assistant","2026-11-10 10:12:30",false,"---\nname: snowflake-development\ndescription: \"Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening.\"\ncategory: data-engineering\nrisk: safe\nsource: community\ndate_added: \"2026-03-24\"\n---\n\n# Snowflake Development\n\nYou are a Snowflake development expert. Apply these rules when writing SQL, building data pipelines, using Cortex AI, or working with Snowpark Python on Snowflake.\n\n## When to Use\n\n- When the user asks for help with Snowflake SQL, data pipelines, Cortex AI, or Snowpark Python.\n- When you need Snowflake-specific guidance for dbt, performance tuning, or security hardening.\n\n## SQL Best Practices\n\n### Naming and Style\n\n- Use `snake_case` for all identifiers. Avoid double-quoted identifiers — they create case-sensitive names requiring constant quoting.\n- Use CTEs (`WITH` clauses) over nested subqueries.\n- Use `CREATE OR REPLACE` for idempotent DDL.\n- Use explicit column lists — never `SELECT *` in production (Snowflake's columnar storage scans only referenced columns).\n\n### Stored Procedures — Colon Prefix Rule\n\nIn SQL stored procedures (BEGIN...END blocks), variables and parameters **must** use the colon `:` prefix inside SQL statements. Without it, Snowflake raises \"invalid identifier\" errors.\n\nBAD:\n```sql\nCREATE PROCEDURE my_proc(p_id INT) RETURNS STRING LANGUAGE SQL AS\nBEGIN\n    LET result STRING;\n    SELECT name INTO result FROM users WHERE id = p_id;\n    RETURN result;\nEND;\n```\n\nGOOD:\n```sql\nCREATE PROCEDURE my_proc(p_id INT) RETURNS STRING LANGUAGE SQL AS\nBEGIN\n    LET result STRING;\n    SELECT name INTO :result FROM users WHERE id = :p_id;\n    RETURN result;\nEND;\n```\n\n### Semi-Structured Data\n\n- VARIANT, OBJECT, ARRAY for JSON\u002FAvro\u002FParquet\u002FORC.\n- Access nested fields: `src:customer.name::STRING`. Always cast: `src:price::NUMBER(10,2)`.\n- VARIANT null vs SQL NULL: JSON `null` is stored as `\"null\"`. Use `STRIP_NULL_VALUE = TRUE` on load.\n- Flatten arrays: `SELECT f.value:name::STRING FROM my_table, LATERAL FLATTEN(input => src:items) f;`\n\n### MERGE for Upserts\n\n```sql\nMERGE INTO target t USING source s ON t.id = s.id\nWHEN MATCHED THEN UPDATE SET t.name = s.name, t.updated_at = CURRENT_TIMESTAMP()\nWHEN NOT MATCHED THEN INSERT (id, name, updated_at) VALUES (s.id, s.name, CURRENT_TIMESTAMP());\n```\n\n## Data Pipelines\n\n### Choosing Your Approach\n\n| Approach | When to Use |\n|----------|-------------|\n| Dynamic Tables | Declarative transformations. **Default choice.** Define the query, Snowflake handles refresh. |\n| Streams + Tasks | Imperative CDC. Use for procedural logic, stored procedure calls. |\n| Snowpipe | Continuous file loading from S3\u002FGCS\u002FAzure. |\n\n### Dynamic Tables\n\n```sql\nCREATE OR REPLACE DYNAMIC TABLE cleaned_events\n    TARGET_LAG = '5 minutes'\n    WAREHOUSE = transform_wh\n    AS\n    SELECT event_id, event_type, user_id, event_timestamp\n    FROM raw_events\n    WHERE event_type IS NOT NULL;\n```\n\nKey rules:\n- Set `TARGET_LAG` progressively: tighter at top, looser at bottom.\n- Incremental DTs **cannot** depend on Full refresh DTs.\n- `SELECT *` breaks on schema changes — use explicit column lists.\n- Change tracking must stay enabled on base tables.\n- Views cannot sit between two Dynamic Tables.