[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6P9WF70RFZ40RK6YDNKDNK1":3,"skill-same:01M6P9WF70RFZ40RK6YDNKDNK1":71},{"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":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":34,"commitSha":52,"skillDirectory":53,"forksCount":13,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6P9WF70RFZ40RK6YDNKDNK1","monte-carlo-performance-diagnosis",null,"Diagnoses pipeline performance issues -- slow jobs, expensive queries, latency trends -- using Monte Carlo's cross-platform observability. Uses a tiered investigation approach: discover problems, bridge to affected tables, then drill into root causes.","duc-duy-1612\u002FOnline-Delivery-Systems-Web","duc-duy-1612","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F192231384?v=4","01M6NSDKQQDKVTHZ8DZMZ1VYY6","01M6NSDKW8DDVTQHBT1HBDDJPT",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fmonte-carlo-performance-diagnosis\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"monte carlo performance diagnosis","diagnosis","performance","monte-carlo","tree","mc","drill","critical","platform","latency","discover","main","community","queries","bridge","pipeline","license","observability","issues","toolkit","2026-11-10 10:12:30",false,"---\nname: monte-carlo-performance-diagnosis\ndescription: \"Diagnoses pipeline performance issues -- slow jobs, expensive queries, latency trends -- using Monte Carlo's cross-platform observability. Uses a tiered investigation approach: discover problems, bridge to affected tables, then drill into root causes.\"\nrisk: critical\nsource: https:\u002F\u002Fgithub.com\u002Fmonte-carlo-data\u002Fmc-agent-toolkit\u002Ftree\u002Fmain\u002Fskills\u002Fperformance-diagnosis\nsource_repo: monte-carlo-data\u002Fmc-agent-toolkit\nsource_type: community\ndate_added: 2026-07-01\nlicense: Apache-2.0\nlicense_source: https:\u002F\u002Fgithub.com\u002Fmonte-carlo-data\u002Fmc-agent-toolkit\u002Fblob\u002Fmain\u002FLICENSE\n---\n\n# Monte Carlo Performance Diagnosis Skill\n\nThis skill helps diagnose data pipeline performance issues using Monte Carlo's cross-platform observability data. It works across Airflow, dbt, Databricks, and warehouse query engines to find bottlenecks, detect regressions, and identify root causes.\n\n> **Monte Carlo tool routing (required):** Always call Monte Carlo MCP tools through this plugin's\n> bundled server, whose fully-qualified tool names are\n> `mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__\u003Ctool>` (e.g.\n> `mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__get_alerts`). Bare tool names used in this skill\n> (`get_alerts`, `search`, `get_table`, …) refer to that bundled server. If the session also has a\n> separately-configured `monte-carlo-mcp` server, do **not** route to it — it may point at a\n> different endpoint or credentials.\n\nReference files live next to this skill file. **Use the Read tool** (not MCP resources) to access them:\n\n- Tiered investigation approach: `references\u002Finvestigation-tiers.md` (relative to this file)\n- Query analysis patterns: `references\u002Fquery-analysis.md` (relative to this file)\n\n## When to activate this skill\n\nActivate when the user:\n\n- Asks about slow pipelines, jobs, or queries\n- Wants to find expensive or costly queries\n- Mentions performance regressions or degradation\n- Asks \"why is this pipeline slow?\" or \"what's using the most compute?\"\n- Wants to compare performance over time or find bottleneck tasks\n- Asks about failed or futile query patterns\n\n## When NOT to activate this skill\n\nDo not activate when the user is:\n\n- Investigating data quality issues (use the prevent skill)\n- Looking at storage costs (use the storage-cost-analysis skill)\n- Creating monitors (use the monitoring-advisor skill)\n- Just querying data or exploring table contents\n\n## Prerequisites\n\nThe following MCP tools must be available (connect to Monte Carlo's MCP server):\n\n**Discovery tools (Tier 1):**\n- `get_jobs_performance` -- find slow\u002Ffailing jobs across Airflow, dbt, Databricks\n- `get_top_slow_queries` -- find slowest query groups by total runtime\n\n**Bridge tool:**\n- `get_tables_for_job` -- convert job MCONs to table MCONs\n\n**Diagnosis tools (Tier 2):**\n- `get_tasks_performance` -- drill into a job's individual tasks\n- `get_change_timeline` -- unified timeline of query changes, volume shifts, Airflow\u002Fdbt failures\n- `get_query_rca` -- root cause analysis for failed\u002Ffutile queries\n- `get_query_latency_distribution` -- latency trend over time\n- `get_asset_lineage` -- trace upstream\u002Fdownstream impact\n\n**Supporting tools:**\n- `get_warehouses` -- list available warehouses\n\n## Workflow\n\n### Step 1: Identify the scope\n\nDetermine what the user wants to investigate:\n- **Specific job\u002Fpipeline**: User mentions a job name or pipeline\n- **Specific table**: User mentions a table that's slow to update\n- **General discovery**: User wants to find what's slow\n\nCall `get_warehouses` to list available warehouses. Match the user's context to a warehouse.