1,396 件 ・ 関連度順
概要と使いどころ
Generates optimized read queries using Dapper. Includes multi-mapping for joins, pagination, dynamic filtering, CTEs, and best practices for high-performance data access.
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。
Translation and interpretation as language meta-skills -- equivalence theory (Nida's formal vs. dynamic equivalence), translation strategies (borrowing, calque, transposition, modulation, adaptation), interpretation modes (simultaneous, consecutive, sight), source text analysis, register preservation, cultural adaptation, untranslatability and compensatory strategies, machine translation literacy, and back-translation for verification. Covers literary, technical, legal, and community interpreting contexts. Use when translating between languages, evaluating translation quality, teaching translation skills, or understanding the limits of cross-linguistic transfer.
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。
Walks a user from one rough content idea to a finished post, platform-adapted variants, and repurposed follow-on assets using a 7-step sequential prompting workflow. Use when the user wants to turn a half-formed idea, frustration, story, or client situation into publishable content quickly and consistently — covering idea extraction, hook generation, outline, draft, humanizer pass, platform adaptation (LinkedIn, Reddit, X/Twitter, newsletter, Telegram), and repurpose engine.
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。
Branch skill for building and improving agents. Use when creating new agents, adapting marketplace agents, validating agent structure, writing system prompts, or improving existing agents. Triggers: 'create agent', 'improve agent', 'validate agent', 'fix agent', 'agent frontmatter', 'system prompt', 'adapt agent', 'customize agent', 'agent examples', 'agent tools'.
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。
Decide what to borrow, adapt, or write before building something. Use when the user describes an app, tool, CLI, or feature they want to build and needs to know which parts already exist as open-source GitHub repos (BORROW), which are worth reading and adapting (KITBASH), and which to just write (WRITE). Every repository named is checked against the live GitHub API, so invented ones never reach the user.
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_plc_construction - AI-PLC Stage 3。Agent 定義が要るタスク（RUL_plc_adaptive §6）に対して実行可能なスキル定義と実行計画を生成する。
AI-PLC Stage 3 Construction。Agent 定義が要るタスク（RUL_plc_adaptive §6: complex・platform_builder・慎重モード・委譲・長い実装）に対して実行可能なAgent定義と実行計画を生成する。
Validates adapter files against live platform documentation to detect stale mappings,missing features, and outdated version information
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。
Builds regime detection models with hidden Markov, threshold, and Bayesian change-point methodologies for strategy adaptation. Use when modeling regime changes, detecting market shifts, or adapting strategies to market conditions.
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。
EZMM writer-stage adapter for the vendored PaperOrchestra skill pack. Use by default during pipeline/04-paper-writing for outline-first paper drafting, literature-review planning, section writing, and refinement discipline. The adapter maps EZMM runtime artifacts to PaperOrchestra-style inputs, runs PaperOrchestra-first writing automatically, and returns EZMM-compatible paper.md.
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。
Send, read, and respond to Telegram messages as Claudicle via the @claudicle_bot Bot API. Use this skill when sending Telegram messages (text, Markdown, inline keyboards), building approval gates with tappable buttons, checking the shared inbox for unhandled Telegram messages, answering callback queries, managing Telegram memory context, or checking bot and listener status. Five purpose-built scripts (send, check, status, callback, memory) wrap the python-telegram-bot v22 polling adapter at ~/.claudicle/adapters/telegram/ with REST-based senders (no asyncio). Pairs with /telegram-respond for the cognitive pipeline and with mazkir-callback-watcher.py for the Mazkir approval gate.
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。
Run GGUF models directly, load LoRA adapters, benchmark inference speed, and serve models via llama-server using llama.cpp. Includes Qwen 3.5 serve scripts (9B dense + F16, 35B MoE) with asymmetric KV cache and thinking mode. Secondary to Ollama; use when needing direct model control or LoRA hot-loading. Triggers on 'llama.cpp', 'GGUF', 'LoRA adapter', 'benchmark inference', 'llama-server'.