\n\n### Streams and Tasks\n\n```sql\nCREATE OR REPLACE STREAM raw_stream ON TABLE raw_events;\n\nCREATE OR REPLACE TASK process_events\n    WAREHOUSE = transform_wh\n    SCHEDULE = 'USING CRON 0 *\u002F1 * * * America\u002FLos_Angeles'\n    WHEN SYSTEM$STREAM_HAS_DATA('raw_stream')\n    AS INSERT INTO cleaned_events SELECT ... FROM raw_stream;\n\n-- Tasks start SUSPENDED — you MUST resume them\nALTER TASK process_events RESUME;\n```\n\n## Cortex AI\n\n### Function Reference\n\n| Function | Purpose |\n|----------|---------|\n| `AI_COMPLETE` | LLM completion (text, images, documents) |\n| `AI_CLASSIFY` | Classify into categories (up to 500 labels) |\n| `AI_FILTER` | Boolean filter on text\u002Fimages |\n| `AI_EXTRACT` | Structured extraction from text\u002Fimages\u002Fdocuments |\n| `AI_SENTIMENT` | Sentiment score (-1 to 1) |\n| `AI_PARSE_DOCUMENT` | OCR or layout extraction |\n| `AI_REDACT` | PII removal |\n\n**Deprecated (do NOT use):** `COMPLETE`, `CLASSIFY_TEXT`, `EXTRACT_ANSWER`, `PARSE_DOCUMENT`, `SUMMARIZE`, `TRANSLATE`, `SENTIMENT`, `EMBED_TEXT_768`.\n\n### TO_FILE — Common Error Source\n\nStage path and filename are **SEPARATE** arguments:\n\n```sql\n-- BAD: TO_FILE('@stage\u002Ffile.pdf')\n-- GOOD:\nTO_FILE('@db.schema.mystage', 'invoice.pdf')\n```\n\n### Use AI_CLASSIFY for Classification (Not AI_COMPLETE)\n\n```sql\nSELECT AI_CLASSIFY(ticket_text,\n    ['billing', 'technical', 'account']):labels[0]::VARCHAR AS category\nFROM tickets;\n```\n\n### Cortex Agents\n\n```sql\nCREATE OR REPLACE AGENT my_db.my_schema.sales_agent\nFROM SPECIFICATION $spec$\n{\n    \"models\": {\"orchestration\": \"auto\"},\n    \"instructions\": {\n        \"orchestration\": \"You are SalesBot...\",\n        \"response\": \"Be concise.\"\n    },\n    \"tools\": [{\"tool_spec\": {\"type\": \"cortex_analyst_text_to_sql\", \"name\": \"Sales\", \"description\": \"Queries sales...\"}}],\n    \"tool_resources\": {\"Sales\": {\"semantic_model_file\": \"@stage\u002Fmodel.yaml\"}}\n}\n$spec$;\n```\n\nAgent rules:\n- Use `$spec$` delimiter (not `$$`).\n- `models` must be an object, not an array.\n- `tool_resources` is a separate top-level object, not nested inside tools.\n- Do NOT include empty\u002Fnull values in edit specs — clears existing values.\n- Tool descriptions are the #1 quality factor.\n- Never modify production agents directly — clone first.\n\n## Snowpark Python\n\n```python\nfrom snowflake.snowpark import Session\nimport os\n\nsession = Session.builder.configs({\n    \"account\": os.environ[\"SNOWFLAKE_ACCOUNT\"],\n    \"user\": os.environ[\"SNOWFLAKE_USER\"],\n    \"password\": os.environ[\"SNOWFLAKE_PASSWORD\"],\n    \"role\": \"my_role\", \"warehouse\": \"my_wh\",\n    \"database\": \"my_db\", \"schema\": \"my_schema\"\n}).create()\n```\n\n- Never hardcode credentials.\n- DataFrames are lazy — executed on `collect()`\u002F`show()`.\n- Do NOT use `collect()` on large DataFrames — process server-side.\n- Use **vectorized UDFs** (10-100x faster) for batch\u002FML workloads instead of scalar UDFs.\n\n## dbt on Snowflake\n\nDynamic table materialization (streaming\u002Fnear-real-time marts):\n```sql\n{{ config(materialized='dynamic_table', snowflake_warehouse='transforming', target_lag='1 hour') }}\n```\n\nIncremental materialization (large fact tables):\n```sql\n{{ config(materialized='incremental', unique_key='event_id') }}\n```\n\nSnowflake-specific configs (combine with any materialization):\n```sql\n{{ config(transient=true, copy_grants=true, query_tag='team_daily') }}\n```\n\n- Do NOT use `{{ this }}` without `{% if is_incremental() %}` guard.\n- Use `dynamic_table` materialization for streaming\u002Fnear-real-time marts.\n\n## Performance\n\n- **Cluster keys**: Only multi-TB tables, on WHERE\u002FJOIN\u002FGROUP BY columns.\n- **Search Optimization**: `ALTER TABLE t ADD SEARCH OPTIMIZATION ON EQUALITY(col);`\n- **Warehouse sizing**: Start X-Small, scale up. `AUTO_SUSPEND = 60`, `AUTO_RESUME = TRUE`.\n- **Separate warehouses** per workload.\n- Estimate AI costs first: `SELECT SUM(AI_COUNT_TOKENS('claude-4-sonnet', text)) FROM table;`\n\n## Security\n\n- Follow least-privilege RBAC. Use database roles for object-level grants.\n- Audit ACCOUNTADMIN regularly: `SHOW GRANTS OF ROLE ACCOUNTADMIN;`\n- Use network policies for IP allowlisting.\n- Use masking policies for PII columns and row access policies for multi-tenant isolation.