{ "schemaVersion": "1.0", "item": { "slug": "compress", "name": "Compress", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/ivangdavila/compress", "canonicalUrl": "https://clawhub.ai/ivangdavila/compress", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/compress", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=compress", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "SKILL.md", "formats.md", "levels.md", "metrics.md", "validation.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "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." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/compress" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/compress", "agentPageUrl": "https://openagent3.xyz/skills/compress/agent", "manifestUrl": "https://openagent3.xyz/skills/compress/agent.json", "briefUrl": "https://openagent3.xyz/skills/compress/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "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." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "⚠️ Important Limitations", "body": "This is SEMANTIC compression, not bit-perfect lossless.\n\nL1-L2: Verified reconstruction, production-ready\nL3-L4: Experimental, may lose subtle information\nNever use for: Medical dosages, legal text, financial figures, safety-critical data" }, { "title": "The Validation Loop", "body": "1. Compress original O → compressed C\n2. Extract anchors from O (entities, numbers, dates)\n3. Reconstruct C → R (without seeing O)\n4. Verify: anchors match + semantic diff\n5. If mismatch → refine C with missing info\n6. Repeat until validated (max 3 iterations)\n\nConvergence = verified. No convergence after 3 rounds = level too aggressive." }, { "title": "Quick Reference", "body": "TaskLoadCompression levels (L1-L4)levels.mdValidation algorithm detailsvalidation.mdFormat-specific strategiesformats.mdToken budgeting and metricsmetrics.md" }, { "title": "Compression Levels", "body": "LevelRatioReliabilityUse CaseL1~0.8x✅ HighProduction, human-readableL2~0.5x✅ GoodSystem prompts, repeated useL3~0.3x⚠️ ModerateExperimental, review outputL4~0.15x⚠️ LowResearch only, expect losses" }, { "title": "Anchor Checksum System", "body": "Before compression, extract critical facts:\n\n[ANCHORS: 3 people, $42,000, 2024-03-15, \"Project Alpha\"]\n\nReconstruction MUST reproduce these exactly. If anchors mismatch → compression failed." }, { "title": "Core Rules", "body": "Always validate — Never trust compression without reconstruction test\nUse anchors — Extract numbers, names, dates before compressing\nCap at L2 for production — L3-L4 are experimental\nReport confidence — Include iteration count and anchor match rate\nIndependent verification — Consider different model for reconstruction" }, { "title": "Cost-Benefit Reality", "body": "Each compression costs 3-4 LLM calls. Break-even calculation:\n\nbreak_even_retrievals = compression_tokens / saved_tokens_per_use\n\nOnly cost-effective if: You'll retrieve the compressed content 6-8+ times.\n\nFor one-time use → just use the original text." }, { "title": "Before Compressing", "body": "Content type is NOT safety-critical\n Target level chosen (L1-L2 recommended)\n Anchors identified (numbers, names, dates)\n ROI makes sense (multiple retrievals expected)" } ], "body": "⚠️ Important Limitations\n\nThis is SEMANTIC compression, not bit-perfect lossless.\n\nL1-L2: Verified reconstruction, production-ready\nL3-L4: Experimental, may lose subtle information\nNever use for: Medical dosages, legal text, financial figures, safety-critical data\nThe Validation Loop\n1. Compress original O → compressed C\n2. Extract anchors from O (entities, numbers, dates)\n3. Reconstruct C → R (without seeing O)\n4. Verify: anchors match + semantic diff\n5. If mismatch → refine C with missing info\n6. Repeat until validated (max 3 iterations)\n\n\nConvergence = verified. No convergence after 3 rounds = level too aggressive.\n\nQuick Reference\nTask\tLoad\nCompression levels (L1-L4)\tlevels.md\nValidation algorithm details\tvalidation.md\nFormat-specific strategies\tformats.md\nToken budgeting and metrics\tmetrics.md\nCompression Levels\nLevel\tRatio\tReliability\tUse Case\nL1\t~0.8x\t✅ High\tProduction, human-readable\nL2\t~0.5x\t✅ Good\tSystem prompts, repeated use\nL3\t~0.3x\t⚠️ Moderate\tExperimental, review output\nL4\t~0.15x\t⚠️ Low\tResearch only, expect losses\nAnchor Checksum System\n\nBefore compression, extract critical facts:\n\n[ANCHORS: 3 people, $42,000, 2024-03-15, \"Project Alpha\"]\n\n\nReconstruction MUST reproduce these exactly. If anchors mismatch → compression failed.\n\nCore Rules\nAlways validate — Never trust compression without reconstruction test\nUse anchors — Extract numbers, names, dates before compressing\nCap at L2 for production — L3-L4 are experimental\nReport confidence — Include iteration count and anchor match rate\nIndependent verification — Consider different model for reconstruction\nCost-Benefit Reality\n\nEach compression costs 3-4 LLM calls. Break-even calculation:\n\nbreak_even_retrievals = compression_tokens / saved_tokens_per_use\n\n\nOnly cost-effective if: You'll retrieve the compressed content 6-8+ times.\n\nFor one-time use → just use the original text.\n\nBefore Compressing\n Content type is NOT safety-critical\n Target level chosen (L1-L2 recommended)\n Anchors identified (numbers, names, dates)\n ROI makes sense (multiple retrievals expected)" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/ivangdavila/compress", "publisherUrl": "https://clawhub.ai/ivangdavila/compress", "owner": "ivangdavila", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/compress", "downloadUrl": "https://openagent3.xyz/downloads/compress", "agentUrl": "https://openagent3.xyz/skills/compress/agent", "manifestUrl": "https://openagent3.xyz/skills/compress/agent.json", "briefUrl": "https://openagent3.xyz/skills/compress/agent.md" } }