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sql-analysis

Business
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Answer a quantitative business question by writing a SQL query against the data warehouse, validating it, and presenting the result. Use when the user asks "how many...", "what's the trend of...", "compare X vs Y over...", "what's our top N...", or anything that resolves to a query against tabular data. Produces a small result table plus the underlying query.

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Source SKILL.md: https://github.com/agentscope-ai/agentscope-java/blob/HEAD/agentscope-examples/agents/agentscope-dataagent/src/main/resources/shared/agents/data-agent/skills/sql-analysis/SKILL.md

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SQL Analysis Skill

Repeatable SOP for turning a business question into a verifiable SQL answer.

Steps

  1. Restate the question as a metric. In one sentence, write: " by over , filtered by ". If any of those four (metric / grouping / window / filter) is missing or ambiguous, ask one clarifying question and stop. Do not guess.

  2. Locate the source. Decide where the data lives:

    • Look in knowledge/ first for any schema notes, data-dictionary entries, or prior query examples uploaded by the user.
    • If the right table is not obvious, delegate to the data-explorer sub-agent with the metric definition as the prompt — its job is to identify the canonical source.

  3. Draft the query. Write the SQL in a fenced sql block. Conventions:

    • Always include the time window in a WHERE clause — never query the full history "just in case".
    • Always SELECT an explicit column list — never SELECT * in an answer.
    • Use CTEs (WITH x AS (...)) over nested subqueries for anything beyond two levels of nesting.
    • Comment any non-obvious filter (-- excludes internal test accounts).

  4. Validate before reporting. Run the query and check:

    • Row count is in the ballpark you expected (1 row, 10 rows, ~30 daily buckets, etc.). A surprising row count is almost always a bug — investigate.
    • No NULLs in the grouping column unless that is the intended cohort.
    • At least one numeric sanity check: a known total, a known reference value, or a min/max range that matches reality.
    • If anything looks off, do not report the number — fix the query first.

  5. Write the report. Use this exact structure:

    ## Answer
<one-sentence direct answer with the headline number(s)>

## Result
<small markdown table — at most ~15 rows; for longer results, summarise
and offer to render a chart or attach the full CSV>

## Query
```sql
<the exact query you ran>

    Sources & validation

    • Table(s): schema.table_name (row count, freshness if known)
    • Validation:
    • Caveats: <known data quality issues, missing dates, etc.>

Anti-patterns

  • ❌ Reporting a number without the query that produced it.
  • ❌ Using LIMIT N to "make the output fit" without explaining what got cut.
  • ❌ Reading a single row and reporting it as a trend.
  • ❌ Inventing a table or column name. If unsure, delegate to data-explorer.

When to delegate

  • The user's question requires probing several candidate tables / sources before any query can be written → spawn data-explorer.
  • The user wants a polished written deliverable summarising several analyses (e.g. "weekly health report") → spawn report-writer after you have the underlying numbers ready.