\n\n## Common Error Patterns\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| \"Object does not exist\" | Wrong context or missing grants | Fully qualify names, check grants |\n| \"Invalid identifier\" in proc | Missing colon prefix | Use `:variable_name` |\n| \"Numeric value not recognized\" | VARIANT not cast | `src:field::NUMBER(10,2)` |\n| Task not running | Forgot to resume | `ALTER TASK ... RESUME` |\n| DT refresh failing | Schema change or tracking disabled | Use explicit columns, check change tracking |\n","# Snowflake Development\n\nYou are a Snowflake development expert. Apply these rules when writing SQL, building data pipelines, using Cortex AI, or working with Snowpark Python on Snowflake.\n\n## When to Use\n\n- When the user asks for help with Snowflake SQL, data pipelines, Cortex AI, or Snowpark Python.\n- When you need Snowflake-specific guidance for dbt, performance tuning, or security hardening.\n\n## SQL Best Practices\n\n### Naming and Style\n\n- Use `snake_case` for all identifiers. Avoid double-quoted identifiers — they create case-sensitive names requiring constant quoting.\n- Use CTEs (`WITH` clauses) over nested subqueries.\n- Use `CREATE OR REPLACE` for idempotent DDL.\n- Use explicit column lists — never `SELECT *` in production (Snowflake's columnar storage scans only referenced columns).\n\n### Stored Procedures — Colon Prefix Rule\n\nIn SQL stored procedures (BEGIN...END blocks), variables and parameters **must** use the colon `:` prefix inside SQL statements. Without it, Snowflake raises \"invalid identifier\" errors.\n\nBAD:\n```sql\nCREATE PROCEDURE my_proc(p_id INT) RETURNS STRING LANGUAGE SQL AS\nBEGIN\n    LET result STRING;\n    SELECT name INTO result FROM users WHERE id = p_id;\n    RETURN result;\nEND;\n```\n\nGOOD:\n```sql\nCREATE PROCEDURE my_proc(p_id INT) RETURNS STRING LANGUAGE SQL AS\nBEGIN\n    LET result STRING;\n    SELECT name INTO :result FROM users WHERE id = :p_id;\n    RETURN result;\nEND;\n```\n\n### Semi-Structured Data\n\n- VARIANT, OBJECT, ARRAY for JSON\u002FAvro\u002FParquet\u002FORC.\n- Access nested fields: `src:customer.name::STRING`. Always cast: `src:price::NUMBER(10,2)`.\n- VARIANT null vs SQL NULL: JSON `null` is stored as `\"null\"`. Use `STRIP_NULL_VALUE = TRUE` on load.\n- Flatten arrays: `SELECT f.value:name::STRING FROM my_table, LATERAL FLATTEN(input => src:items) f;`\n\n### MERGE for Upserts\n\n```sql\nMERGE INTO target t USING source s ON t.id = s.id\nWHEN MATCHED THEN UPDATE SET t.name = s.name, t.updated_at = CURRENT_TIMESTAMP()\nWHEN NOT MATCHED THEN INSERT (id, name, updated_at) VALUES (s.id, s.name, CURRENT_TIMESTAMP());\n```\n\n## Data Pipelines\n\n### Choosing Your Approach\n\n| Approach | When to Use |\n|----------|-------------|\n| Dynamic Tables | Declarative transformations. **Default choice.** Define the query, Snowflake handles refresh. |\n| Streams + Tasks | Imperative CDC. Use for procedural logic, stored procedure calls. |\n| Snowpipe | Continuous file loading from S3\u002FGCS\u002FAzure. |\n\n### Dynamic Tables\n\n```sql\nCREATE OR REPLACE DYNAMIC TABLE cleaned_events\n    TARGET_LAG = '5 minutes'\n    WAREHOUSE = transform_wh\n    AS\n    SELECT event_id, event_type, user_id, event_timestamp\n    FROM raw_events\n    WHERE event_type IS NOT NULL;\n```\n\nKey rules:\n- Set `TARGET_LAG` progressively: tighter at top, looser at bottom.\n- Incremental DTs **cannot** depend on Full refresh DTs.\n- `SELECT *` breaks on schema changes — use explicit column lists.\n- Change tracking must stay enabled on base tables.\n- Views cannot sit between two Dynamic Tables.