Results for “context-clash”

Breaks down current state, context, user input, and data into a structured assessment before taking action.

Extends effective context capacity through strategic compression, masking, caching, and partitioning techniques.

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

Diagnose whether an AI workflow suffers from context stuffing or benefits from context engineering, and apply structured techniques to improve reliability.

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

Incident response mode — diagnose a production issue, write a postmortem, add follow-up tasks to TODO.md. Use when user says "/incident [description]".

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

Suggests manual context compaction at logical intervals to preserve context through task phases rather than relying on arbitrary auto-compaction.

Optimizes agent context setup by structuring rules, specs, source files, error output, and conversation history to improve output quality.

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

Selects a minimal, non-redundant set of facts, artifacts, skills, and references for a working decision when context is stale, uncertain, or over budget.

Uses the filesystem as a durable overflow layer for agent context, enabling scratch pads, plan persistence, sub-agent communication, dynamic skill loading, and log management.

Rebuilds working context after a session clear by reading git history, plans, and beads issue state, then produces a visible briefing.