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Found 27 Skills
Transform vague feature ideas into clear, testable requirements using EARS format. Capture user stories, define acceptance criteria, identify edge cases, and validate completeness before moving to design.
Use when "requirements document", "acceptance criteria", "user stories", "EARS format", "specification", "feature spec", "product requirements"
Creates structured change proposals with specification deltas for new features, breaking changes, or architecture updates. Use when planning features, creating proposals, speccing changes, introducing new capabilities, or starting development workflows. Triggers include "openspec proposal", "create proposal", "plan change", "spec feature", "new capability", "add feature planning", "design spec".
Turn ambiguous coding requests into verified Claude Code implementation specs. Use when the user wants requirements, an implementation plan, or a spec before coding; include source checks, needed ADRs, and agreed delivery. Do not use for already specified direct implementation or memory cleanup.
Turn ambiguous coding requests into verified Codex implementation specs. Use when the user wants requirements, an implementation plan, or a spec before coding; include source checks, needed ADRs, and agreed delivery. Do not use for already specified direct implementation or memory cleanup.
Expert in bridging the gap between business needs and technical solutions. Specializes in Requirements Engineering, BPMN, and Agile User Stories. Use when gathering requirements, creating user stories, modeling business processes, or translating business needs to technical specs.
Convert ambiguous user requests into structured USDM requirements documents. Decomposes requirements into Requirement → Reason → Description → Specification hierarchy. Integrates with GitHub Issues, Asana, and Jira tickets as input sources. Use when: "create requirements", "write requirements document", "USDM", "decompose requirements", "requirements definition", "要件定義", "要件を整理", "要件分解".
Complete Problem-Based Software Requirements Specification methodology following Gorski & Stadzisz research. Use when you need to perform requirements engineering from business problems to functional requirements with full traceability.
AI SDLC business analysis workflow. Use when an AI assistant needs to frame a feature or change before implementation, derive actors, workflows, business rules, assumptions, acceptance criteria, and richer spec context for requirements and design. 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.
You MUST use this when work has to be designed before any code is written - a new feature, a change to product or UX behavior, an architecture change, success criteria that are still unclear, or a choice between two materially different approaches - including a spec revision after a document review. A fully specified mechanical refactor, a localized fix whose expected behavior is known, or a config change with a single possible outcome gets no spec; the skill hands it to implement in one sentence. Turns the request into a spec with checkable acceptance criteria behind an explicit approval. Replaces devflow scope-triage.
Turn ambiguous coding requests into verified Cursor Agent implementation specs. Use when the user wants requirements, an implementation plan, or a spec before coding; include source checks, needed ADRs, and agreed delivery. Do not use for already specified direct implementation or memory cleanup.
Write unambiguous specifications using EARS (Easy Approach to Requirements Syntax) patterns. Provides 6 sentence templates that eliminate ambiguity: Ubiquitous, Event-driven, State-driven, Unwanted behavior, Optional feature, and Complex. Use when: "EARS", "specification writing", "write specs", "仕様を書く", "EARS記法", "仕様を明確化", "requirements specification", "unambiguous specification".