「multi-step」の検索結果

Create dynamic schema-based forms using @delon/form (SF component). Use this skill when building complex forms with validation, conditional rendering, async data loading, custom widgets, and multi-step workflows. Ensures forms follow JSON Schema standards, integrate with Angular reactive forms, support internationalization, and maintain consistent validation patterns across the application.

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 you have a spec or requirements for a multi-step task, before touching code

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。

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

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。

Python-based workflow manager for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake resolves execution order and parallelism. Runs local, SLURM, LSF, AWS, GCP via profiles; per-rule conda/Singularity envs. For NGS pipelines, ML training, and multi-step file processing. Use Nextflow for Groovy dataflow or nf-core integration.

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。

Implements GitHub or GitLab issues via parallel subagents with review gates between task batches. Use when resolving multi-step issues end-to-end.

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 session-scoped temporary memory palaces for extended conversations. Use when tracking state across interruptions in a multi-step project.

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。

Optimizes context window via MECW principles and memory tiering. Use when context exceeds 30% or before long multi-step tasks.

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。

CHAIN multiple existing skills into custom multi-step workflows. Programmable skill combinations with automatic handoffs. Create composite skills from building blocks with conditional logic.

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。

Uses Cowork-style multi-step analysis to process deal room documents -- contracts, financials, org charts. Outputs risk assessment, term comparison, key findings, and negotiation recommendations.

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。

Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about AI coding agents, agent runtimes, or similar internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration

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 an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without trusting self-reported success.

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。

After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.

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。

Guard long, ambiguous, or stateful AI-agent work from drift. Use when the user asks to run or continue a multi-step task, autonomous loop, bug fix, repo change, PR readiness check, compaction handoff, resume from previous context, cost-control checkpoint, or any task likely to span many tool calls, files, sessions, agents, or verification gates.

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。

Analyze user conversion funnels, calculate step-by-step conversion rates, create interactive visualizations, and identify optimization opportunities. Use when working with multi-step user journey data, conversion analysis, or when user mentions funnels, conversion rates, or user flow 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。

Use dspy.RLM (Recursive Language Model) for reasoning over contexts too large to fit in an LLM's working window — entire codebases, long logs, massive documents, or multi-step data exploration that needs a sandboxed Python REPL. Use when the input is >100k tokens, needs recursive chunking, or benefits from the LLM writing and running code to probe 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。

Use when implementing multi-step forms and processes.

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 implementing multi-step checkout process.

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 autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.

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。

Define, validate, run, resume, and audit repo-local multi-step automations with current `asc workflow` and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.

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。

Type-safe React forms with React Hook Form and Zod validation. Use for form schemas, field arrays, multi-step forms, or encountering validation errors, resolver issues, nested field problems.

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。

Cloudflare Workflows for durable long-running execution. Use for multi-step workflows, retries, state persistence, or encountering NonRetryableError, execution failed errors.

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。

Conduct in-depth, multi-step research on a given topic by decomposing queries, finding diverse sources, cross-referencing findings, and synthesizing a comprehensive report. Use when the user requests deep research or provides relevant inputs for this workflow.

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 creating, reviewing, or debugging GitHub Copilot reusable prompt files and slash commands in Visual Studio Code, including .github/prompts/*.prompt.md, inputs, context links, agent selection, and tool selection. Do not use for always-on rules, multi-step Agent Skills, custom personas, or VS Code extension commands.

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。

Create a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.

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。