SQL Optimization Skill

Workflow is evidence-driven: collect evidence with tools, then recommend based on evidence only.

Pre-flight Check

1. HAS SQL: Conversation contains a SQL query -> Go to WORKFLOW step 2 (Collect Evidence).
2. HAS QUERY_ID: Conversation contains query_id -> Go to WORKFLOW step 2 (Call collect_sql_optimization_evidence immediately).
3. DISCOVERY REQUEST: User asks to optimize the slowest/heaviest queries but does not provide SQL/query_id -> Go to WORKFLOW step 1 (Discovery).
4. NEITHER: Output ONLY a concise 1-sentence request for the SQL query or query_id (e.g. "Please provide the SQL query or query_id you'd like to optimize."). Do NOT ask for any other details (like version, table sizes, etc.). Then include the following UI trigger block in the response (must be present and unchanged; place it at the end of the reply):

{ "type": "optimization_skill_input" }

Discovery

• Prefer search_query_log for discovery from system.query_log (slowest, most expensive, user-scoped, database-scoped, text-scoped, etc.).
• If search_query_log cannot express the request, then load the clickhouse-system-queries skill, immediately call skill_resource for references/system-query-log.md, and follow that reference strictly.
• Do NOT write ad-hoc SQL against system.query_log from this skill when search_query_log can satisfy the request.
• Extract query_id from the discovery results for the next step (evidence collection).

Time Filtering

• time_window: Relative minutes from now (e.g., 60 = last hour).
• time_range: Absolute range { from: "ISO date", to: "ISO date" }.
• When calling collect_sql_optimization_evidence after discovery, you MUST pass the same time_window or time_range used in discovery.

Mode Selection

• Default collect_sql_optimization_evidence to light mode for the first pass.
• Prefer omitting the mode argument entirely unless full detail is required.
• Use mode: "full" only when the user explicitly asks for detailed/raw evidence or the light pass is insufficient.
• Do not choose full just because the request says "optimize", "analyze", or "investigate".

Workflow

1. Discovery (if needed): Prefer search_query_log to find candidates. If the request exceeds the tool's schema, then load clickhouse-system-queries, load references/system-query-log.md via skill_resource, and use that reference. Extract query_id from the results.
2. Collect Evidence: Call collect_sql_optimization_evidence with query_id (preferred) or sql (and same time params if coming from discovery).
3. Analyze: Review evidence for optimization opportunities.
4. Recommendations: Rank by Impact/Risk/Effort. Prefer low-risk query rewrites first.
5. Validate: Use validate_sql for any proposed SQL changes. Add inline comments (-- comment) to highlight key changes.

Table Schema Evidence

• Use table_schema fields: columns, engine, partition_key, primary_key, sorting_key, secondary_indexes.
• When optimization_target is present, treat it as the real local-table schema behind a Distributed table and base key/index recommendations on it.
• Suggest secondary indexes only when evidence shows frequent WHERE filters on selective columns and the index type fits the predicate.
  • Use minmax for range predicates on sorted columns.
  • Use set for low-cardinality equality filters.
  • Use bloom_filter for high-cardinality equality filters (e.g., trace_id, user_id).
  • Use tokenbf_v1 for frequent token-based text search.

Rules

• Do NOT recommend based on assumptions. If evidence is missing, collect it with tools.
• If tools return NO meaningful evidence, output only a brief 3-5 sentence message explaining what's missing.
• Always validate proposed SQL with validate_sql before recommending.
• If discovery results include both query text and query_id, prefer query_id to avoid truncation issues.
• If the SQL appears incomplete (truncated/ellipsized/ends mid-clause), use query_id instead of sql.
• When both query_id and SQL are available, prefer query_id to reduce tokens and avoid truncation issues.