190 件 ・ 関連度順
概要と使いどころ
Plan, audit, and post-process PFC 5.0 asphalt calibration tests, including compression, diametral loading, direct tension, creep-recovery, test geometry, histories, peak metrics, stiffness, and fistPkg handoff.
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Integrate an external, licensed Itasca PFC 5.0 fistPkg26 tree as a reproducible material-generation and compression/diametral/tension test baseline; use to validate its PFC5.0 layout, copy a private working case, preserve provenance, and audit ck/ct/dc/tt/ft extension points for asphalt workflows.
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Use when loading glTF/GLB models or textures in PlayCanvas, choosing Draco or Basis/KTX2 compression, or setting up decoders and preloading.
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HTTP/2 protocol-specific attack playbook. Use when the target supports HTTP/2 and you need to exploit binary framing, HPACK compression, h2c upgrade smuggling, pseudo-header injection, stream multiplexing abuse, or H2→H1 downgrade translation flaws.
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Client-side image handling - preview generation, Canvas API resizing, compression, EXIF orientation, format conversion, memory management with object URL cleanup
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Section-by-section media planning and generation. Image generation (GPT Image 2 primary, built-in fallback), logo/icon generation (Ideogram v3 → favicon set), video generation (Veo 3.1), social preview images (OG 1200x630 + AI search optimization), stock photo curation (Pexels, Pixabay), critique/remix loops (max 3 rounds), asset compression pipeline, and media performance budgets.
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Persistent token-compression mode (~75% reduction) — now always-on by default. Strips filler while keeping code exact.
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Hunts for pre-exfiltration data staging and archiving by detecting archive-tool execution (rar, 7z, zip, tar), large compressed files written to staging directories, and collection into a single location before transfer. Activates for requests to hunt data staging, detect pre-exfil archiving, or find collection and compression before exfiltration.
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Detects packing and encryption by measuring Shannon entropy across a binary's sections and regions: flagging high-entropy executable sections, entropy spikes, and size/raw-vs-virtual anomalies that indicate compression or encryption. Activates for requests to measure entropy, detect packing, or assess whether a sample is compressed/encrypted.
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Use when a task needs the judgment of a Commercial Diver — planning a surface-supplied or SCUBA-mode dive against no-decompression limits, sizing a bailout/reserve gas supply before descent, choosing wet vs. dry (habitat) underwater welding for a repair, scoping an underwater NDT inspection, or deciding whether to abort a dive against a safety threshold.
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Transform reference photographs and visually dense images into sparse, recognizable, high-saturation flat-vector-style illustrations through semantic compression rather than literal tracing. Use when the user asks for photo-to-flat illustration, photo-to-vector-style redraw, 照片转扁平插画, 无渐变高饱和插画, iconification, visual simplification, composition-preserving abstraction, a consistent minimal illustration set, or revisions that remove gradients, glow, texture, blur, clutter, text, logos, numbers, or UI. Default to human-perceived semantic complexity 6 out of 10 or lower, fixed-HSB solid fills, crisp boundaries, and no gradients or light-halo effects.
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**WORKFLOW SKILL** — Persist analysis so it survives context compression and chains between tasks. USE FOR: any task whose decisions affect future work — features, refactors, architecture changes, integrations. Produces a lightweight Task Analysis Map that externalizes intent, decisions, and chain context. Use to document decisions, create continuity, prevent context loss, bridge tasks, externalize reasoning. DO NOT USE FOR: quick intent validation (use task-intent), deep structured analysis (use contextação).
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Create a bounded, extractive, structurally idempotent rollup of recent Obsidian wiki log entries, with dry-run preview by default and one optional transaction apply. Use for manual log compression without modifying child pages. Triggers: fold the log, run a fold, run wiki-fold, log rollup, roll up log entries, commit the fold.
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Review Caveman Cloud evidence read-only: costs, Cave Score, Cave Plan, workflows, traces, latency, errors, compression, routing, and verified savings. Use when the user asks what Caveman found, where LLM spend goes, why cost or quality changed, which workflows need attention, or asks for a trace or analytics review. Prefer Caveman MCP tools; fall back to CLI JSON.
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Configure a CDN with optimized caching, SSL/TLS, security headers, and cache invalidation — auto-detects hosting provider and app type, generates CloudFront, Cloudflare, or Vercel config with per-content-type cache rules, edge functions, and compression. Use when deploying a static site, SPA, SSR app, or adding a CDN layer to an existing API.
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Audits and implements mandatory image resizing and compression for all uploaded user images to reduce storage costs while preserving visual quality.
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name: 4d-compression-core version: 1.0.2 description: "把长内容压缩成结构化向量——节省 60-80% Token，保留核心信息" metadata: { "openclaw": { "emoji": "🌀", "requires": { "bins": ["jq", "awk"] }, "triggers": ["压缩", "4d",...
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Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity.
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Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.
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Deep analysis of knowledge distillation techniques for compressing large models into smaller efficient ones
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Master FFmpeg for video/audio conversion, compression, trimming, subtitles, streaming, and batch processing.
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Lossless LLM-optimized compression of source documents. Use when the user requests to 'distill documents' or 'create a distillate'.
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Formato GGUF e quantização llama.cpp para inferência eficiente em CPU/GPU. Use ao implantar modelos em hardware de consumidor, Apple Silicon, ou quando precisar de quantização flexível de 2-8 bits sem requisitos de GPU.
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Quantização Half-Quadratic para LLMs sem dados de calibração. Use ao quantizar modelos com precisão 4/3/2-bit sem necessidade de conjuntos de calibração, para workflows de quantização rápida, ou ao fazer deploy com vLLM ou HuggingFace Transformers.
日本語の概要は準備中です。原文の説明を表示しています。