「multi-step」の検索結果

Structured task planning with clear breakdowns, dependencies, and verification criteria. Use when implementing features, refactoring, or any multi-step 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。

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.

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 construction plan for a multi-step engineering task that requires multiple PRs or a phased approach. Produces a self-contained plan file with dependency graph, review gates, and step-by-step execution instructions for coding agents. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex task, or describes work that clearly needs multiple PRs. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, user asks to modify/update an existing plan, user says "just do it", or user asks for code review/audit.

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。

Classify financial PDFs by extracting company identity, document type, and date metadata. Validates ticker via Yahoo Finance with multi-step fallback. Uses a Python orchestration script for validation, file modification, and writing markdown.

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。

When the same multi-step workflow repeats in Cursor (user corrections or agent redos), capture it as a new SKILL.md under .cursor/skills/ so future sessions load it automatically.

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Performs retrosynthetic planning using AiZynthFinder (MCTS, template-based), Chemformer (template-free transformer), ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, building-block availability (eMolecules, Enamine, Mcule), forward prediction validation (Molecular Transformer), and disconnection-aware multi-objective search (MO-MCTS). Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.

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 AI prompt chains and multi-step workflows for complex tasks. TRIGGERS - Use when user needs help with ai-chain-builder related tasks.

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Design multi-step AI agent architectures with tools, memory, and orchestration. TRIGGERS - Use when user wants to design AI agents, multi-agent systems, or autonomous AI 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。

Generates an Apple-compliant account deletion flow with multi-step confirmation UI, optional data export, configurable grace period, Keychain cleanup, and server-side deletion request. Use when user needs account deletion, right-to-delete, or Apple App Review compliance for account removal.

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 multi-step change needs explicit sequencing, dependencies, or a handoff

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。

Performs retrosynthetic planning using AiZynthFinder (MCTS, template-based), Chemformer (template-free transformer), ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, building-block availability (eMolecules, Enamine, Mcule), forward prediction validation (Molecular Transformer), and disconnection-aware multi-objective search (MO-MCTS). Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.

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 working with @Transactional, multi-step database operations, distributed transactions, or any code that needs atomicity guarantees. Covers propagation rules, isolation levels, read-only optimization, and common pitfalls.

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。

Sequential multi-step setup flow (assistant/wizard) with AdwNavigationView, Back/Forward navigation, progress indication. Use when building onboarding or setup flows with ordered steps.

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 a multi-step conversion funnel to find where visitors drop off and which steps have the worst leakage. Use this skill when someone describes a journey and asks about conversion rates, drop-off, fallout, or step completion. Trigger for "analyze our checkout funnel," "where are visitors dropping off," "what's our add-to-cart to purchase conversion rate," "funnel analysis," "show me fallout between steps," or "which step loses the most visitors."

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 a multi-step conversion funnel to find where users drop off and which steps have the worst leakage. Use this skill when someone describes a journey or funnel and asks about conversion rates, drop-off, fallout, or step completion. Trigger for phrases like "analyze our onboarding funnel," "where are users dropping off," "what's our checkout conversion rate," "funnel analysis," "show me fallout between these steps," or "which step loses the most users."

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。

Deep reasoning mode for complex problems requiring maximum cognitive depth. Use when facing difficult architectural decisions, complex debugging, multi-step problem solving, or situations requiring careful analysis before implementation.

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Append structured execution traces across operational, cognitive, and contextual surfaces with minimal overhead. Load when inspecting agent runs, logging tool calls and observations, enabling post-run debugging, or pairing with structured-planning step IDs. Also triggers on "trace this run", "log execution", "agent observability", "run log", or when fault-localize needs evidence. Default-on during multi-step plans. Traces live at .agent-loom/traces/ — git-ignored by default.

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Select alternative execution paths when a plan step fails — branch on outcomes instead of blind retry. Load when a structured plan step fails, an unexpected observation arrives, or the user asks what to try next after an error. Also triggers on "try another approach", "route around this failure", "replan on failure", "if X fails try Y", or outcome-based branching during multi-step work. Pairs with structured-planning and debug-and-fix. Does not replace root-cause debugging — adds plan-level path selection.

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。

Performs retrosynthetic planning using AiZynthFinder (MCTS, template-based), Chemformer (template-free transformer), ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, building-block availability (eMolecules, Enamine, Mcule), forward prediction validation (Molecular Transformer), and disconnection-aware multi-objective search (MO-MCTS). Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.

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。

[40] PLAN. Create clear action plans with steps, success criteria, and risk awareness. Use before implementing features, making changes, starting projects, or anytime you need a roadmap to success. Triggers on "plan this", "how should we approach", "what's the strategy", "steps to complete", or when facing complex multi-step 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。

[50] EXECUTE. Execute plans step-by-step with confirmation gates. Each step requires user approval before proceeding. Includes change management lifecycle (Pre-Change → During → Post-Change → Rollback). Use when implementing approved plans, deploying changes, or any multi-step execution requiring control and reversibility.

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。

[01] META. Сканирует доступные skills, создает план выполнения и идет шаг за шагом с подтверждением каждого этапа. Triggers on complex tasks, multi-step work, or when structured execution is needed.

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。

[00] META. Orchestrate skills into dynamic chains for complex tasks. Analyzes the task, discovers available skills, builds an optimal chain, explains why each skill is needed, and executes step-by-step with user confirmation. Use for any complex task requiring multiple thinking/research/analysis steps. Triggers on "plan this", "how to approach", "what's the strategy", "build a plan", or any multi-step problem requiring skill 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。

Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

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。