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Found 551 Skills
Bootstrap, install, and operate an external task-management CLI as the source of truth for agent execution tracking (instead of built-in todos). Provides the abstraction layer between spec-management intent (implementation plans and tasks) and concrete CLI commands. MUST be invoked when any implementation-tier artifact (SPEC, STORY, BUG) comes up for implementation — create a tracked plan before writing code. Optional but recommended for complex SPIKEs. For coordination-tier artifacts (EPIC, VISION, JOURNEY), spec-management must decompose into implementable children first — this skill tracks the children, not the container. Also use for standalone tasks that require backend portability, persistent progress across agent runtimes, or external supervision. Use this skill whenever the user asks to track tasks, create an implementation plan, check what to work on next, see task status, manage dependencies between work items, or close/abandon tasks — even if they don't mention "execution tracking" explicitly.
Debug applications using the dbg CLI debugger. Supports Node.js (V8/CDP), Bun (WebKit/JSC), and native code via LLDB (DAP). Use when: (1) investigating runtime bugs by stepping through code, (2) inspecting variable values at specific execution points, (3) setting breakpoints and conditional breakpoints, (4) evaluating expressions in a paused context, (5) hot-patching code without restarting (JS/TS), (6) debugging test failures by attaching to a running process, (7) debugging C/C++/Rust/Swift with LLDB, (8) any task where understanding runtime behavior requires a debugger. Triggers: "debug this", "set a breakpoint", "step through", "inspect variables", "why is this value wrong", "trace execution", "attach debugger", "runtime error", "segfault", "core dump".
This skill should be used when the user wants to "package an MCP server", "bundle an MCP", "make an MCPB", "ship a local MCP server", "distribute a local MCP", discusses ".mcpb files", mentions bundling a Node or Python runtime with their MCP server, or needs an MCP server that interacts with the local filesystem, desktop apps, or OS and must be installable without the user having Node/Python set up.
Vercel Functions expert guidance — Serverless Functions, Edge Functions, Fluid Compute, streaming, Cron Jobs, and runtime configuration. Use when configuring, debugging, or optimizing server-side code running on Vercel.
Odoo frontend JavaScript patterns for website themes. Covers publicWidget framework (complete pattern with editableMode handling), Owl v1/v2 component patterns, _t() translation best practices, Bootstrap 4-to-5 migration, version detection, and critical development rules. Supports Odoo 14-19. <example> Context: User wants to create a publicWidget user: "Create a publicWidget for my Odoo website" assistant: "I will create a publicWidget with editableMode handling and proper cleanup." <commentary>publicWidget creation.</commentary> </example> <example> Context: User asks about Owl components user: "How do I create an Owl component in Odoo 18?" assistant: "I will show the Owl v2 pattern with static template and props." <commentary>Owl component pattern.</commentary> </example> <example> Context: User needs help with translations user: "How do I translate JavaScript strings in Odoo?" assistant: "Use _t() at DEFINITION TIME for static labels, not runtime wrappers." <commentary>Translation best practices.</commentary> </example> <example> Context: User migrating Bootstrap classes user: "Convert Bootstrap 4 classes to Bootstrap 5 for Odoo 17" assistant: "Replace ml-* with ms-*, mr-* with me-*, text-left with text-start." <commentary>Bootstrap migration.</commentary> </example>
Lovrabet Runtime CLI — Manage application directories, dataset queries, data CRUD, SQL execution, and BFF invocations via the lovrabet command. Trigger words: Cloud Diagram, lovrabet, lovrabet-cli, app list, dataset, data filter, data getOne, create, update, delete, sql exec, bff exec, accessKey, compress, jq.
Eight-axis judgment code review for the current diff — Correctness, Simplification, Tests, Documentation, Style, Intent, Design/API, Performance (+ Coherence on metadata changes). Five-phase pipeline scope → deterministic tool battery (npx/uvx-preferred, zero-install for the JS + Python majority) → 8 parallel LLM axis reviewers → Haiku validators on sub-80 findings (verbatim rubric, ≥80 threshold) → synthesis with no-silent-drop + Conventional Comments JSONL. Every report closes with "What I did NOT check" (security → /security-review, runtime perf, flaky detection). Opt-in flags `--verify-build`, `--mutation-test`, `--reconcile`, `--apply-safe`. Public-skill posture — zero auto-install, graceful skip on missing native tools.
Grounding an assistant in your app with assistant-ui copilots (@assistant-ui/react). Use when steering assistant behavior with useAssistantInstructions, feeding lazy app-state context via useAssistantContext({ getContext }), exposing rendered components with makeAssistantVisible(Component, { clickable, editable }), building two-way interactable state with useAssistantInteractable and Interactables(), or registering instructions and tools imperatively through useAui().modelContext().register({ getModelContext }). Reach for this when the assistant should read the current page, click or edit UI, or read and update component state through auto-generated update_{name} tools. For LLM tools and tool-call UI use the tools skill; for runtime and thread state use the runtime skill.
Sets up and manages Postgres using the clickhousectl CLI — runs a local Docker-backed Postgres for development, and creates and operates managed ClickHouse Cloud Postgres services (connections, TLS, runtime config, read replicas, failover, point-in-time restore). Use when the user wants a Postgres or PostgreSQL database for their application, a local Postgres dev environment, psql access, or a managed/production Postgres in ClickHouse Cloud, or mentions moving a local Postgres to production.
Bridge between OpenWork UI and OpenCode runtime
Amazon Bedrock AgentCore platform for building, deploying, and operating production AI agents. Covers Runtime, Gateway, Browser, Code Interpreter, and Identity services. Use when building Bedrock agents, deploying AI agents to production, or integrating with AgentCore services.
Coordinates 3 specialized audit workers (query efficiency, transaction correctness, runtime performance). Researches DB/ORM/async best practices, delegates parallel audits, aggregates results into single Linear task in Epic 0.