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data

Data analysis skill hub. Routes to the right specialist subskill depending on the request — exploration, query writing, end-to-end analysis, visualization, validation, dashboard planning plus assembly, or recurring snapshot refresh.

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npx skills add https://github.com/plc1220/HelpUDoc --skill data

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plc1220/HelpUDoc

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업데이트2026년 5월 25일 18:54

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SKILL.md

readonly

name	data
description	Data analysis skill hub. Routes to the right specialist subskill depending on the request — exploration, query writing, end-to-end analysis, visualization, validation, dashboard planning plus assembly, or recurring snapshot refresh.
tools	["data_agent_tools","get_table_schema","run_sql_query","materialize_bigquery_to_parquet","export_sql_query","generate_summary","generate_dashboard"]
mcp_servers	["toolbox-bq-demo"]

data — Skill Hub

This hub describes when to use each subskill in the data/ family. Load the most specific subskill that matches the request; fall back to data/analyze for general analysis tasks.

Subskill routing

Request type	Load skill
"What tables / columns / data do I have?"	`data/explore`
"Write me a SQL query to…"	`data/query`
"Analyze / what's happening with…" (general)	`data/analyze`
"Chart / visualize this…"	`data/visualize`
"Check / validate this analysis before I share it"	`data/validate`
"Build me a dashboard"	`data/dashboard`
"Refresh this every day / keep this report in sync"	`data/refresh`

Connector selection (apply in every subskill)

Choose the right data connector for the source:

Warehouse (managed datasets, large tables) — use the BigQuery MCP server (toolbox-bq-demo) for discovery and the initial scoped query. If the task will require iterative follow-up analysis, materialize the scoped result with materialize_bigquery_to_parquet and continue in DuckDB. Treat BigQuery as the system of record and DuckDB over workspace snapshots as the local serving layer.
Local files (CSV / Parquet in the workspace) — use data_agent_tools. Available tools: get_table_schema, run_sql_query, materialize_bigquery_to_parquet, generate_summary, generate_dashboard.
Recurring dashboards / canned reports — use data/refresh to publish a stable snapshot such as datasets/<slug>/latest.parquet, optionally mirror it to CSV, and regenerate a stable HTML artifact in dashboards/ or reports/.
Dashboard creation — data/dashboard is review-first. It should inspect the tagged dataset/report context, propose a dashboard plan, wait for approval, then generate the dashboard package.
Do not attempt cross-source SQL joins in v1. Orchestrate at the workflow level (query each source separately; combine in Python / summary prose).

Guardrails that apply to every subskill

Inspect schema / metadata before executing SQL.
Always cite concrete numbers (counts, averages, %, deltas). No speculation.
Maximum 10 SQL queries per run (enforced in code).
Maximum 5 charts per run (enforced in code).
One generate_summary OR one generate_dashboard per run.
Reports and dashboards only include artifacts from the current run — never from prior runs.
Dashboard requests should prefer a tagged dataset artifact as the source of truth and avoid fresh warehouse rediscovery unless the tagged inputs are unusable.
In data/dashboard, prefer structured chart bindings over generate_chart_config on the main path.

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Produce a sourced research report in the user's language using a structured, evidence-first workflow. Supports quick, standard, deep, and exhaustive research modes. Prioritizes evidence quality, entity disambiguation, claim verification, contradiction checking, uncertainty handling, and clear synthesis before final writing.

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plc1220

plc1220/HelpUDoc

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name	data
description	Data analysis skill hub. Routes to the right specialist subskill depending on the request — exploration, query writing, end-to-end analysis, visualization, validation, dashboard planning plus assembly, or recurring snapshot refresh.
tools	["data_agent_tools","get_table_schema","run_sql_query","materialize_bigquery_to_parquet","export_sql_query","generate_summary","generate_dashboard"]
mcp_servers	["toolbox-bq-demo"]

data — Skill Hub

This hub describes when to use each subskill in the data/ family. Load the most specific subskill that matches the request; fall back to data/analyze for general analysis tasks.

Subskill routing

Request type	Load skill
"What tables / columns / data do I have?"	`data/explore`
"Write me a SQL query to…"	`data/query`
"Analyze / what's happening with…" (general)	`data/analyze`
"Chart / visualize this…"	`data/visualize`
"Check / validate this analysis before I share it"	`data/validate`
"Build me a dashboard"	`data/dashboard`
"Refresh this every day / keep this report in sync"	`data/refresh`

Connector selection (apply in every subskill)

Choose the right data connector for the source:

Warehouse (managed datasets, large tables) — use the BigQuery MCP server (toolbox-bq-demo) for discovery and the initial scoped query. If the task will require iterative follow-up analysis, materialize the scoped result with materialize_bigquery_to_parquet and continue in DuckDB. Treat BigQuery as the system of record and DuckDB over workspace snapshots as the local serving layer.
Local files (CSV / Parquet in the workspace) — use data_agent_tools. Available tools: get_table_schema, run_sql_query, materialize_bigquery_to_parquet, generate_summary, generate_dashboard.
Recurring dashboards / canned reports — use data/refresh to publish a stable snapshot such as datasets/<slug>/latest.parquet, optionally mirror it to CSV, and regenerate a stable HTML artifact in dashboards/ or reports/.
Dashboard creation — data/dashboard is review-first. It should inspect the tagged dataset/report context, propose a dashboard plan, wait for approval, then generate the dashboard package.
Do not attempt cross-source SQL joins in v1. Orchestrate at the workflow level (query each source separately; combine in Python / summary prose).

Guardrails that apply to every subskill

Inspect schema / metadata before executing SQL.
Always cite concrete numbers (counts, averages, %, deltas). No speculation.
Maximum 10 SQL queries per run (enforced in code).
Maximum 5 charts per run (enforced in code).
One generate_summary OR one generate_dashboard per run.
Reports and dashboards only include artifacts from the current run — never from prior runs.
Dashboard requests should prefer a tagged dataset artifact as the source of truth and avoid fresh warehouse rediscovery unless the tagged inputs are unusable.
In data/dashboard, prefer structured chart bindings over generate_chart_config on the main path.