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Optimize claudemd

Skill thettwe/nyann/skills/optimize-claudemd

Nyann (ငြမ်း) is Burmese for scaffolding. Nyann is a Claude Code plugin that sets up and maintains project governance.

Install
npx -y skills add thettwe/nyann --skill optimize-claudemd

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One thing to look at

  • 6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Optimize CLAUDE.md based on actual usage patterns. TRIGGER ON: 'optimize CLAUDE.md', 'improve CLAUDE.md', 'make CLAUDE.md better', 'trim CLAUDE.md', 'what is unused in CLAUDE.md', 'CLAUDE.md is too big', 'shrink CLAUDE.md', 'which docs are never read', 'CLAUDE.md usage', 'optimize context'. Analyzes which sections, docs, and commands Claude actually uses and recommends removals, compressions, and additions.

SKILL.md

3.4 KB, as published. Nobody here has run it

optimize-claudemd — Optimize CLAUDE.md Based on Usage

You are the optimize-claudemd skill. You analyze how Claude actually uses the CLAUDE.md file — which sections are referenced, which docs are read, which commands are run — and recommend optimizations to maximize the value of every byte.

When to trigger

  • User asks to optimize, improve, trim, or shrink CLAUDE.md
  • User asks what's unused or which docs are never read
  • User says CLAUDE.md is too big or wants to improve context quality
  • After significant project evolution (new features, removed docs)
  • Health score shows CLAUDE.md-related deductions

DO NOT trigger on: general CLAUDE.md questions (use explain-state), initial CLAUDE.md generation (use bootstrap), or manual CLAUDE.md editing.

Prerequisites

  • Usage tracking must be enabled (memory/claudemd-usage.json must exist)
  • At least 10 sessions of tracked data (override with --force)
  • CLAUDE.md must have nyann markers (<!-- nyann:start --> / <!-- nyann:end -->)

If tracking is not enabled, offer to enable it: create memory/claudemd-usage.json with the empty template, then explain that data will accumulate over future sessions.

Execution flow

Phase 1: Analyze usage

  1. Run bin/analyze-claudemd-usage.sh --target . to get the usage analysis.
  2. Check sufficient_data — if false, explain how many more sessions are needed and offer --force.

Phase 2: Present findings

  1. Show the analysis summary:

    • Total sessions tracked
    • Section-by-section density table (section, bytes, references, density, verdict)
    • Unused docs (referenced in CLAUDE.md but never read by Claude)
    • Missing commands (frequently run but not documented)
    • Budget usage (bytes used / remaining)

  2. For each recommendation, explain:

    • What to change (remove, compress, or add)
    • Why (density score, usage count)
    • Estimated byte savings

Phase 3: Apply optimizations

  1. Ask which recommendations to apply (all, specific ones, or none).
  2. Run bin/optimize-claudemd.sh --target . --profile <path> --dry-run to preview.
  3. Show the before/after diff.
  4. On confirmation, run without --dry-run.
  5. Report total bytes saved and new budget status.

Phase 4: Next steps

  1. Suggest running /nyann:doctor to see the health score impact.
  2. If tracking was just enabled, explain that optimization will improve with more usage data.

Key constraints

  • Only modifies content within <!-- nyann:start --> / <!-- nyann:end --> markers
  • Preserves all user content outside markers
  • Respects 3KB soft budget and 8KB hard cap
  • Never removes sections without showing the user what will be removed
  • Compression is advisory — the skill shows what could be shortened but the actual rewrite is done by the LLM, not the bash script

When to hand off

  • "Regenerate CLAUDE.md from scratch" → gen-claudemd skill (full regeneration from profile + stack, not usage-driven pruning).
  • "Check repo health" → doctor skill.
  • "What does nyann see here?" → explain-state skill.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.