Loading...
Loading...
Found 3,012 Skills
Interactive interview to create comprehensive Product Requirements Documents with User Stories and Use Cases, starting with the problem, not the solution
Draft or update architecture documents under `easysdd/architecture/` — describe what a subsystem/module looks like currently, how it is divided, and how external interfaces operate, to provide pre-positioning input for subsequent feature-design. Information sources include code + user materials (oral accounts, scattered documents, compound deposits, existing decisions), and the output can be reverse-validated by anchoring to specific `file:line`. Two modes: new (draft a new architecture document from scratch), update (refresh an existing document based on the latest code status and new user materials). Single-target rule — only modify one document at a time. Trigger scenarios: user says "fill in an architecture doc", "draft an architecture document", "update the architecture directory", "write down the structure of this module", or when it is found that "something that should be in the architecture is missing" during the feature-design / feature-acceptance phase.
Document the pitfalls encountered or good practices discovered during this work into searchable learning documents, which can be accessed by both AI and humans when similar tasks arise in the future. Two tracks: The pitfall track records experiences where "things should have worked but didn't" — including bugs, configuration traps, environment issues, and integration failures; The knowledge track records findings that "should be the default approach going forward" — including best practices, workflow improvements, and reusable patterns. Trigger scenarios: Proactively prompt at the end of feature-acceptance or issue-fix workflows, or when the user mentions phrases like "document knowledge", "learning", "document learnings", or "record this experience". Spec documents record what was done, while learning documents record what pitfalls were encountered / what was learned — they complement each other and are not interchangeable.
STUB — installed at ~/openclaw/skills/healthcheck/SKILL.md
Generate images with Venice. Covers POST /image/generate (Venice-native), POST /images/generations (OpenAI-compatible), GET /image/styles (style presets), request fields (prompt, dimensions, cfg_scale, seed, variants, style_preset, aspect_ratio, resolution, safe_mode, watermark), and response formats.
Write a new doc-site page (guide, tutorial, reference, concept) following the project's docs conventions. Use when user says 'write a doc', 'add a page to the docs site', 'create a guide for X', 'write a tutorial', 'document this concept', or 'add reference for our API'. Do NOT use for feature-specific end-user docs (use document-feature), ADRs (use document-decision), or CLAUDE.md updates (use create-or-audit-claude-md).
When the user wants to create or update their Unreal Engine project context document. Use when the user says 'project context,' 'set up context,' 'UE context,' 'configure project,' or wants to avoid repeating their project setup across UE development tasks. Creates `.agents/ue-project-context.md` that all other UE skills reference. See related skills footer for skills that depend on this context.
Create a ***plain import module that provides shared definitions, implementation reqs, and test reqs for other modules to import. Use when the user wants to create a new .plain file that contains only definitions, implementation reqs, and/or test reqs — no functional specs.
Use when a Beat change is implemented and ready to archive — not for verifying implementation
Create a complete SPEC from scratch through an exhaustive requirements interview before any planning or implementation. Use this skill whenever the user asks to create, define, clarify, scope, or write a spec/SPEC/PRD/requirements document from an idea, especially when they want to avoid assumptions, start at "step zero," or prepare input for later planning workflows. This skill must question goals, requirements, constraints, edge cases, business rules, and acceptance criteria before drafting the final spec.
Organize scientific research text based on user-provided research materials and verifiable sources, determine the writing functions of chapters, sections, and paragraphs, and fill in the gaps between evidence, explanations, significance, and research questions. Use when the user asks for “write PhD chapters”, “revise paper arguments”, “write academic paragraphs based on sources”, “revise scientific writing according to supervisor feedback”, or requests the rw-phd-write workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Log genuine, recurring repository friction to .agents/PAPERCUTS.md — confusing setup, a flaky repo command or script, a misleading in-repo error, stale generated files, or a non-obvious gotcha that will cost the next contributor time. Also use to review, deduplicate, and resolve existing entries. Gate hard before logging: only friction the repository itself can fix counts. Never log the agent's own sandbox/permission errors, shell-scripting mistakes, transient flakiness, or third-party tool quirks the repo can't change.