What problem does it solve? Long-running agent sessions generate millions of tokens of conversation history that exceed context window limits, and naive compression loses critical details like file paths, error messages, and decision rationale, forcing costly re-exploration. ## Core Features & Use Cases - Anchored Iterative Summarization: Maintains persistent structured summaries with explicit sections for session intent, file modifications, decisions, and next steps, merging new content incrementally instead of regenerating from scratch. - Probe-Based Evaluation: Generates recall, artifact, continuation, and decision probes from conversation history, then scores responses across six dimensions (accuracy, context awareness, artifact trail, completeness, continuity, instruction following) using an LLM judge rubric. - Compression Method Selection: Provides calibrated guidance on anchored iterative, opaque, and regenerative compression approaches with benchmarked compression ratios and quality scores. - Use Case: A coding agent debugging a 401 authentication error across 178 messages hits the context limit; the skill compresses the history into a structured summary preserving the root cause, modified files, and failing tests, then validates quality with probes before discarding the original history. ## Quick Start Ask the agent to compress the current conversation history into a structured summary and evaluate whether critical file modifications and decisions survived compression.
