[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F95JMHD0ES7RWA1BS90711":3,"skill-same:01M6F95JMHD0ES7RWA1BS90711":94},{"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":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":42,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":61,"install":62,"resources":75},"01M6F95JMHD0ES7RWA1BS90711","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.","boisenoise\u002Fskills-collections","boisenoise","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F193180?v=4","01M6CDBDQK6BDN55ZMA6B6PKBM","01M6CDBDVSJ3JPTBYN26FZ7TZK",12,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-monte-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",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"performance","Monte Carlo","performance diagnosis","monte carlo performance diagnosis","diagnosis","platform","github","toolkit","tree","mc","issues","trends","observability","drill","critical","license","latency","discover","monte-carlo","main","community","queries","bridge","pipeline","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## Bundled upstream support\n\nSupporting files are included at skill-relative paths. See [UPSTREAM-NOTICE.md](UPSTREAM-NOTICE.md) for their pinned sources, licenses, hashes, and import changes.\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:** This catalog tree does not bundle a server. Discover an existing authorized connection, confirm its endpoint\u002Faccount, and use its actual schema. Upstream installations may expose names such as\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_query_perf_profile` -- 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\nDiagnosis remains read-only. Query text and job metadata can contain private data; redact report output. Verify telemetry coverage, units, trend sign\u002Fconfidence semantics and grouping in the actual tool schema. Correlated timing, precomputed RCA and percentile ratios suggest hypotheses, not causal proof. Runtime is not monetary cost without billing\u002Fresource evidence. Applying warehouse, query or pipeline changes needs a separate scoped request.\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_query_perf_profile` 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:** Report that these queries returned no rows, with the inspected scope\u002Fwindow and telemetry gaps; that does not establish absence of issues. 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. Use a bounded investigation budget, but do not declare a root cause merely to meet a call count.\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","## Bundled upstream support\n\nSupporting files are included at skill-relative paths. See [UPSTREAM-NOTICE.md](UPSTREAM-NOTICE.md) for their pinned sources, licenses, hashes, and import changes.\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:** This catalog tree does not bundle a server. Discover an existing authorized connection, confirm its endpoint\u002Faccount, and use its actual schema. Upstream installations may expose names such as\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_query_perf_profile` -- 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\nDiagnosis remains read-only. Query text and job metadata can contain private data; redact report output. Verify telemetry coverage, units, trend sign\u002Fconfidence semantics and grouping in the actual tool schema. Correlated timing, precomputed RCA and percentile ratios suggest hypotheses, not causal proof. Runtime is not monetary cost without billing\u002Fresource evidence. Applying warehouse, query or pipeline changes needs a separate scoped request.\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_query_perf_profile` 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:** Report that these queries returned no rows, with the inspected scope\u002Fwindow and telemetry gaps; that does not establish absence of issues. 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. Use a bounded investigation budget, but do not declare a root cause merely to meet a call count.\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":37,"source":52,"license":14,"date_added":53,"description":7,"source_repo":54,"source_type":43,"license_source":55},"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","7bf2279c8a65758319526a721d5c4b75683aec9c","skills\u002Fantigravity-monte-carlo-performance-diagnosis",3,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections","https:\u002F\u002Fgithub.com\u002Fboisenoise","2026-11-12 14:21:24",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add boisenoise\u002Fskills-collections --skill monte-carlo-performance-diagnosis",{"tool":68,"command":69},"bunx","bunx skills add boisenoise\u002Fskills-collections --skill 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Treg for public web or platform data only when existing search, browser and connected tools cannot cover the task, with explicit cost and data boundaries.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FSKILL.md",[],[103,104,105,106,107,108,109,110,111,112,113,114,115,116,28,37,117,118,119,120,121],"web","access","web access","official","development","browser","search","data","Tools","read","fetch","only","youtube","free 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