\n\n### Step 2: Tier 1 -- Discovery\n\nIf you don't have specific MCONs to investigate, start with discovery:\n\n1. **Find slow jobs**: Call `get_jobs_performance` with optional `integration_type` filter (AIRFLOW, DATABRICKS, DBT) if the user specifies a platform.\n   - Results include: job name, average duration, trend (7-day), run count, failure rate\n   - Look for: high `avgDuration`, negative `runDurationTrend7d`, high failure rates\n\n2. **Find expensive queries**: Call `get_top_slow_queries` with optional `warehouse_id` and `query_type` (\"read\" for SELECTs, \"write\" for INSERT\u002FCREATE\u002FMERGE).\n   - Results include: query hash, total runtime, average runtime, run count\n   - Look for: queries with high total runtime or high individual execution time\n\nPresent the top findings to the user before drilling deeper. A typical investigation needs only 3-7 tool calls.\n\n**If both discovery tools return no results:** Tell the user no performance issues were found in the current time window. Suggest broadening the scope (different warehouse, longer time range, or a different platform filter).\n\n### Step 3: Bridge -- Job to Tables\n\nAfter Tier 1 identifies problematic jobs, convert to table MCONs:\n\nCall `get_tables_for_job(job_mcon=..., integration_type=...)` using the `integration_type` from the job performance results.\n\nThis gives you the table MCONs needed for Tier 2 investigation.\n\n### Step 4: Tier 2 -- Diagnosis\n\nNow drill into root causes using the MCONs from discovery or the bridge:\n\n1. **Task bottleneck**: Call `get_tasks_performance` to find which specific task in a job is the bottleneck.\n\n2. **What changed?** Call `get_change_timeline` -- this is your most powerful tool. It returns a unified timeline of:\n   - Query text changes (schema modifications, new JOINs, filter changes)\n   - Volume shifts (row count spikes\u002Fdrops)\n   - Airflow task failures\n   - dbt model failures\n   All in one call. Look for correlations: \"query changed on day X, runtime doubled on day X+1.\"\n\n3. **Why are queries failing?** Call `get_query_rca` to get root cause analysis:\n   - **Failed** queries: errors, timeouts, permission issues\n   - **Futile** queries: queries that run but produce no useful output\n   - Patterns are pre-computed -- the tool groups failures by cause\n\n4. **Is latency degrading?** Call `get_query_latency_distribution` to see the trend:\n   - Compare p50 vs p95 -- if p95 >> p50 (>5x), the problem is outlier queries\n   - Look for step-changes in latency (sudden increase = regression)\n   - For step-change \u002F regression-time-localization use cases, pass `bucket=\"1h\"`. The default downsamples to daily on windows ≥ 3 days, which hides hour-level steps.\n\n5. **Trace impact**: Call `get_asset_lineage` with `direction=\"DOWNSTREAM\"` to see what's affected by a slow table, or `direction=\"UPSTREAM\"` to find what feeds it.\n\n### Step 5: Present findings\n\nStructure your response as:\n\n1. **Problem summary**: What's slow and by how much (with exact numbers from tools)\n2. **Root cause**: What changed or what's causing the issue\n3. **Impact**: What downstream systems are affected\n4. **Recommendations**: Specific actions to fix the issue\n\n### Important rules\n\n- **Quote tool numbers exactly.** If a tool returns \"1282 runs, avg 22.5s\", say exactly that. Never round, estimate, or fabricate numbers.\n- **Always compare to baselines.** Use 7-day trend data (`runDurationTrend7d`) to distinguish regressions from normal variance. Flag if trend data has less than 0.1 confidence.\n- **Stop when you have a root cause.** 3-7 tool calls is typical. More than 10 means you're over-investigating.\n- **Read vs write queries**: When the user asks about \"reads\" or \"read queries\", filter with `query_type=\"read\"`. When they ask about \"writes\", use `query_type=\"write\"`. Do NOT mix them.\n- **Never expose MCONs, UUIDs, or internal identifiers** to the user. Use human-readable names.\n- **Cross-platform**: This skill works across Airflow, dbt, and Databricks. Note which platform each finding comes from.\n\n## Example\n\n**User request:**\n\n> Diagnose why this pipeline became slow, identify the bottleneck from the available telemetry, and propose the smallest verified fix.