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Found 10,447 Skills
Scrapin.io integration. Manage data, records, and automate workflows. Use when the user wants to interact with Scrapin.io data.
Awesome Support integration. Manage data, records, and automate workflows. Use when the user wants to interact with Awesome Support data.
Worldline integration. Manage data, records, and automate workflows. Use when the user wants to interact with Worldline data.
Phase 2 of the feature workflow — Write code according to the implementation sequence in {slug}-design.md, and submit a completion report in a unified format for user review after finishing. Prerequisites: {slug}-design.md has been approved (standard design includes test design, or fastforward design includes acceptance criteria), and {slug}-checklist.yaml exists in the same directory. Trigger scenarios: User says "The plan is confirmed, start implementation", "Write code according to the plan", "Start working". If you encounter situations not covered by the plan during implementation (new concepts, out-of-scope files, need for patch branches), proactively stop and discuss with the user based on the plan, do not proceed forcefully.
Xata integration. Manage data, records, and automate workflows. Use when the user wants to interact with Xata data.
Privacy Dynamics integration. Manage data, records, and automate workflows. Use when the user wants to interact with Privacy Dynamics data.
Jack Henry integration. Manage data, records, and automate workflows. Use when the user wants to interact with Jack Henry data.
Jumio integration. Manage data, records, and automate workflows. Use when the user wants to interact with Jumio data.
Document the pitfalls encountered or good practices discovered during this work into searchable learning documents, so that both AI and humans can look them up when similar tasks arise in the future. Two tracks: The pitfall track records experiences where "things should have worked but didn't" — bugs, configuration traps, environment issues, integration failures; The knowledge track records findings that "should be the default approach going forward" — best practices, workflow improvements, reusable patterns. Trigger scenarios: Proactively prompt for input when wrapping up feature-acceptance or issue-fix, or when the user says phrases like "document knowledge", "learning", "document learnings", "record this experience". Spec documents record what was done and how it was done, while learning documents record what pitfalls were encountered / what was learned — the two complement each other and are not interchangeable.
Fix a bug with systematic debugging, TDD, and PR workflow
Generate deterministic publication-quality architecture, workflow, and pipeline diagrams from structured JSON (FigureSpec) into editable SVG. Use when user says "架构图", "workflow 图", "pipeline 图", "确定性矢量图", "figure spec", "draw architecture", or needs precise, editable, publication-ready vector diagrams. Preferred over AI illustration for formal architecture/workflow figures.
Ultra-lightweight channel for refactor processes - used when changes are obviously too small to justify the full scan → design → apply three-stage workflow. AI directly identifies 1-3 low-risk optimization points, confirms with the user once, modifies in-place using classic methods, and validates itself by running tests. No scan checklist, no design documentation, no multi-step HUMAN verification required. Trigger scenarios: When the user says "quick refactor", "small refactor", "simply optimize XX function", "modify directly", "skip all those steps", and the scope of changes is clearly limited to a single function/single component, with tests available for self-validation.