windagszip

license	Apache-2.0
name	windagszip
description	This skill should be used when a SKILL.md file needs compression, deduplication, or token reduction. It provides an embedding-based compression pipeline that detects and removes redundant chunks within SKILL.md files using local embeddings (all-MiniLM-L6-v2). Two-pass approach: (1) free intra-skill deduplication via cosine similarity clustering, (2) optional LLM-judged graded eval to detect pretraining overlap. Typical result: 25-46% token reduction with zero quality loss. This skill is not intended for editing skill content, creating new skills, routing optimization, or cross-skill deduplication.
category	Agent & Orchestration
tags	["windags","packaging","distribution","compression","deployment"]
allowed-tools	Read,Write,Edit,Bash(python:*),Bash(pip:*),Glob,Grep
user-invocable	true
pairs-with	[{"skill":"skill-creator","reason":"Compress skills after creation to reduce context window cost"},{"skill":"skill-architect","reason":"Analyze skill structure to inform compression targets"},{"skill":"next-move","reason":"Include compression as a step in skill development workflows"}]

/windagszip — Skill Compression via Embeddings

Analyze SKILL.md files for redundancy and produce compressed variants that preserve behavioral quality while cutting token count.

Two types of redundancy exist in skills:

1. Intra-skill duplication — chunk A says the same thing as chunk B within the same skill. Detected by embedding cosine similarity. Cost: zero.
2. Pretraining overlap — chunk teaches what Claude already knows from training data. Detected by LLM-judged quality eval. Cost: ~$0.011/test case.

Always run embeddings first (free), then graded eval on survivors (targeted).

---

When to Activate

• Compressing a SKILL.md to reduce context window consumption
• Analyzing redundancy patterns in one or many skills
• Generating ablation variants for quality testing
• Identifying which chunks carry unique signal vs. duplicate other chunks
• Pre-filtering before expensive graded eval

---

Compression Pipeline

flowchart LR
  A[Chunk<br/>12 types] --> B[Embed<br/>384-dim]
  B --> C[Similarity<br/>Matrix]
  C --> D[Cluster<br/>BFS components]
  D --> E[Variants<br/>per-cluster + max]
  E --> F{Graded Eval?}
  F -->|Free pass| G[Ship compressed]
  F -->|Quality check| H[LLM Judge<br/>sonnet + haiku]
  H --> G

Step 1: Analyze Redundancy

All scripts live in tools/skill-compression/ relative to the WinDAGs repo root.

python tools/skill-compression/embed_ablate.py <skill-name>

Output shows redundancy clusters — groups of chunks saying the same thing:

Cluster 1 (avg sim: 0.847, redundant tokens: 2,127)
  KEEP [reference   ] 1821tk  Full CSS layering reference...
  CUT  [code_block  ]  312tk  .aurora-container { position: absol...
  CUT  [code_block  ]  287tk  .atmosphere-layer { position: absol...

KEEP = canonical version (most complete). CUT = duplicates the canonical.

Step 2: Generate Compressed Variants

python tools/skill-compression/embed_ablate.py <skill-name> --generate

Produces per-cluster variants (remove one cluster's redundancy) and a max-compression variant (remove all redundancy). Output in ablations/<skill-name>/.

Step 3: Validate Quality (Optional)

If a test suite exists for the skill:

# Verify pipeline with 1 test case
python tools/skill-compression/eval_judge.py <skill-name> --


python tools/skill-compression/eval_judge.py <skill-name> --top-n 5


python tools/skill-compression/eval_judge.py <skill-name> --all


python tools/skill-compression/eval_judge.py <skill-name> --analyze