\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream source and local project context.\n- Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.\n- Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n","# Monte Carlo Performance Diagnosis Skill\n\nThis skill helps diagnose data pipeline performance issues using Monte Carlo's cross-platform observability data. It works across Airflow, dbt, Databricks, and warehouse query engines to find bottlenecks, detect regressions, and identify root causes.\n\n> **Monte Carlo tool routing (required):** Always call Monte Carlo MCP tools through this plugin's\n> bundled server, whose fully-qualified tool names are\n> `mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__\u003Ctool>` (e.g.\n> `mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__get_alerts`). Bare tool names used in this skill\n> (`get_alerts`, `search`, `get_table`, …) refer to that bundled server. If the session also has a\n> separately-configured `monte-carlo-mcp` server, do **not** route to it — it may point at a\n> different endpoint or credentials.\n\nReference files live next to this skill file. **Use the Read tool** (not MCP resources) to access them:\n\n- Tiered investigation approach: `references\u002Finvestigation-tiers.md` (relative to this file)\n- Query analysis patterns: `references\u002Fquery-analysis.md` (relative to this file)\n\n## When to activate this skill\n\nActivate when the user:\n\n- Asks about slow pipelines, jobs, or queries\n- Wants to find expensive or costly queries\n- Mentions performance regressions or degradation\n- Asks \"why is this pipeline slow?\" or \"what's using the most compute?\"\n- Wants to compare performance over time or find bottleneck tasks\n- Asks about failed or futile query patterns\n\n## When NOT to activate this skill\n\nDo not activate when the user is:\n\n- Investigating data quality issues (use the prevent skill)\n- Looking at storage costs (use the storage-cost-analysis skill)\n- Creating monitors (use the monitoring-advisor skill)\n- Just querying data or exploring table contents\n\n## Prerequisites\n\nThe following MCP tools must be available (connect to Monte Carlo's MCP server):\n\n**Discovery tools (Tier 1):**\n- `get_jobs_performance` -- find slow\u002Ffailing jobs across Airflow, dbt, Databricks\n- `get_top_slow_queries` -- find slowest query groups by total runtime\n\n**Bridge tool:**\n- `get_tables_for_job` -- convert job MCONs to table MCONs\n\n**Diagnosis tools (Tier 2):**\n- `get_tasks_performance` -- drill into a job's individual tasks\n- `get_change_timeline` -- unified timeline of query changes, volume shifts, Airflow\u002Fdbt failures\n- `get_query_rca` -- root cause analysis for failed\u002Ffutile queries\n- `get_query_latency_distribution` -- latency trend over time\n- `get_asset_lineage` -- trace upstream\u002Fdownstream impact\n\n**Supporting tools:**\n- `get_warehouses` -- list available warehouses\n\n## Workflow\n\n### Step 1: Identify the scope\n\nDetermine what the user wants to investigate:\n- **Specific job\u002Fpipeline**: User mentions a job name or pipeline\n- **Specific table**: User mentions a table that's slow to update\n- **General discovery**: User wants to find what's slow\n\nCall `get_warehouses` to list available warehouses. Match the user's context to a warehouse.\n\n### Step 2: Tier 1 -- Discovery\n\nIf you don't have specific MCONs to investigate, start with discovery:\n\n1. **Find slow jobs**: Call `get_jobs_performance` with optional `integration_type` filter (AIRFLOW, DATABRICKS, DBT) if the user specifies a platform.\n   - Results include: job name, average duration, trend (7-day), run count, failure rate\n   - Look for: high `avgDuration`, negative `runDurationTrend7d`, high failure rates\n\n2. **Find expensive queries**: Call `get_top_slow_queries` with optional `warehouse_id` and `query_type` (\"read\" for SELECTs, \"write\" for INSERT\u002FCREATE\u002FMERGE).\n   - Results include: query hash, total runtime, average runtime, run count\n   - Look for: queries with high total runtime or high individual execution time\n\nPresent the top findings to the user before drilling deeper. A typical investigation needs only 3-7 tool calls.\n\n**If both discovery tools return no results:** Tell the user no performance issues were found in the current time window. Suggest broadening the scope (different warehouse, longer time range, or a different platform filter).\n\n### Step 3: Bridge -- Job to Tables\n\nAfter Tier 1 identifies problematic jobs, convert to table MCONs:\n\nCall `get_tables_for_job(job_mcon=..., integration_type=...)