192 件 ・ 関連度順
概要と使いどころ
Native agent-to-agent language for compact multi-agent messaging. A shared tongue agents speak directly, not a translation layer. 340+ atoms across 7 domains; 3x smaller than natural language.
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。
Create ZIP and TAR archives programmatically with Archiver — bundle files, directories, and streams into compressed archives with progress tracking. Use when tasks involve generating downloadable bundles, backing up directories, creating deployment packages, or building export features that zip multiple files together.
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Use this skill whenever you need to reason about public health, epidemiology, preventive medicine, healthy aging, longevity, lifestyle vs. genetics, or dietary patterns. Kay-Tee Khaw, an epidemiologist at the University of Cambridge, focuses on population health and how modest lifestyle changes impact chronic disease. Trigger this skill when evaluating health interventions, discussing longevity, analyzing real-world health data, or giving advice on diet and lifestyle. Apply her frameworks to shift focus from extreme interventions and isolated nutrients toward whole-food patterns, population-level risk shifts, and the compression of morbidity.
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Applies the reasoning, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.
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。
Suede-owned marketing image production for generation prompts, hero and social graphics, product mockups, export sizing, compression, and preview assets. Use when the user needs a general-purpose marketing image or an image-production workflow. NOT FOR: paid-ad creative systems (use suede-ad-creative), video production (use suede-video), or app-store listing strategy (use suede-aso).
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Use when adding, importing, moving or loading textures, models, audio or scenes - per-platform compression, atlases, LODs, async loading per engine, engine metadata files (.meta, .import, .uid, .uasset), Git LFS, and asset validation tests.
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。
All-in-one ComPDF workflow for document conversion, OCR, data extraction, PDF editing, protection, compression, and watermarking. Use for any ComPDF Server API request that processes PDF, Office, HTML, CSV, RTF, TXT, or image files.
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。
Compress PDF files with ComPDF while balancing file size and readable visual quality. Use for PDF optimization, resolution control, compression strategies, and removal of non-essential document content.
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。
I-Lang compression engine. All internal planning uses I-Lang v5.0 syntax. User never sees compressed output.
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。
Audit a .glb or .gltf and make it small, correct, and fast to load in a browser, with a measured before/after report. Use when a model is "too big" or "slow to load", when the user asks to "optimize this GLB", "compress this glTF", or "export for web", or when they mention Draco, meshopt, gltfpack, KTX2, Basis, texture VRAM, draw calls, or a Blender / CAD / photogrammetry / Pascal export headed for Three.js, React Three Fiber (drei useGLTF), Babylon.js, or model-viewer. Covers inspection, spec validation, geometry and texture compression, scene-graph cleanup, and the correctness checks (metres, Y-up, node names, animations, PBR fidelity, alpha modes, vertex colors) that must not regress. Not for editing or modelling geometry, authoring or laying out a scene, generating 3D from text or images, converting from FBX/OBJ/CAD, or debugging a runtime frame rate that has nothing to do with asset size.
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。
Design cost reduction strategies — model tiering, prompt compression, caching, batch inference. Use when asked to "reduce our AI costs", "set up model tiering", or "cut LLM spend".
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。
Audio engineering — mastering, mixing, EQ, compression, loudness standards, synthesis, podcast production, music theory, spectrum analysis.
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。
Use before you shorten, compress, or rewrite the BODY of an existing SKILL.md or agent .md — the moment you are about to cut text to reduce length. First inventory the file's operational anchors (exact code / command / API snippets, fail-closed workflow guards, and rule / threshold statements), preserve every one, then judge the rewrite by expected downstream task cost (exploration, debugging, recovery tokens) rather than by the resulting line count. A shorter skill that strips an anchor makes the agent MORE expensive per task, so length is never the objective and rewrite is not compression. Backed by Preserving Operational Anchors When Compressing Agent Instructions (arxiv 2606.09421v2). Triggers on shortening or refactoring a skill or agent body.
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。
Cognitive superposition synthesizing Riehl (∞-categories), Sutskever (compression), Schmidhuber (curiosity-driven), and Bengio (GFlowNets) into unified ASI framework with quantum-inspired measurement collapse.
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Unified code intelligence — Skeleton Index (zero-dep, <4s) + AST knowledge graph (CodeGraph) + architecture diagrams (Mermaid) + smart context builder. Pre-indexes code structure so AI agents understand any codebase instantly. 95% token compression for onboarding. 30% fewer tokens for deep analysis.
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BorgBackup (Borg) is a deduplicating backup program with optional compression and authenticated encryption. It uses content-defined chunking for space-efficient daily backups, making it ideal for automating secure incremental backups to local or remote SSH targets.
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。
Session lifecycle management. Parent skill for session-related skills: learning (pattern extraction) and compact (context compression).
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。
Strategic compaction skill: suggests context compression at logical breakpoints. Use to suggest compaction at logical intervals during development sessions.
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。
Use Tokenless for large files and noisy tool outputs before they enter context. Must be used when commands, Read output, diffs, logs, or search results are large.
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。
Token-range severity zones (Green/Yellow/Orange/Red/Critical) with detection checklist, early warning indicators, and corrective routing actions for context window degradation
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。
AI internal coordination using stigmergic signals — managing information freshness in context and memory, decay rates for assumption staleness, and emergent coherent behavior from simple local protocols. Use during complex tasks where multiple sub-tasks must coordinate, when context has grown long and information freshness is uncertain, after context compression when information may have been lost, or when sub-task outputs need to feed into each other cleanly without degradation.
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。
Use when the user says "simplify this diff" or asks for a compression pass over a change-set. Not for dead-code sweeps: use deslop.
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当用户直接要求进行数据集压缩测评，或在量化调优过程中需要使用 AISBench 评测工具输出数据集精度等场景时使用。 本 Skill 负责：使用 AISBench 数据集压缩代码、用 RBF Kernel Herding 算法生成压缩后的数据子集（coreset），并按用户目标交付两种产物之一： ① 接入量化调优主流程：产出独立的 coreset 数据集配置（不覆盖原始数据），回传「全集 config_name + coreset config_name」给编排层，供 evaluation.yaml 切换； ② 仅数据集压缩测评（不做量化调优）：基于 example 模板生成「全集测试脚本」和「子集测试脚本」两份 shell 脚本。 暂时支持 AIME 2025 和 GPQA，其他数据集需要再结合 aisbench tools目录下 herding_coreset_selector 源码进行分析。
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。
Manage AI context windows with compression. TRIGGERS - Use when user needs help with ai-context-window-manager related tasks.
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。