「selection」の検索結果

Trigger for: writing, revising, converting, or descoping a federal Statement of Work, Performance Work Statement, SOW, PWS, or SOO; develop executable requirements; define contract scope; create measurable performance standards; or prepare requirements before an IGCE. Produce a contract-file-ready .docx plus separate chat-only staffing and Section B handoffs. Never place FTEs, SOC codes, staffing estimates, IGCE content, CLINs, or pricing schedules inside the SOW/PWS body. Do NOT use for the IGCE, market research report, acquisition plan, QASP, or clause-selection matrix.

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。

Trigger for: explaining current FAR, DFARS, or agency-supplement text; determining documented acquisition-policy status for an agency and FAR part; comparing codified text, RFO model text, and agency deviations; tracing acquisition rulemaking; finding procurement comment periods; analyzing public comments; refreshing prior policy analysis; or producing an Acquisition Policy Impact Brief. Use published federal sources and supplied policy documents without making legal or procurement-specific applicability determinations. Do not use for clause selection, market research execution, opportunities, pricing, grants, or cooperative agreements.

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。

Trigger for: finding federal opportunities; capture and bid screening; competitor or incumbent intelligence; recompete pipelines; teaming partner research; agency, customer, or market intelligence; federal labor-rate or pricing context; or refreshing prior GovCon research. Always begin with the complete workflow menu and obtain the user's selection before research, preflight, file generation, web search, or MCP calls. Produce sourced chat findings or an optional validated GovCon Growth Brief without making a bid decision from public data 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。

Still-to-video conversion guide: model selection, motion prompting, and camera movement. Covers Wan 2.5 i2v, Seedance, Fabric, Grok Video with when to use each. Use for: animating images, creating video from stills, adding motion, product animations. Triggers: image to video, i2v, animate image, still to video, add motion to image, image animation, photo to video, animate still, wan i2v, image2video, bring image to life, animate photo, motion from image

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 a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.

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。

Orchestrate the BZD mathematical-modeling Skills across problem reading, idea generation, model selection, paper drafting, section checks, final format/AIGC audits and judge-style scoring. Use when a user wants one entry point, an end-to-end competition workflow, the next appropriate Skill, or coordinated processing of a problem and paper.

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。

Cross-engine decision rubric for self-hosting or recommending an LLM serving stack. Picks among vLLM, SGLang, TensorRT-LLM, TGI, llama.cpp, Ollama, and MLX as a function of (hardware × workload × constraint), not "which is fastest". Activates whenever a coder-agent must choose, defend, or migrate a serving runtime.

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。

Adds and tunes a reranker stage for RAG using the retrieve-wide, rerank-narrow pattern. Use when relevant documents appear in the initial top-N but are buried by noise, top-1 precision or MRR is low despite adequate recall, or too many marginal chunks consume context. Covers cross-encoder, API, and local rerankers; N-to-k selection; and latency/cost tradeoffs. Do not use when retrieval recall itself is failing.

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。

Creates isolated git worktrees with smart directory selection and safety checks. Use when starting isolated feature work.

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。

Venue- and market-type-agnostic strategy, sizing, and backtesting layer for binary prediction markets (Kalshi, Polymarket, ForecastEx). Covers the durable edge thesis, fee-aware selection, fractional-Kelly sizing, and leak-free validation methodology.

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。

Stage implementation changes for commit with precise file selection. Use when the user asks to "stage changes", "stage files", "add files to staging", or "prepare changes for commit".

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 Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.

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。

Expert guide for using n8n-mcp MCP tools effectively. Use when searching for nodes, validating configurations, accessing templates, managing workflows, or using any n8n-mcp tool. Provides tool selection guidance, parameter formats, and common patterns.

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。

Choose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via claudish, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.

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。

Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.

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。

Optimize provider selection, routing, and credit usage across 150+ enrichment sources for company/contact 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。

Guides Stripe integration decisions across API selection (Checkout Sessions vs PaymentIntents), Connect platform setup (Accounts v2, controller properties), billing/subscriptions, tax and registrations (Stripe Tax, automatic_tax, product tax codes), Treasury financial accounts, integration options (Checkout, Payment Element), migrating from deprecated Stripe APIs, and security best practices (API key management, restricted keys, webhooks, OAuth). Use when building, modifying, or reviewing any Stripe integration, including accepting payments, building marketplaces, integrating Stripe, processing payments, setting up subscriptions, collecting sales tax, VAT, or GST, creating connected accounts, or implementing secure key handling.

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。

Install or diagnose the local GreenBubbles CLI and read-only WeChat access. Use for a missing CLI, failed database access, source selection, or initial owner-operated key capture.

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。

Retrieve mouse phenotype data from the Jackson Laboratory Mouse Phenome Database (MPD) via its REST API. Browse 520+ projects, look up per-project measure metadata, pull strain-level means (raw or LS-mean adjusted) and per-animal values, find measures by MP/VT ontology terms, and resolve strain nomenclature or gene coordinates. Use for QTL support, cross-strain comparison, mouse model selection, and ontology-driven phenotype discovery. Use monarch-database for disease-gene-phenotype knowledge graphs; ensembl-database for mouse genome annotations.

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。

scRNA-seq with Scanpy: QC, normalization, HVG selection, PCA, neighborhood graph, UMAP/t-SNE, Leiden clustering, markers, cell annotation, trajectory inference. Standard scRNA-seq exploration.

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。

Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.

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。

Designing scientific schematics, diagrams, and graphical abstracts. Covers tool selection (BioRender, Inkscape, Affinity, PowerPoint), design principles for pathway diagrams, mechanism schematics, experimental workflows, and journal graphical abstracts. Includes composition, icon sourcing, color for biological entities, and accessibility. Use when creating illustrative (not data-driven) scientific figures.

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。

mdtraj molecular dynamics trajectory analysis (Python). Reads DCD/XTC/TRR/NetCDF/H5/PDB topologies and trajectories; computes RMSD vs time, radius of gyration, per-residue RMSF, residue-residue contact frequency maps, phi/psi torsions for Ramachandran plots (general + Gly/Pro), and 8-state DSSP secondary structure. Modules: trajectory I/O, geometry (distances/angles/dihedrals), structural analysis (RMSD/Rg/RMSF/SASA), contacts, hydrogen bonds, secondary structure (DSSP), NMR observables. For broader atom-selection grammar use mdanalysis-trajectory; for running MD simulations use OpenMM/GROMACS.

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。

Evaluating scientific evidence and claims. Covers study design hierarchy (RCT to expert opinion), effect sizes (OR, RR, NNT, Cohen's d), confounding, p-value vs clinical significance, GRADE quality assessment, reproducibility, and bias types (selection, information, confounding, reporting). Use when reading a paper or assessing 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。