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Found 536 Skills
Update an existing specification file for the solution, optimized for Generative AI consumption based on new requirements or updates to any existing code.
Craft elegant technical specifications with ASCII artistry, flow diagrams, and the Grove voice. The swan glides with purpose—vision first, then form, then perfection. Use when creating specs, reviewing documents, or transforming technical plans into storybook entries.
Create git commits following Conventional Commits specification with project-specific branch naming rules. Use for commit message generation, changelog, and versioning.
Structured specification with explicit scope boundaries: user stories, acceptance criteria, out-of-scope definition, risks, and estimation. Positions before feature-design in the feature lifecycle pipeline. Use when: "write spec", "user stories", "define requirements", "scope this", "what should this do", "acceptance criteria", "define scope"
Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.
Ralph Wiggum-inspired automation loop for specification-driven development. Orchestrates task implementation, review, cleanup, and synchronization using a Python script. Use when: user runs /loop command, user asks to automate task implementation, user wants to iterate through spec tasks step-by-step, or user wants to run development workflow automation with context window management. One step per invocation. State machine: init → choose_task → implementation → review → fix → cleanup → sync → update_done. Supports --from-task and --to-task for task range filtering. State persisted in fix_plan.json.
Generate per-asset visual specifications and AI generation prompts from GDDs, level docs, or character profiles. Produces structured spec files and updates the master asset manifest. Run after art bible and GDD/level design are approved, before production begins.
Gate 2 sub-skill - validates uncertain mappings from Gate 1 and confirms all field specifications through testing.
Conduct deep research using NotebookLM integration — upload documents, query with citation-backed answers, synthesize findings, and produce infographic-style presentations. Output in Markdown, HTML/reveal.js slides, or Mermaid diagrams with visual hierarchy design specifications.
Spec Status - displays pipeline progress dashboard for a single specification showing document statuses, blockers, and next action.
Review Specification - validates documents for completeness, quality, and consistency against the codebase. Use when checking spec quality at any pipeline stage.
Extract technical implementation evidence from developed code projects, generate algorithm/software specification-style technical disclosure documents around candidate patent solutions, and use the two-step method of "Claim Layout Card → Invention Patent Draft" to continue generating draft materials for Chinese invention patents that are close to the declarable version. Trigger scenarios include: writing technical disclosure documents after reading code repositories, mapping manually summarized patent solutions to specific implementations, mining patentable technical solutions from code, and preparing claim layouts and invention patent drafts for patent attorneys.