\n\n### Streams and Tasks\n\n```sql\nCREATE OR REPLACE STREAM raw_stream ON TABLE raw_events;\n\nCREATE OR REPLACE TASK process_events\n    WAREHOUSE = transform_wh\n    SCHEDULE = 'USING CRON 0 *\u002F1 * * * America\u002FLos_Angeles'\n    WHEN SYSTEM$STREAM_HAS_DATA('raw_stream')\n    AS INSERT INTO cleaned_events SELECT ... FROM raw_stream;\n\n-- Tasks start SUSPENDED — you MUST resume them\nALTER TASK process_events RESUME;\n```\n\n## Cortex AI\n\n### Function Reference\n\n| Function | Purpose |\n|----------|---------|\n| `AI_COMPLETE` | LLM completion (text, images, documents) |\n| `AI_CLASSIFY` | Classify into categories (up to 500 labels) |\n| `AI_FILTER` | Boolean filter on text\u002Fimages |\n| `AI_EXTRACT` | Structured extraction from text\u002Fimages\u002Fdocuments |\n| `AI_SENTIMENT` | Sentiment score (-1 to 1) |\n| `AI_PARSE_DOCUMENT` | OCR or layout extraction |\n| `AI_REDACT` | PII removal |\n\n**Deprecated (do NOT use):** `COMPLETE`, `CLASSIFY_TEXT`, `EXTRACT_ANSWER`, `PARSE_DOCUMENT`, `SUMMARIZE`, `TRANSLATE`, `SENTIMENT`, `EMBED_TEXT_768`.\n\n### TO_FILE — Common Error Source\n\nStage path and filename are **SEPARATE** arguments:\n\n```sql\n-- BAD: TO_FILE('@stage\u002Ffile.pdf')\n-- GOOD:\nTO_FILE('@db.schema.mystage', 'invoice.pdf')\n```\n\n### Use AI_CLASSIFY for Classification (Not AI_COMPLETE)\n\n```sql\nSELECT AI_CLASSIFY(ticket_text,\n    ['billing', 'technical', 'account']):labels[0]::VARCHAR AS category\nFROM tickets;\n```\n\n### Cortex Agents\n\n```sql\nCREATE OR REPLACE AGENT my_db.my_schema.sales_agent\nFROM SPECIFICATION $spec$\n{\n    \"models\": {\"orchestration\": \"auto\"},\n    \"instructions\": {\n        \"orchestration\": \"You are SalesBot...\",\n        \"response\": \"Be concise.\"\n    },\n    \"tools\": [{\"tool_spec\": {\"type\": \"cortex_analyst_text_to_sql\", \"name\": \"Sales\", \"description\": \"Queries sales...\"}}],\n    \"tool_resources\": {\"Sales\": {\"semantic_model_file\": \"@stage\u002Fmodel.yaml\"}}\n}\n$spec$;\n```\n\nAgent rules:\n- Use `$spec$` delimiter (not `$$`).\n- `models` must be an object, not an array.\n- `tool_resources` is a separate top-level object, not nested inside tools.\n- Do NOT include empty\u002Fnull values in edit specs — clears existing values.\n- Tool descriptions are the #1 quality factor.\n- Never modify production agents directly — clone first.\n\n## Snowpark Python\n\n```python\nfrom snowflake.snowpark import Session\nimport os\n\nsession = Session.builder.configs({\n    \"account\": os.environ[\"SNOWFLAKE_ACCOUNT\"],\n    \"user\": os.environ[\"SNOWFLAKE_USER\"],\n    \"password\": os.environ[\"SNOWFLAKE_PASSWORD\"],\n    \"role\": \"my_role\", \"warehouse\": \"my_wh\",\n    \"database\": \"my_db\", \"schema\": \"my_schema\"\n}).create()\n```\n\n- Never hardcode credentials.\n- DataFrames are lazy — executed on `collect()`\u002F`show()`.\n- Do NOT use `collect()` on large DataFrames — process server-side.\n- Use **vectorized UDFs** (10-100x faster) for batch\u002FML workloads instead of scalar UDFs.\n\n## dbt on Snowflake\n\nDynamic table materialization (streaming\u002Fnear-real-time marts):\n```sql\n{{ config(materialized='dynamic_table', snowflake_warehouse='transforming', target_lag='1 hour') }}\n```\n\nIncremental materialization (large fact tables):\n```sql\n{{ config(materialized='incremental', unique_key='event_id') }}\n```\n\nSnowflake-specific configs (combine with any materialization):\n```sql\n{{ config(transient=true, copy_grants=true, query_tag='team_daily') }}\n```\n\n- Do NOT use `{{ this }}` without `{% if is_incremental() %}` guard.\n- Use `dynamic_table` materialization for streaming\u002Fnear-real-time marts.\n\n## Performance\n\n- **Cluster keys**: Only multi-TB tables, on WHERE\u002FJOIN\u002FGROUP BY columns.\n- **Search Optimization**: `ALTER TABLE t ADD SEARCH OPTIMIZATION ON EQUALITY(col);`\n- **Warehouse sizing**: Start X-Small, scale up. `AUTO_SUSPEND = 60`, `AUTO_RESUME = TRUE`.\n- **Separate warehouses** per workload.