slm-compress
FeaturedCompress large text, tool output, or transcripts to reduce context-window usage while keeping the full 1M window intact — call slm_compress(content, mode, reversible, ttl_seconds) to shrink content; if the result is lossy a ccr_id is returned so you can call slm_retrieve(ccr_id) later to recover the exact original; always fail-open (ok:false → continue with the original).
Install
Quality Score: 87/100
Skill Content
Details
- Author
- qualixar
- Repository
- qualixar/superlocalmemory
- Created
- 7 months ago
- Last Updated
- 1 weeks ago
- Language
- Python
- License
- AGPL-3.0
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
slm-status
Health and optimization stats for SuperLocalMemory — call slm_optimize_stats() for live compression and cache counters (compress_runs, tokens_saved_compress, cache_proxy_hits, cache_proxy_misses, cache_kv_hits, cache_kv_misses); run slm status [--json] for system state (mode, profile, DB size, fact/entity/edge counts) and slm doctor [--json] for preflight including the "Optimize (Surface B)" health line; use together to confirm optimization is actually saving tokens.
compress-context
Compress a given file (CLAUDE.md, skill file, prompt file) into token-efficient symbolic notation. Rewrites the file in-place and maintains a symbols.md legend. Use when asked to compress, reduce tokens, shorten prompts, or make context more efficient.
semantic-compress
Make an LLM-directed document smaller while preserving what it does. Two modes: a local span-level core->pointer pass, and an A/B-validated distill loop that produces the smallest document that behaves the same as the original. Point at core knowledge the model already holds (a concept name activates it); keep project-specific detail explicit and verbatim. TRIGGER when asked to compress, tighten, shorten, or strip a prompt / instruction / system message meant for an LLM; to distill a skill; to compress a whole document; to make this smaller while preserving behaviour; to A/B test a compression or produce a behaviourally-equivalent compression; when an instruction set explains concepts the model already knows from training; or when reducing token cost of an LLM-directed prompt without losing meaning. Not for human-facing prose - that is /deslop.