1.1万 件 ・ 関連度順
End-to-end guidance for protein design pipelines. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design. For QC thresholds, use protein-qc.
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。
ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.
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。
End-to-end protein design pipeline guide across preparation, generation, validation, and filtering. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design-tool-selection. For QC thresholds, use protein-design-qc.
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。
ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.
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。
Research any topic in exhaustive depth by combining web search, academic papers, social discussions, and community conversations. Produces comprehensive briefings with evidence from every available source type, confidence scoring, and identified knowledge gaps.
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。
Identify emerging trends early by monitoring signals across Twitter trending, TikTok viral content, YouTube rising videos, Google News velocity, and Reddit community buzz. Combines platform-specific trend indicators into a unified trend confidence score.
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。
Generate data-backed content briefs by analyzing Google SERP results, competitor content, keyword intent, and audience questions. Produces writer-ready briefs with target queries, required subtopics, recommended format, word count, and competitive differentiation angles.
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。
Research potential partners by analyzing company profiles, product fit, market positioning, team background, and mutual connections. Combines firmographic data, content analysis, and social signals for partnership qualification and relationship mapping.
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。
Research podcasts, episodes, and audio content by searching YouTube (where most podcasts crosspost), web sources, and transcript databases. Discover relevant shows, track guest appearances, and analyze topic coverage in the podcast ecosystem.
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。
Source technical and professional talent by combining LinkedIn profiles, GitHub contributions, and company intelligence. Identifies candidates with specific skills, assesses expertise depth, and provides outreach context for recruiting teams.
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。
Generate content ideas backed by real audience demand data from Reddit questions, YouTube search gaps, Twitter discussions, and web search trends. Identifies proven topics, underserved questions, and content formats that outperform in your niche.
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。
Research competitor pricing strategies by extracting pricing pages, comparing product listings on Google Shopping and Amazon, and analyzing market positioning. Produces pricing benchmarks, tier comparisons, and feature-per-price 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。
Research emerging academic trends by combining scholarly databases, web sources, and news to identify rising research areas, breakthrough papers, and shifting scientific consensus. Ideal for R&D teams tracking frontier science.
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。
Prepare personalized email outreach by researching prospects, verifying email addresses, building company context, and generating relevant talking points. End-to-end outreach preparation from prospect identification to send-ready intelligence.
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。
Research target audiences across Reddit, Twitter, Instagram, and Xiaohongshu to understand demographics, interests, pain points, language patterns, and content preferences. Produces audience personas backed by real community data.
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。
Rapidly assess crisis situations by monitoring Twitter, Reddit, news, and Chinese social platforms simultaneously. Detects escalation velocity, identifies key narratives, tracks official responses, and measures public sentiment shifts during critical events.
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。
Reconcile accounts by comparing GL balances to subledgers, bank statements, or third-party data. Use when performing bank reconciliations, GL-to-subledger recs, intercompany reconciliations, or identifying and categorizing reconciling items.
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 bundled self-contained Godot WeChat Mini Game adapter kit to an official Godot checkout. Use when the user wants to port official Godot to WeChat Mini Game, refresh a nearby 4.6-based port, or inspect the shipped patch/source bundle for WXMEMFS, `wx.request`, audio cleanup, display/input, WXGLX dual-path rendering, `.wasm.br`, and `wx.exitMiniProgram`. This skill is version-locked to the bundled upstream base and must not be described as a generic “Godot 4.x” patch dump.
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。
Generate structured lead lists from prospect databases and web directories with Firecrawl browser. Use for finding prospects by role, company type, industry, stage, location, technologies, or other criteria and exporting CRM-ready JSON or CSV.
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。
Extract any website's design system into an agent-ready DESIGN.md using Firecrawl scrape evidence. Use when the user wants colors, fonts, spacing, components, layout patterns, or brand/UI guidance from a website so AI agents can create new websites, clone a look, or build pages inspired by that design.
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 for all frontend and web UI tasks by default to apply a business-agnostic desktop web visual style system with medium-strength guardrails; if an existing design system is present, follow it first and use this skill only as a style harmonizer.
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。
Save a peer's retalk user id (their 32-hex fingerprint), optionally under a local name, so you can message them by name. Use when you have a peer's fingerprint to record. Add --verify to fetch and pin their keys now. If the fingerprint is missing, ask with AskUserQuestion.
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 an end-to-end-encrypted message to a peer, autonomously. Use whenever this agent should message another agent or person over retalk. Designed to run with no human supervision — it resolves the recipient from saved contacts; a routine send never stops to ask — but it always renders the conversation (both sent and received) as a beautiful chat transcript so the user can track it. (Recipient/relay are set up once by the init skill.)
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。
Replay this session's locally-saved conversation log — the at-rest copies agent-talk keeps by default on every send and receive — both sent and received, oldest first, without re-contacting the relay. Use to review the conversation with a peer. `<user>` is this session's user directory (absolute path; from init).
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。