2,112 件 ・ 関連度順
概要と使いどころ
Automate Microsoft Teams tasks via Rube MCP (Composio): send messages, manage channels, create meetings, handle chats, and search messages. Always search tools first for current schemas.
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。
Automate Postmark email delivery tasks via Rube MCP (Composio): send templated emails, manage templates, monitor delivery stats and bounces. Always search tools first for current schemas.
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。
A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
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。
Automate ConvertKit (Kit) tasks via Rube MCP (Composio): manage subscribers, tags, broadcasts, and broadcast stats. Always search tools first for current schemas.
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。
音乐卡点视频 / 踩点视频：检测背景音乐的节拍，让图片或片段在节拍点上切换，配推进/白闪特效，做出燃系'卡点'短视频。当用户说 卡点视频、踩点视频、音乐卡点、节奏卡点、按音乐切换、鼓点视频、踩节奏、beat 卡点、图片卡点、卡点混剪 时使用。基于 shared/scripts/beatsync.py（librosa 节拍检测）。与 slideshow-video 区别：slideshow 每图固定时长、柔和转场，本 SKILL 切换点由音乐节拍决定、硬切踩点带特效。
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 this skill when the user is invoking doca_flow_dpa_perf on DPA-capable hardware (ConnectX-7 minimum supported, ConnectX-8 recommended, or BlueField-3) to measure rule update / disable rates on the DPA-offloaded DOCA Flow path — picking the active / passive device split, choosing workload-shape axes (burst, queue, completion threshold, workers, hash pipe algo, PSL tables), or reading Kops/sec iteration stats and the optional self-test. Trigger even when the user does not explicitly mention "doca_flow_dpa_perf" or "DPA Provider" — typical implicit phrasings include "how fast can the DPA program path-selector entries", "baseline rule-update rate on ConnectX-8", "tool reports zero ops on my BlueField", "self-test sentinel never shows on tcpdump", or "is my BlueField-2 DPA-capable". Refuse and route elsewhere for the host / DPU-CPU Flow path (doca-flow-perf), Flow pipeline tuning (doca-flow-tune), writing doca-flow / doca-dpa applications, or DOCA install — those belong to other skills.
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 this skill when wiring the DOCA SHA Offload Engine (an OpenSSL ENGINE) into an existing OpenSSL pipeline to offload one-shot SHA-1, SHA-256, or SHA-512 (EVP_Digest) onto DOCA SHA hardware without rewriting against doca-sha. Covers engine load mechanics (`openssl engine dynamic`, `set_pci_addr` ctrl, `-engine_impl`), the SHA-224 negative test that proves offload engaged, the message-size window where offload beats CPU SHA, and engine-vs-library selection. Trigger even when the user does not say "DOCA SHA Offload Engine" or "OpenSSL ENGINE" — typical implicit phrasings: "speed up openssl SHA on BlueField", "offload SHA without code changes", "is openssl using the accelerator or falling back to software", "prove DOCA SHA actually ran", "openssl dgst hashed but I'm not sure it was offloaded". Refuse and route elsewhere for new SHA pipelines (use doca-sha), MD5 / SHA-3 / SHA-224 / HMAC-SHA offload, incremental hashing via chained `EVP_DigestUpdate`, or OpenSSL PROVIDER authoring.
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 this skill when the user is debugging any DOCA symptom — a build that won't compile, a link step that can't resolve a doca_* symbol, a runtime call returning DOCA_ERROR_*, a silent service or tool, or a stack trace / valgrind / core dump — and needs the layered ladder (install → version → build → link → runtime → program → driver), verbosity controls (--sdk-log-level, DOCA_LOG_LEVEL, the doca-{lib}-trace flavor), container-debug constraints, or how to capture state for a Developer Forum post. Trigger even when the user does not say "DOCA debug" — implicit phrasings include "undefined reference to doca_*", "how do I get more logs", "packets aren't reaching the wire", "doca_caps returned nothing", or "hugepages empty in the container". Refuse and route elsewhere for library-specific debug (Flow pipe trace, RDMA QP, Comch stats), env-class pkg-config or hugepages symptoms, the DOCA_ERROR_* taxonomy and lifecycle interpretation, and performance or incident-response work — those belong to other skills.