` using the `integration_type` from the job performance results.\n\nThis gives you the table MCONs needed for Tier 2 investigation.\n\n### Step 4: Tier 2 -- Diagnosis\n\nNow drill into root causes using the MCONs from discovery or the bridge:\n\n1. **Task bottleneck**: Call `get_tasks_performance` to find which specific task in a job is the bottleneck.\n\n2. **What changed?** Call `get_change_timeline` -- this is your most powerful tool. It returns a unified timeline of:\n   - Query text changes (schema modifications, new JOINs, filter changes)\n   - Volume shifts (row count spikes\u002Fdrops)\n   - Airflow task failures\n   - dbt model failures\n   All in one call. Look for correlations: \"query changed on day X, runtime doubled on day X+1.\"\n\n3. **Why are queries failing?** Call `get_query_rca` to get root cause analysis:\n   - **Failed** queries: errors, timeouts, permission issues\n   - **Futile** queries: queries that run but produce no useful output\n   - Patterns are pre-computed -- the tool groups failures by cause\n\n4. **Is latency degrading?** Call `get_query_latency_distribution` to see the trend:\n   - Compare p50 vs p95 -- if p95 >> p50 (>5x), the problem is outlier queries\n   - Look for step-changes in latency (sudden increase = regression)\n   - For step-change \u002F regression-time-localization use cases, pass `bucket=\"1h\"`. The default downsamples to daily on windows ≥ 3 days, which hides hour-level steps.\n\n5. **Trace impact**: Call `get_asset_lineage` with `direction=\"DOWNSTREAM\"` to see what's affected by a slow table, or `direction=\"UPSTREAM\"` to find what feeds it.\n\n### Step 5: Present findings\n\nStructure your response as:\n\n1. **Problem summary**: What's slow and by how much (with exact numbers from tools)\n2. **Root cause**: What changed or what's causing the issue\n3. **Impact**: What downstream systems are affected\n4. **Recommendations**: Specific actions to fix the issue\n\n### Important rules\n\n- **Quote tool numbers exactly.** If a tool returns \"1282 runs, avg 22.5s\", say exactly that. Never round, estimate, or fabricate numbers.\n- **Always compare to baselines.** Use 7-day trend data (`runDurationTrend7d`) to distinguish regressions from normal variance. Flag if trend data has less than 0.1 confidence.\n- **Stop when you have a root cause.** 3-7 tool calls is typical. More than 10 means you're over-investigating.\n- **Read vs write queries**: When the user asks about \"reads\" or \"read queries\", filter with `query_type=\"read\"`. When they ask about \"writes\", use `query_type=\"write\"`. Do NOT mix them.\n- **Never expose MCONs, UUIDs, or internal identifiers** to the user. Use human-readable names.\n- **Cross-platform**: This skill works across Airflow, dbt, and Databricks. Note which platform each finding comes from.\n\n## Example\n\n**User request:**\n\n> Diagnose why this pipeline became slow, identify the bottleneck from the available telemetry, and propose the smallest verified fix.\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream source and local project context.\n- Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.\n- Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n",{"name":5,"risk":30,"source":48,"license":14,"date_added":49,"description":7,"source_repo":50,"source_type":35,"license_source":51},"https:\u002F\u002Fgithub.com\u002Fmonte-carlo-data\u002Fmc-agent-toolkit\u002Ftree\u002Fmain\u002Fskills\u002Fperformance-diagnosis",1782864000000,"monte-carlo-data\u002Fmc-agent-toolkit","https:\u002F\u002Fgithub.com\u002Fmonte-carlo-data\u002Fmc-agent-toolkit\u002Fblob\u002Fmain\u002FLICENSE","f3312ba749561b6159f477dbfc9313c875d40a56",".agents\u002Fskills_archive\u002Fmonte-carlo-performance-diagnosis","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612","2026-10-25 23:50:58",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add duc-duy-1612\u002FOnline-Delivery-Systems-Web --skill monte-carlo-performance-diagnosis",{"tool":63,"command":64},"bunx","bunx skills add duc-duy-1612\u002FOnline-Delivery-Systems-Web --skill monte-carlo-performance-diagnosis",{"tool":66,"command":67},"pnpm","pnpm dlx skills add duc-duy-1612\u002FOnline-Delivery-Systems-Web --skill monte-carlo-performance-diagnosis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fmonte-carlo-performance-diagnosis\u002FSKILL.md",true,[],[72,99,123,147,175,199],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":82,"publishedAt":43},"01M6Q0FA5FZ410Z33Z3TFQ4DBA","zero-trust","Implement zero-trust network architecture. Configure identity-based access, micro-segmentation, and continuous verification. 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