「msa」の検索結果

Remove AI-generated writing patterns from Modern Standard Arabic (MSA/الفصحى) text. Use when editing or reviewing Arabic formal text to make it sound naturally human-written.

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。

Legal-contract review desk (Busabase App-in-Skill) for NDAs, MSAs, DPAs, and SOWs. Use when the user invokes $kelly-legal-contracts or /kelly-legal-contracts, mentions legal, 法务, contract review, 合同审阅, NDA, MSA, DPA, SOW, redlines, clause playbook, legal intake, issue list, or wants a Busabase-backed review desk where a human legal reviewer approves, revises, or blocks agent-prepared contract issues.

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 structured fields from umsatzsteuervoranmeldung documents.

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。

Align protein structures using Foldseek 3Di, TM-align, US-align, DALI, or Foldmason for structural MSA. Predict, score, and superpose backbone coordinates when sequence identity is below the twilight zone or remote-homology detection is required. Use when sequence MSA fails (<25% identity), when the dark proteome is the target, when AlphaFoldDB / ESM Atlas search is needed, or when structural superposition is the goal.

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。

Align protein structures using Foldseek 3Di, TM-align, US-align, DALI, or Foldmason for structural MSA. Predict, score, and superpose backbone coordinates when sequence identity is below the twilight zone or remote-homology detection is required. Use when sequence MSA fails (<25% identity), when the dark proteome is the target, when AlphaFoldDB / ESM Atlas search is needed, or when structural superposition is the goal.

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 carrier is evaluating ELD / telematics / dashcam platforms and needs an objective comparison of Samsara, Motive (formerly KeepTruckin), and Geotab. Covers ELD compliance, AI dashcams, driver scoring, integrations, pricing structure, and which fleet profile suits which platform.

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A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's inferences and guesses quarantined away from the user's own phrasing, so the user verifies what the model believes, not just what it doubts. Use when the user says "let me think out loud" or wants to ramble a bit before building anything, when a message opens with a speech-to-text preamble like "switching to voice, sorry for typos", when input is a long weakly punctuated stream with restarts and mid-message reversals ("actually no, wait, scrap that idea entirely"), or when the user asks to be interviewed about a fuzzy half-formed idea. Includes an optional capture mode for rambles spread across several messages and an optional targeted interview.

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**MANDATORY for ALL MCP server work** - mcp-use framework best practices and patterns. **READ THIS FIRST** before any MCP server work, including: - Creating new MCP servers - Modifying existing MCP servers (adding/updating tools, resources, prompts, widgets) - Debugging MCP server issues or errors - Reviewing MCP server code for quality, security, or performance - Answering questions about MCP development or mcp-use patterns - Making ANY changes to server.tool(), server.resource(), server.prompt(), or widgets This skill contains critical architecture decisions, security patterns, and common pitfalls. Always consult the relevant reference files BEFORE implementing MCP features.

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DEPRECATED: This skill has been replaced by `mcp-app-builder`. Check if `mcp-app-builder` is available in the skills folder. If not, install it: `npx skills install mcp-use/mcp-use --skill mcp-app-builder` Use `mcp-app-builder` instead of this skill. Build Model Context Protocol (MCP) servers with mcp-use framework. Use when creating MCP servers, defining tools/resources/prompts, working with mcp-use, bootstrapping MCP projects, or when user mentions MCP development, tools, resources, or prompts.

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。

Scans the developer's machine for dead side projects, autopsies each one from its git history (died at the payments wall, killed by a newer project, finished but never shipped), surfaces their personal death patterns, and picks the corpse most worth resurrecting — then helps ship it. Use when the user mentions abandoned, unfinished, or old side projects, asks "what should I finish", wants to revive or resurrect a project, says "run the graveyard", wonders why they never finish anything, or is about to start a new project that sounds like one they already built. Runs entirely locally.

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Reconstructs why code exists from local git history, including the introducing commit, later changes, current authors, repeated companion files, and likely intent. Use when the user asks "why does this code exist", "who wrote this function and why", or to "explain the history of this function" before a rewrite, refactor, or risky edit. Runs entirely locally.