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 a command, adapter, or runtime smoke depends on proving an agent runtime's capabilities before trusting command routing, writes, shell access, or validation claims.
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。
Observe and assess a garden using a structured sensory protocol adapted from Coordinate Remote Viewing. Covers pre-entry clearing (meditate checkpoint), Stage I gestalt impression, Stage II sensory layer (leaf, stem, root, soil), Stage III pattern recognition with AOL management, and garden health triage matrix (heal checkpoint). Use before any intervention, when plants show stress symptoms, at seasonal transitions, when evaluating a new garden site, during regular health monitoring, or after extreme weather events such as frost or heat waves.
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。
Master skill composing the 5-step synoptic cycle for panoramic synthesis across multiple domains. Orchestrates meditate, expand-awareness, observe, awareness, integrate-gestalt, and express-insight into a coherent process that produces unified understanding rather than sequential compromise. Use when a problem genuinely spans 3+ domains and the interactions between domains matter more than depth in any one, when sequential analysis feels like compromise rather than integration, or before major architectural decisions affecting multiple stakeholders.
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。
Execute structural metamorphosis using strangler fig migration, chrysalis phases, and interface preservation. Covers transformation planning, parallel running, progressive cutover, rollback design, and post-metamorphosis stabilization for system architecture evolution. Use when assess-form has classified the system as READY for transformation, when migrating from monolith to microservices, when replacing a core subsystem while dependents continue operating, or when any architectural change must be gradual rather than big-bang.
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。
在实现适配器前对候选模型做分析。确定模型实现来源（transformers / 模型目录 / diffusers）、结构特征、内存约束下的逐层量化建议（可选）及 MoE 融合权重风险。适用于用户询问模型适配可行性或做适配前分析时使用。识别为 DiT 扩散模型时，索取推理仓路径后回传 `next_step: model-adapt`，由 orchestrator 委派 `msmodelslim-model-adapt`（DiT 扩展节承接）。
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。
为 msModelSlim 创建基础 Transformers 模型适配器（Model Adapter）。 包含创建适配器、实现必需接口与注册安装流程。 适用：Decoder-only LLM、理解类 VLM（仅 LLM/text 部分）。 多模态生成（DiT/扩散，含双/多专家）见下方「多模态生成扩展」节，按需加载。 不适用：Encoder-only、非 Transformers 架构。
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。
在基础模型适配完成后，按算法适配并安装 Adapter 接口，依据目标模型真实 forward 生成并验证 final-logits patch，执行单算法 msModelSlim 离群值抑制变换、官方计算图提取和浮点 logits 门禁。
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。
"Apply the Dynamic Capabilities framework (Teece et al., 1997) — sensing, seizing, and transforming — to analyze how firms adapt, integrate, and reconfigure competences in rapidly changing environments. Use this skill when the user needs to explain why some firms sustain advantage while others decline, evaluate organizational agility, distinguish operational from strategic capabilities, or when they ask 'how do we stay competitive as the market shifts', 'why did this firm fail to adapt', or 'what capabilities do we need to build'.".
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。
VST3 format adapter for Pulp — SingleComponentEffect wiring, bus arrangement negotiation, parameter / MIDI event routing, state round-trip, and the pitfalls discovered while wiring the adapter against Steinberg's SDK.
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。
CLAP format adapter for Pulp — how Processor bridges to clap_plugin_t, how parameters / modulation / sidechain / MPE / UMP / sysex flow, and the pitfalls discovered while wiring the adapter.
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 Unit v3 (AUAudioUnit) format adapter for Pulp — render-block wiring, parameter tree bridging, MIDI / sysex via AURenderEvent, sidechain pulls, state persistence, iOS extension surface, and the pitfalls discovered while wiring the adapter.
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 a task needs the judgment of an Adapted Physical Education Specialist — writing a legally-defensible PE present level and annual goal from motor/fitness assessment data, choosing the right test battery and alternate items for a student's mobility profile, recommending a placement step on the PE least-restrictive-environment continuum, or distinguishing a motor/balance deficit from a behavioral avoidance pattern.
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。