query-explainer
Query Explainer Protocol
This skill bridges the gap between raw database optimizer output and human-readable performance tuning. It reads EXPLAIN (ANALYZE, BUFFERS) and translates the nodes and costs into concrete actions.
Core assumption: A developer should not need to be a DBA to understand why their query takes 3 seconds and what to do about it.
1. Plan Ingestion & Translation (Static vs Dynamic)
- Default (Static): Analyze based on user-provided EXPLAIN text/JSON outputs.
- Dynamic (On-Demand): Only connect to a live database to execute
EXPLAIN (ANALYZE, BUFFERS)directly if the user explicitly authorizes it and provides the target query. - When evaluating the plan structurally:
- Cost vs Actual: Differentiate between planner estimates (
cost=0.00..10.00) and reality (actual time=0.015..0.020). - Data Volume: Note discrepancies between
rows=1000000(estimated) andloops=1actual rows fetched (this indicates bad statistics).
- Cost vs Actual: Differentiate between planner estimates (
2. Identify Bottleneck Nodes
Highlight the most expensive parts of the query:
- π Sequential Scans (Seq Scan): Acceptable on tiny tables. Disastrous on millions of rows.
- π Nested Loops over many rows: Indicates missing indexes on the joined columns.
More from fatih-developer/fth-skills
task-decomposer
Break down large, complex, or ambiguous tasks into independent subtasks with dependency maps, execution order, and success criteria. Plan first, then execute step by step. Triggers on 'how should I do this', 'where do I start', 'plan the project', 'break it down', 'implement' or whenever a task involves multiple phases.
24multi-brain-debate
Two-round debate protocol where perspectives challenge each other before consensus. Round 1 presents independent positions, Round 2 allows counter-arguments and rebuttals. Produces battle-tested decisions for high-stakes choices.
20context-compressor
Compress long conversation histories, large code files, research results, and documents by 70% without losing critical information. Triggers when context window fills up, when summarizing previous steps in multi-step tasks, before loading large files into context, or on 'summarize', 'compress', 'reduce context', 'save tokens'.
18multi-brain-score
Confidence scoring overlay for multi-brain decisions. Each perspective rates its own confidence (1-10) with justification. Consensus uses scores as weights, flags low-confidence areas, and surfaces uncertainty explicitly.
16checkpoint-guardian
Automatic risk assessment before every critical action in agentic workflows. Detects irreversible operations (file deletion, database writes, deployments, payments), classifies risk level, and requires confirmation before proceeding. Triggers on destructive keywords like deploy, delete, send, publish, update database, process payment.
14multi-brain
Evaluate complex requests from 3 independent perspectives (Creative, Pragmatic, Comprehensive), reach consensus, then produce complete outputs. Use for architecture decisions, creative content, analysis, and any task where multiple valid approaches exist.
14