L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Productivité · SKILL.md

Compress

Compress

Compress text semantically with iterative validation, anchor checksums, and verified information preservation.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: Compress slug: compress version: 1.0.0 description: Compress text semantically with iterative validation, anchor checksums, and verified information preservation. homepage: https://clawic.com/skills/compress metadata: clawdbot: emoji: 🗜️ displayName: Compress

⚠️ Important Limitations

This is SEMANTIC compression, not bit-perfect lossless.

  • L1-L2: Verified reconstruction, production-ready
  • L3-L4: Experimental, may lose subtle information
  • Never use for: Medical dosages, legal text, financial figures, safety-critical data

The Validation Loop

1. Compress original O → compressed C
2. Extract anchors from O (entities, numbers, dates)
3. Reconstruct C → R (without seeing O)
4. Verify: anchors match + semantic diff
5. If mismatch → refine C with missing info
6. Repeat until validated (max 3 iterations)

Convergence = verified. No convergence after 3 rounds = level too aggressive.


Quick Reference

TaskLoad
Compression levels (L1-L4)levels.md
Validation algorithm detailsvalidation.md
Format-specific strategiesformats.md
Token budgeting and metricsmetrics.md

Compression Levels

LevelRatioReliabilityUse Case
L1~0.8x✅ HighProduction, human-readable
L2~0.5x✅ GoodSystem prompts, repeated use
L3~0.3x⚠️ ModerateExperimental, review output
L4~0.15x⚠️ LowResearch only, expect losses

Anchor Checksum System

Before compression, extract critical facts:

[ANCHORS: 3 people, $42,000, 2024-03-15, "Project Alpha"]

Reconstruction MUST reproduce these exactly. If anchors mismatch → compression failed.


Core Rules

  1. Always validate — Never trust compression without reconstruction test
  2. Use anchors — Extract numbers, names, dates before compressing
  3. Cap at L2 for production — L3-L4 are experimental
  4. Report confidence — Include iteration count and anchor match rate
  5. Independent verification — Consider different model for reconstruction

Cost-Benefit Reality

Each compression costs 3-4 LLM calls. Break-even calculation:

break_even_retrievals = compression_tokens / saved_tokens_per_use

Only cost-effective if: You'll retrieve the compressed content 6-8+ times.

For one-time use → just use the original text.


Before Compressing

  • Content type is NOT safety-critical
  • Target level chosen (L1-L2 recommended)
  • Anchors identified (numbers, names, dates)
  • ROI makes sense (multiple retrievals expected)
Skills enregistrées