「features」の検索結果

Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use when prioritizing features, synthesizing user research, writing requirement documentation, or developing product strategy.

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Map user Jobs-to-Be-Done with functional, emotional, and social dimensions plus outcome expectations. Use when reframing product decisions around user motivations rather than features.

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Design the structure, hierarchy, and navigation model for a product's content and features.

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Conduct a structured competitive analysis comparing UX patterns, features, strengths, and gaps across rival products.

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You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.

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Use when reviewing a spec, PRD, requirements doc, or design plan before implementation begins — especially when the doc feels too big, bundles unrelated features, may contradict the current codebase, or seems vague, infeasible, or thin on security and error handling. Not for cross-checking a spec against a plan — that's /alignment.

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Use when reviewing a spec, PRD, requirements doc, or design plan before implementation begins — especially when the doc feels too big, bundles unrelated features, may contradict the current codebase, or seems vague, infeasible, or thin on security and error handling. Not for cross-checking a spec against a plan — that's the alignment skill.

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Review or improve code — one skill, four modes: bug/security review (default), performance, clean-code audit, slop cleanup. Pass mode:review|perf|clean|cleanup or infer. Don't use for writing features or generating tests (use test-coverage).

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Build production-grade frontend interfaces with distinctive aesthetics and working code. Use for UI components, pages, or frontend features. Don't use for backend/API work or usability-only audits.

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AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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Monitor competitor pricing, features, changelogs, dashboards, and product changes with Firecrawl. Use for recurring competitive intelligence, pricing tier extraction, feature change tracking, or structured competitor alerts.

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Structured planning mode for AI agents working on coding and development tasks. Produces a comprehensive plan BEFORE any code is written or changed, including task decomposition, architecture decisions, risk assessment, and approval gates. Use this skill whenever the user says "/plan", "plan mode", "plan first", "think before coding", "make a plan", or asks the agent to plan before executing. Also trigger when the user describes a complex multi-step development task and would clearly benefit from structured planning before implementation — especially for tasks involving new features, refactors, migrations, infrastructure changes, or multi-file edits. When in doubt, plan. A few minutes of planning prevents hours of rework.

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Fix bugs and broken behavior when there is enough evidence to act on a repair path. Use for errors, crashes, incorrect results, API failures (500, 404, 403), CORS problems, database exceptions, broken rendering, duplicated or wrong data, off-by-one mistakes, timezone/date bugs, broken forms, config-caused runtime failures, and regressions. Trigger when the user wants the bug repaired and the conversation already contains a clear failing area, a reproducible failing test, a concrete error path, or a prior diagnosis to implement. Do NOT use for new features, pure explanation, architecture discussion, broad research, or bug reports where the main need is figuring out why the behavior happens — use diagnose for that.

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Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.

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Turn ideas into designs through collaborative dialogue. Use before any creative work - features, components, or modifications.

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Guide for the Kimix web frontend (src/app/) — vanilla TypeScript + Vite, SSE-based chat UI mirroring sse_cli.py. Use when modifying the frontend, adding new UI features, changing the API client, or understanding the message rendering pipeline.

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Use when you meet a new dataset, table, file or source, before building features or a model on it, or when a number looks wrong — profile grain, keys, nulls, cardinality, time coverage and target balance, and persist what you learn as code

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Use when ordering board tasks - set priority/blocked_by/deploy_depends_on by severity and dependency, split MoSCoW within a feature, and use RICE/ICE only for a genuine "what next" call across several features

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Guides and best practices for working with Neon Serverless Postgres. Covers getting started, local development with Neon, choosing a connection method, Neon features, authentication (@neondatabase/auth), PostgREST-style data API (@neondatabase/neon-js), Neon CLI, and Neon's Platform API/SDKs. Use for any Neon-related questions.

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Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

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Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.

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This skill should be used when creating, formatting, or optimizing content for a Substack newsletter. Covers post structure, SEO metadata (titles, slugs, meta descriptions), native engagement features (Notes, Polls, Chat), monetization tactics, and free-to-paid conversion strategies.

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Scan a live web app with Playwright, extract all features, generate PRD/epics/stories with priorities and dependencies, export to Notion. Checks required MCP servers before starting.

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