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。

Analyzes git diffs against a stated intent to detect scope creep, unrelated files, broad pull requests, changes that grew beyond a fix, dependency additions, public API renames, config or CI edits, oversized hunks, and formatting-only files. Use when the user asks whether a change grew beyond the fix, a PR is too broad, or what unrelated stuff they touched, and wants keep, split, or justify guidance. Operates locally and offline.

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Use when a task is too large for one model pass, needs parallel research or generation across many subtasks (like researching a dozen competitors at once), or the user asks to orchestrate multiple models, split work across a model team, run an advisor-worker loop, have a stronger model review the plan while cheap workers execute, or says "too big for one model" or "fan this out". Not for single-file edits or tasks one model handles in one pass.

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。

Beta readers for any draft, run by simulating how a real reader experiences it, moment by moment. A skim gate, a no-lookahead timed read producing an attention transcript with quit points, a recall test of what a reader remembers the next day, and a trust ledger of the implied author. Use when the user asks for a beta read, beta readers, test readers, human review, reader review, to read something like a human, to be a first reader, whether a piece holds attention or will actually get read, for a quick read of whether a busy skimmer would even open a post, what readers will remember or take away tomorrow, where readers stop reading or bounce, or why a draft still feels off or hollow after anti-slop or humanizer edits. Final gate before publishing; reports where the reading broke; never rewrites.

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。

Checks requirements.txt, pyproject.toml, and package.json dependency manifests for surface-level direct-dependency footguns: standard-library shadowing pins, abandoned backports, unpinned dependencies, and obvious intra-manifest conflicts, plus opt-in PyPI yanked releases. Use when the user asks to check a manifest for dependency problems, asks why dependencies won't install or whether anything is wrong with their dependencies, wants a dependency autopsy, or suspects dependency manifest rot. Runs offline by default as a local tool for the user's own project, not repository CI.

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。

DEPRECATED: This skill has been replaced by `mcp-app-builder`. Check if `mcp-app-builder` is available in the skills folder. If not, install it: `npx skills install mcp-use/mcp-use --skill mcp-app-builder` Use `mcp-app-builder` instead of this skill. Build ChatGPT apps with interactive widgets using mcp-use and OpenAI Apps SDK. Use when creating ChatGPT apps, building MCP servers with widgets, defining React widgets, working with Apps SDK, or when user mentions ChatGPT widgets, mcp-use widgets, or Apps SDK development.

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Provides access to a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.

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。

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

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。

Lightweight NDA, MSA, and vendor contract review for SMBs without legal on staff. Reads contracts from local files, mail attachments (Gmail or M365), a connected file store (Google Drive or M365), or DocuSign envelopes; flags non-standard terms; explains risks in plain English; and outputs a marked-up redline as a separate DOCX. Use when the user says "review this contract," "what am I signing," "red flags," "flag any concerns," "check the payment terms," or uploads/forwards a contract or legal agreement.

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。

Check the status of existing agreements with a vendor across all connected systems — CLM, CRM, email, and document storage — with gap analysis and upcoming deadlines. Use when onboarding or renewing a vendor, when you need a consolidated view of what's signed and what's missing (MSA, DPA, SOW), or when checking for approaching expirations and surviving obligations.

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。

General Counsel advisory for startups: contract review (MSA, SaaS, NDA, DPA, employment), IP strategy, term sheet decoding, and regulatory landscape mapping. Use when reviewing any contract or term sheet, deciding when to engage outside counsel, defining IP strategy, evaluating regulatory exposure (HIPAA, GDPR, FDA, fintech), or when user mentions general counsel, GC, legal review, contract risk, term sheet, IP assignment, or regulatory exposure. NOT a substitute for licensed counsel — surfaces questions to bring to qualified attorneys.

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。

/cs:gc-review <plan> — General Counsel interrogation of contracts, IP, regulatory, term sheets, and employment-law surface. Use when reviewing a term sheet before signing, redlining a customer MSA, or checking IP assignment and regulatory exposure on a new product.

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 professional, jurisdiction-aware business documents: freelance contracts, project proposals, SOWs, NDAs, and MSAs. Structured Markdown output with docx conversion instructions. Covers US (Delaware), EU (GDPR), UK, and DACH (German law) jurisdictions. Not a substitute for legal counsel — use as strong starting points. Use when drafting a freelance contract, preparing a client proposal, writing an SOW for a new engagement, or producing an NDA before sharing sensitive material.

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。

Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.

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。