Agent-Skills.md

Agent Skills: Context Compactor Skill

Analyzes and compacts context-affecting documents (project memory, CLAUDE.md, skill files) to reduce token usage and compaction frequency. Manual trigger only. Always requires user approval before making changes.

UncategorizedID: fubira/claude-code-settings/context-compactor
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Install this agent skill to your local

pnpm dlx add-skill https://github.com/fubira/claude-code-settings/tree/HEAD/skills/context-compactor

Skill Files

Browse the full folder contents for context-compactor.

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skills/context-compactor/SKILL.md

Skill Metadata

Name
context-compactor
Description
Analyzes and compacts context-affecting documents (project memory, CLAUDE.md, skill files) to reduce token usage and compaction frequency. Manual trigger only. Always requires user approval before making changes.

Context Compactor Skill

Analyze and compact documents that affect context window size. Reduce token usage while preserving essential information.

Activation Triggers

Manual only — never auto-activate.

  • /compact-context — run full analysis
  • "コンテキストを整理して", "compact context", "reduce context size"

Target Files

| Target | Path Pattern | Description | |--------|-------------|-------------| | Project Memory | .claude/projects/*/memory/*.md | Auto-memory files that grow over time | | Project CLAUDE.md | {project}/CLAUDE.md | Project-specific instructions | | Global CLAUDE.md | ~/.claude/CLAUDE.md | Global instructions | | Skill files | ~/.claude/skills/*/SKILL.md | Skill definitions |

Workflow

Phase 1: Measure

  1. Count lines and estimate tokens for each target file
  2. Present a summary table with file sizes
  3. Identify the largest contributors to context

Phase 2: Analyze

For each target file, detect:

  • Redundancy: Content duplicated across files (e.g., CLAUDE.md vs skills, memory vs CLAUDE.md)
  • Obsolescence: Outdated information (completed tasks, old decisions, superseded patterns)
  • Verbosity: Content that can be expressed more concisely without losing meaning
  • Language inefficiency: Japanese text in technical/process docs where English would use fewer tokens

Phase 3: Propose

Present findings as a structured table:

| File | Current | Est. After | Reduction | Changes |
|------|---------|-----------|-----------|---------|
| ... | ... | ... | ... | Brief description |

For each file with proposed changes, show:

  • What will be removed/compacted (with reasoning)
  • What will be preserved (and why)

Wait for user approval before proceeding.

Phase 4: Execute (after approval)

  • Apply approved changes only
  • Show before/after line counts
  • Suggest committing if in a git repo

Analysis Rules

MUST preserve

  • User-specified behavioral instructions (personality, output style, preferences)
  • Active project conventions and standards
  • Security-related rules and constraints
  • Information that exists nowhere else (sole source of truth)

Safe to compact

  • Duplicated content (keep in the most appropriate location)
  • Verbose explanations of standard practices (LLMs already know these)
  • Completed/obsolete memory entries
  • Boilerplate sections repeated across files

Requires user judgment

  • Content the user may consider important even if technically redundant
  • Trade-offs between token savings and clarity
  • Whether to convert Japanese → English for token efficiency

Important Notes

  • Never delete without showing what will be removed
  • Preserve meaning even when reducing words
  • When in doubt, keep — false deletion is worse than slight verbosity
  • This skill does NOT auto-activate — context growth is normal and expected