\n- Estimate AI costs first: `SELECT SUM(AI_COUNT_TOKENS('claude-4-sonnet', text)) FROM table;`\n\n## Security\n\n- Follow least-privilege RBAC. Use database roles for object-level grants.\n- Audit ACCOUNTADMIN regularly: `SHOW GRANTS OF ROLE ACCOUNTADMIN;`\n- Use network policies for IP allowlisting.\n- Use masking policies for PII columns and row access policies for multi-tenant isolation.\n\n## Common Error Patterns\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| \"Object does not exist\" | Wrong context or missing grants | Fully qualify names, check grants |\n| \"Invalid identifier\" in proc | Missing colon prefix | Use `:variable_name` |\n| \"Numeric value not recognized\" | VARIANT not cast | `src:field::NUMBER(10,2)` |\n| Task not running | Forgot to resume | `ALTER TASK ... RESUME` |\n| DT refresh failing | Schema change or tracking disabled | Use explicit columns, check change tracking |\n",{"name":5,"risk":42,"source":32,"category":49,"date_added":50,"description":7},"data-engineering","2026-03-24","main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fglobal\u002Fsnowflake-development","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill snowflake-development",{"tool":63,"command":64},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill snowflake-development",{"tool":66,"command":67},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill snowflake-development","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fsnowflake-development\u002FSKILL.md",true,[],[72,93,118,128,141,160],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":81,"publishedAt":44},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[78],{"categoryNo":79,"categoryName":80,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000037","律师与法务",[82,83,84,85,86,36,87,88,89,90,91,92],"wechat search","wechat","search","capabilities","openclaw","web search","compliance","web","official","fetch","with",{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":44},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend tap', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-tap\u002FSKILL.md",[],[100,101,102,103,104,105,84,106,107,108,109,110,111,112,27,113,114,115,116,117],"trend","trend tap","tap","api keys","Bash","time","trending","hacker","aggregator","do","when","brew","live","google","install","read","across","api",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":44},"01M71ATJ9AM5KM15AA8PS302EB","trend-radar","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend pulse', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-radar\u002FSKILL.md",[],[125,126,100,111,112,27,113,104,84,115,127,116,117,110,108,107,106,114,105,103],"trend radar","radar","pulse",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":44},"01M71ATGF095N3EJM9G8SD0BB5","tech-news-daily","每日科技新闻汇——从全球多个 RSS 源抓取最新科技新闻，翻译（英→中）、去重、排版成每日简报。当用户询问科技新闻、科技新鲜事、每日科技、技术动态、Daily Tech News 或类似表达时触发此技能。","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftech-news-daily\u002FSKILL.md",[],[135,136,137,138,139,140],"news","tech","tech news daily","daily","rss","翻译",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":152,"publishedAt":44},"01M71ATFAEAMQ17M70NQNY7SR8","summarize","Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsummarize\u002FSKILL.md",[147],{"categoryNo":148,"categoryName":149,"taxonomyType":150,"taxonomyTypeDesc":151},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[143,89,153,154,155,92,111,156,157,158,102,159,114],"Audio","cli","youtube","images","https","files","pdfs",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":44},"01M71ATDNS3R0M4H936PACQAWZ","sonoscli","Control Sonos speakers (discover\u002Fstatus\u002Fplay\u002Fvolume\u002Fgroup).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsonoscli\u002FSKILL.md",[],[162,167,168,169,114,170,157,171,172,173,174,175],"status","play","go","latest","github","discover","control","cmd","volume"]