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。
Look up Y Combinator companies and batches from the public yc-oss API, a static JSON dataset refreshed daily: company profiles, batch rosters (e.g. W25, S24), companies by industry or tag, top companies, who is hiring, founder-diversity lists, and overall YC stats. Use this skill whenever the user asks about YC-backed startups, a YC batch, YC companies in a sector or tag or that are hiring, the Y Combinator portfolio, or startup and venture research that draws on YC data. Read-only.
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。
Read Telegram channels and groups for financial news and market research with the tdl CLI: list chats and channels, find a channel by name, and export recent messages or a date or ID range. Use this skill whenever the user wants to check their Telegram, see what's new in their channels, read or export messages from a news, crypto, or trading channel or group, or gather market news and signals posted on Telegram. Read-only: it cannot send messages or join or leave channels.
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。
[omh] Fine-tuning a model on your own data -- SFT, DPO, RLVR or a LoRA adapter: decide first whether prompting or retrieval already closes the gap, choose the method from the data you have, and promote a checkpoint only when it beats the untuned baseline on a held-out eval. Use when the user says: model-finetuning, model finetuning, model fine-tuning, fine-tune a model, fine-tune the model, fine tune a model, fine tune the model, fine-tune.
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 acpx as a headless ACP CLI for agent-to-agent communication, including installed-agent inspection, prompt/exec/sessions workflows, session scoping, queueing, permissions, output formats, system-prompt overrides, and multi-agent flows authored with defineFlow/decision/decisionEdge.
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。
Query the JASPAR database for Transcription Factor (TF) binding profiles. Use when retrieving Position Frequency Matrices (PFMs) or Position Weight Matrices (PWMs) for specific TFs, resolving gene symbols to JASPAR Matrix IDs, or getting TF metadata. Supports multiple output formats (MEME, TRANSFAC, PFM, JASPAR, YAML).
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。
Drafting campaigns and ads as staged drafts, from acquisition and seasonal pushes to lifecycle sends such as win-backs, pre-arrival messages, and presale or last-call notices, choosing audiences and placements by intent, allocating budget across campaigns, and reviewing campaign results with their attribution caveats. Not needed for listing copy (catalog-listings) or overall performance questions (performance-insights).
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。
Introspect, explain, and improve Ralph Orchestrator using its published llms.txt doc map. Use this skill whenever the user asks questions about Ralph's behavior, wants to understand how a Ralph internal works (event loop, hats, memories, tasks, backends, presets), debug an unfamiliar failure mode, or propose a code change to the ralph-orchestrator repo. The skill teaches the agent to discover authoritative answers from the live docs via llms.txt before guessing, and to scope improvements through the published architecture rather than the local checkout alone.
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。
Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for 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。
Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale. Triggers: "document translation", "batch translation", "translate documents", "DocumentTranslationClient".
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。
Build, manage, and search spectral libraries for proteomics. Use when creating or working with spectral libraries for DIA analysis. Covers DDA-based library generation, predicted libraries (Prosit, DeepLC), and library formats.
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。
Parse and write protein structure files using Biopython Bio.PDB. Use when reading PDB, mmCIF, and MMTF files, downloading structures from RCSB PDB, or writing structures to various formats.
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。
Load and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Use when starting a proteomics analysis with raw or processed MS data. Handles contaminant filtering and missing value assessment.
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。
Statistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups.
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。
Detects differential alternative splicing between conditions using rMATS-turbo (BAM-based) or SUPPA2 diffSplice (TPM-based). Reports events with FDR-corrected significance and delta PSI effect sizes. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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。
Load, convert, and manipulate Hi-C contact matrices using cooler format. Read .cool/.mcool files, convert from .hic format, access matrix data, and export to different formats. Use when loading or converting Hi-C contact matrices.
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。
ChIP-seq peak calling using MACS3 (or MACS2). Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports input control, fragment size modeling, and various output formats including narrowPeak and broadPeak BED files. Use when calling peaks from ChIP-seq alignments.
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。