ariffazil/FORGE-data-compression
skills/FORGE-data-compression/SKILL.md
Context-window-aware data compression for long logs, code dumps, and multi-source evidence bundles. Compresses input to fit within model context limits while preserving semantic fidelity (F2 TRUTH ≥ 0.95). Supports streaming chunk-and-summarize pipelines. USE WHEN: "compress this log", "summarize before context window", "fit this in context".
$ install --global
skillsauthnpx skillsauth add ariffazil/openclaw-workspace FORGE-data-compressionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Aug 1, 2026, 2:14 AM136.9s1 file scanned
SKILL.md
- name:
- FORGE-data-compression
- description:
- Context-window-aware data compression for long logs, code dumps, and
- pipelines. USE WHEN:
- compress this log", "summarize before context window", "fit
- version:
- 1.0.0
- owner:
- A-FORGE
FORGE-data-compression
Purpose
Long logs, massive codebases, and multi-organ evidence bundles routinely exceed model context windows. This skill compresses input through a staged pipeline while maintaining ≥0.95 semantic fidelity.
Pipeline
- Chunk — Split input into semantic units (log blocks, code functions, evidence sections)
- Score — Rank chunks by relevance to the query/task context
- Compress — Apply lossy compression to low-relevance chunks, lossless to high-relevance
- Reconstruct — Reassemble into compressed artifact with provenance markers
- Verify — F2 TRUTH check: decompress sample and compare against original
Compression Modes
log-compress: Strip timestamps, deduplicate repeated lines, extract error/warn signalscode-compress: Collapse boilerplate, preserve signatures + logic + commentsevidence-compress: Rank by epistemic rung, compress low-confidence sectionsstreaming: Chunk-summarize-recurse until target token count reached
Floors
- F2 TRUTH: ≥ 0.95 fidelity. Compression must not fabricate or distort meaning.
- F4 CLARITY: Compressed output must be more readable than raw input.
- F7 HUMILITY: Report compression ratio and what was lost.
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