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Found 554 Skills
Act as an active outline partner who develops structure collaboratively. Use when developing, iterating, or improving story outlines. Generates scene beats, character arcs, plot structures, and exploratory prose samples. Contrasts with story-collaborator which drafts finished prose.
Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says "analyze experiment", "compare experiments", "analyze against baseline", or provides one or two experiment IDs for analysis.
Analyzes events through futures lens using scenario planning, trend analysis, weak signals, drivers of change, and forecasting methods (exploratory, normative, backcasting). Provides insights on possible futures, emerging trends, disruptive forces, strategic foresight, and alternative scenarios. Use when: Strategic planning, emerging trends, technology assessment, long-term planning, uncertainty navigation. Evaluates: Trends, weak signals, drivers of change, plausible futures, strategic options, uncertainty ranges.
Facilitate collaborative idea exploration and refinement. Guides through iterative questioning, design validation, and solution architecture before implementation.
Core rules for bkit plugin. PDCA methodology, level detection, agent auto-triggering, and code quality standards. These rules are automatically applied to ensure consistent AI-native development. Use proactively when user requests feature development, code changes, or implementation tasks. Triggers: bkit, PDCA, develop, implement, feature, bug, code, design, document, 개발, 기능, 버그, 코드, 설계, 문서, 開発, 機能, バグ, 开发, 功能, 代码, desarrollar, función, error, código, diseño, documento, développer, fonctionnalité, bogue, code, conception, document, entwickeln, Funktion, Fehler, Code, Design, Dokument, sviluppare, funzionalità, bug, codice, design, documento Do NOT use for: documentation-only tasks, research, or exploration without code changes.
Activate the 'Brainstorming Coach' agent (Carson) in the BMad system, which is used to facilitate innovation workshops, brainstorming sessions, and idea generation. It is suitable for scenarios that require breaking conventional thinking, generating a large number of ideas, or conducting systematic innovation exploration.
AI SDLC QA workflow. Use when an AI assistant is asked for QA planning, acceptance validation, regression scope, exploratory checks, smoke tests, release verification, or change-focused manual validation evidence. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
ALWAYS use this skill before answering brainstorming, ideation, prompt crafting, or open-ended exploration requests. Transforms vague requests into actionable outputs via adaptive guided questioning — triages into Prompt Mode (craft/improve prompts), Explore Mode (brainstorm ideas), or Focused Mode (specific problem strategies). Trigger when user says: "brainstorm", "ช่วยคิด", "help me think", "I have an idea", "improve this prompt", "let's explore", "I want to build", "I'm thinking about", "brainstorm วิธี", "ช่วยคิดหน่อย", "อยากทำ", "ยังไม่รู้จะทำอะไร", "not sure about the approach", "help me figure out", "what should I". Also trigger for: side projects, career decisions, project planning, migration strategies, architecture decisions, cost optimization, or any request where the user hasn't decided direction yet and would benefit from structured discovery. Do NOT skip — this skill adapts depth automatically (2-7 questions) and produces BETTER results by asking targeted questions first.
Abductive analysis for qualitative interview data following Timmermans & Tavory. Guides you through theory-first analysis that recognizes anomalies and generates novel theoretical insights through systematic puzzle exploration.
AI-powered adversarial UI testing via the browse CLI. Analyzes git diffs to test only what changed, or explores the full app to find bugs. Tests functional correctness, accessibility, responsive layout, and UX heuristics. Use when the user asks to test UI changes, QA a pull request, audit accessibility, or run exploratory testing. Supports local browser (localhost) and remote Browserbase (deployed sites).
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
Create and configure LaunchDarkly feature flags in a way that fits the existing codebase. Use when the user wants to create a new flag, wrap code in a flag, add a feature toggle, or set up an experiment. Guides exploration of existing patterns before creating.