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Found 9,824 Skills
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 go back to discuss the plan instead of pushing forward blindly.
Ultra-lightweight channel for refactor processes - used when changes are clearly too small to go through 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: User says "quick refactor", "small refactor", "simply optimize XX function", "modify directly", "skip the extra steps", and the scope of changes is clearly localized to a single function / single component with test coverage for self-validation.
Phase 1 of the feature workflow — Draft a design document for the new feature, serving as the sole input for subsequent implementation and acceptance. First gather evidence (read architecture docs, review relevant code, grep to prevent term conflicts, check archives), then write a complete first draft in one go (including YAML frontmatter + three-tier structure + test design), submit it to the user for overall review, and iterate until approval. After approval, extract {slug}-checklist.yaml from {slug}-design.md for use in the next two phases. Trigger scenarios: "Start designing the solution", "Write design doc", "Prepare to implement XX", with the prerequisite that you already know what to do, who it's for, and how to define success.
Tray.ai platform help — enterprise iPaaS with 700+ connectors, Intelligent iPaaS, Enterprise Core governance, Merlin Agent Builder for AI agents, Tray Embedded for SaaS vendors, GraphQL API, Connector Development Kit. Use when Tray bill keeps climbing and task consumption is unpredictable, workflows fail with unclear errors and debugging feels opaque, evaluating Tray vs Workato vs MuleSoft vs Boomi, embedding integrations into a SaaS product via Tray Embedded, building Merlin AI agents, or configuring the GraphQL Embedded API and solution instances. Do NOT use for simple Zapier/Make automations (use /sales-integration), Workato-specific questions (use /sales-workato), or MuleSoft-specific questions (use /sales-mulesoft).
Threat Stack integration. Manage data, records, and automate workflows. Use when the user wants to interact with Threat Stack data.
Campaign Cleaner integration. Manage data, records, and automate workflows. Use when the user wants to interact with Campaign Cleaner data.
MSG91 integration. Manage data, records, and automate workflows. Use when the user wants to interact with MSG91 data.
Fix GitHub Actions failures by analyzing recent workflow runs and applying fixes in a worktree
Gorgias integration. Manage data, records, and automate workflows. Use when the user wants to interact with Gorgias data.
SendOwl integration. Manage data, records, and automate workflows. Use when the user wants to interact with SendOwl data.
PushBots integration. Manage data, records, and automate workflows. Use when the user wants to interact with PushBots data.
Guide for AI agents to source electronic components using parts-mcp — tool sequencing, decision patterns, and multi-step workflows