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Compress

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

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High Signal

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

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, formats.md, levels.md, metrics.md, validation.md

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

Download the package from Yavira.
Extract it into a folder your agent can access.
Paste one of the prompts below and point your agent at the extracted folder.

New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: ivangdavila
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 8 sections Open source page

⚠️ 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

TaskLoadCompression levels (L1-L4)levels.mdValidation algorithm detailsvalidation.mdFormat-specific strategiesformats.mdToken budgeting and metricsmetrics.md

Compression Levels

LevelRatioReliabilityUse CaseL1~0.8x✅ HighProduction, human-readableL2~0.5x✅ GoodSystem prompts, repeated useL3~0.3x⚠️ ModerateExperimental, review outputL4~0.15x⚠️ 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

Always validate — Never trust compression without reconstruction test Use anchors — Extract numbers, names, dates before compressing Cap at L2 for production — L3-L4 are experimental Report confidence — Include iteration count and anchor match rate 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)

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

5 Docs

SKILL.md Primary doc
formats.md Docs
levels.md Docs
metrics.md Docs
validation.md Docs