Manage what enters and stays in the context window — pruning, compaction, summary fidelity, ordering — so the agent stays coherent on long runs without inflating cost. Use when the user is hitting context limits, running long agentic loops, paying for full-history replays, or asks "how do I keep context manageable?" /…
Use before starting a long or expensive task (a large refactor, a change across many files, a long agent loop, a big test or eval run, a batch of LLM calls), and when asked how much AI budget is left.
Audit SQL for the cost & performance anti-patterns that burn warehouse credits. Catches SELECT , full-table scans, non-sargable predicates, Cartesian joins, NULL-trap NOT IN, and 17 more rules. Scores warehouse health 0-100 and outputs a prioritized cost-reduction plan for BigQuery, Snowflake, Redshift, and Postgres.
★not rated 1 2mo agoA77 tokens
originalMIT
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