「multi-step」の検索結果

Activates Standard of Excellence enforcement for the current session. Load before starting any task to apply completion standards: finish the whole thing, fix the root cause, ship the complete working result. Blocks partial solutions, workarounds, deferred threads, and invented content limits when the permanent solve is within reach. Triggers: 'do the whole thing', 'boil the ocean', 'standard of excellence', 'finish it completely', before starting any implementation, refactoring, or multi-step task where partial output is a risk. Does NOT apply to: read-only queries, one-line typo fixes, pure knowledge questions, or single-output summarize requests.

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Multi-step reasoning engine for complex analysis and systematic problem solving. Use when: (1) Complex debugging scenarios with multiple layers, (2) Architectural analysis and system design, (3) Problems requiring hypothesis testing and validation, (4) Multi-component failure investigation, (5) Performance bottleneck identification. Triggers: "--think", "--think-hard", "--ultrathink", "analyze step by step", "break down this problem", "systematic analysis". IMPORTANT: Do NOT use for simple single-step tasks.

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Design and operate multi-agent orchestration patterns (ReAct loops, evaluator-optimizer, orchestrator-workers, tool routing) for LLM systems. Use when building or debugging agent workflows, tool-use loops, or multi-step task delegation; triggers: agentic, multi-agent, orchestration, ReAct, evaluator-optimizer, tool-use, handoff.

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Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.

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AI agent workflow patterns including ReAct agents, multi-agent systems, loop control, tool orchestration, and autonomous agent architectures. Use when building AI agents, implementing workflows, creating autonomous systems, or when user mentions agents, workflows, ReAct, multi-step reasoning, loop control, agent orchestration, or autonomous AI.

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Generate a new agent tool skill, written in the style of ASE skills, which orchestrates a workflow of sequential actions, parallel actions, sub-agent calls, and skill calls. Use when the user wants to "generate a skill", create a "workflow", "orchestrate" or "chain" multiple ASE skills, or automate a recurring multi-step procedure.

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You MUST use this before any creative work or multi-step task. Explores intent, requirements, and design before implementation. Then documents the plan before coding.

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You MUST use this before any creative work or multi-step task. Explores intent and writes execution-ready plans before coding.

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Browser Use is an open source browser automation framework for AI agents that turns websites into controllable interfaces for multi-step tasks. It combines a Python SDK, browser orchestration, and model integrations so agents can navigate, extract data, and complete workflows in real browsers.

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Sequential thinking and structured problem solving. Break down complex problems into steps with revision and branching capabilities. Use for multi-step analysis, planning, and hypothesis verification.

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Manus-style file-based planning for complex tasks. Use task_plan.md, findings.md, and progress.md to maintain persistent context. Use for multi-step tasks, research, or work spanning many tool calls.

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Programmatic web automation using Playwright Python — data extraction, form filling, multi-step workflows, auth session management, screenshot/PDF capture, and anti-detection patterns. Use for scripted automation tasks, not interactive MCP-based browser control.

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Design, structure, or improve form interfaces for clarity, completion rates, and user confidence. Use when the user asks to build a form, redesign a form flow, improve form conversion, add multi-step forms, fix form UX, or structure field layouts and validation.

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Hunt business logic flaws — race conditions, workflow state bypass, parameter pollution affecting logic, coupon/discount abuse, refund logic, multi-step process bypass, integer overflow, negative quantities, double-spend. Use when looking for non-CVE bugs that require understanding the app's intended workflow.

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Use when developing or extending EZ_math_model itself from a written implementation plan, especially adding algorithms, tool skills, templates, or multi-step repo changes.

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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. Now with automatic session recovery after /clear.

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Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.

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Thoroughness and diligence in execution — systematic checking, completeness verification, follow-through on commitments, and the discipline of finishing well. Maps the personality trait of conscientiousness to AI task execution: not cutting corners, verifying results, and ensuring that what was promised is what was delivered. Use before marking a task as complete, when a response feels "good enough" but deserves better, after a complex multi-step operation where steps may have drifted, or when self-monitoring detects a pattern of cutting corners or rushing.

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AI context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether the current reasoning structure can support it, when accumulated workarounds suggest the underlying approach may be wrong, or as a periodic structural health check during extended multi-step tasks.

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Add PUT workflow annotations to source files using the correct language-specific comment prefix. Covers annotation syntax, skeleton generation via put_generate(), multiline annotations, .internal variables, and validation. Supports 30+ languages with automatic comment prefix detection. Use after analyzing a codebase and having an annotation plan, when adding workflow documentation to new or existing source files, or when documenting data pipelines, ETL processes, or multi-step computations.

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Angular forms: Reactive Forms (preferred — typed FormGroup/FormControl since Angular 14, FormBuilder, custom + async validators, FormArray, multi-step) and Template-driven (`[(ngModel)]` + FormsModule). Validation strategies, server error mapping, accessibility. Use this skill to: - Build Reactive Forms with typed FormGroup/FormControl. - Use FormBuilder для concise syntax. - Implement custom synchronous and async validators. - Wire FormArray for dynamic field lists. - Map server errors back to form fields. - Pick Reactive vs Template-driven (prefer Reactive). Do NOT use this skill for: - General conventions (see angular-conventions). - State management beyond forms (see angular-state-and-rx). - Routing (see angular-routing). - Testing forms (see angular-testing).

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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.

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Multi-step recovery strategy to turn a specific negative review into a positive outcome

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Use when the user wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.

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