46ki75 Skills

Expert guidance for building browser extensions with WXT — covering project setup, file-based entrypoints (background, content scripts, popup, options, side panel), the unified browser API, auto-imports, manifest configuration, storage, content script UIs (integrated, shadow root, iframe), frontend frameworks (React, Vue, Svelte, Solid), multi-browser targeting (Chrome, Firefox, Safari, Edge), MV2/MV3 compatibility, and publishing. Use this skill whenever someone is building, debugging, or reviewing a WXT extension, asking how entrypoints work, trying to add a popup or content script, using `defineBackground`, `defineContentScript`, `createShadowRootUi`, `storage`, `wxt.config.ts`, or wondering why their extension code breaks at build time. Always invoke this skill for any question that mentions WXT, wxt.config.ts, defineBackground, defineContentScript, or refers to building a Chrome/Firefox extension using WXT, even if the question seems simple.

Maintains a Known Error Database (KEDB) of documented errors, symptoms, workarounds, root causes, and solutions. READ mode (autonomous): on any error, stack trace, test/build failure, or unexpected behavior, consult `kedb-index.md` before investigating from scratch. Triggers: "this error", "is this a known issue", "check KEDB", "known error database", "have we seen this before". WRITE mode (human-invoked only): create or update entries via `kedb-template.md` when asked to "add to KEDB", "document this issue", "record this fix", "log this error", "save this workaround", or "create a KEDB entry". Always update `kedb-index.md` after writing. Never auto-create entries.

Expert guidance for Qwik (v1 and v2) and its meta-framework — Qwik City in v1, Qwik Router in v2 — covering resumability, components, signals, stores, tasks, context, QRLs, file-based routing, route loaders/actions, middleware, endpoints, layouts, async data (useResource$/useAsync$/Suspense), v1→v2 migration, and deployment. Use whenever someone writes, debugs, or reviews Qwik code, asks about useSignal, useStore, useTask$, useAsync$, routeLoader$, routeAction$, component$, the $ suffix, resumability vs hydration, migrating v1 to v2, server-side data fetching, REST/JSON endpoints, middleware, or deployment. Always invoke for any question mentioning Qwik, Qwik City, Qwik Router, QRL, routeLoader$, routeAction$, useVisibleTask$, useAsync$, noSerialize, or the resumable architecture.

Create diagrams and visualizations using Mermaid syntax. Use when users request flowcharts, sequence diagrams, class diagrams, state diagrams, entity relationship diagrams, Gantt charts, pie charts, mindmaps, timelines, user journey maps, Git graphs, quadrant charts, or requirement diagrams. Also use when users want to visualize processes, workflows, systems, data structures, or relationships.

Markdown linting and automated fixing using markdownlint-cli2. Use when Claude needs to: (1) Check markdown files for style issues, (2) Fix markdown formatting problems, (3) Ensure markdown follows best practices, (4) Validate markdown documents, or (5) Apply consistent markdown styling

Expert guidance for the Toasty Rust ORM (v0.7): `#[derive(toasty::Model)]` with `#[key]`, `#[auto]`, `#[unique]`, `#[index]`, `#[has_many]`, `#[belongs_to]`, `#[has_one]`, `#[version]`; `Deferred` for lazy relations/columns vs. bare types for eager; multi-step `via` relations; `create!`, `update!`, `find_by_*`, `filter_*` macros; increment/decrement/add/subtract update ops; `toasty::Json`, embedded types, scalar `Vec` arrays; raw SQL via `toasty::sql::*`; `tracing`; batch ops; transactions; per-driver behavior for SQLite, Turso, PostgreSQL, MySQL, DynamoDB (`.ilike()` is PostgreSQL-only). Internals: app/db schema, `schema::diff::Schema`, the query pipeline (Simplify → Lower → Plan → Execute), the driver trait. Always invoke for any question mentioning Toasty, `toasty::Model`, `toasty::Db`, `toasty::Deferred`, `toasty::Json`, `toasty::create!`, `toasty::update!`, `toasty::sql`, code under `submodules/toasty/`, `crates/toasty-app/`, or any Rust code importing `toasty`.

Expert guidance for the A2A (Agent2Agent) Protocol — an open HTTP-based protocol for independent, opaque AI agents to collaborate as peers. Covers v1.0: data model (Agent Card, Task, Message, Part, Artifact, Extension); operations (`SendMessage`, `SendStreamingMessage`, `GetTask`, `ListTasks`, `CancelTask`, `SubscribeToTask`, the `*TaskPushNotificationConfig` family, `GetExtendedAgentCard`); task lifecycle and multi-turn flows; three protocol bindings (JSON-RPC, gRPC, HTTP+JSON/REST) and custom bindings; agent discovery (well-known URI, registries, signed Agent Cards); enterprise security (TLS, OAuth 2.0 / OIDC / mTLS, in-task authorization); SSE streaming and webhook push notifications; the extension framework; A2A↔MCP relationship; and the six SDKs (`a2a-python`, `a2a-js`, `a2a-java`, `a2a-go`, `a2a-dotnet`, `a2a-rs`). Always invoke for A2A, Agent2Agent, agent card, A2A tasks / messages / artifacts / parts, push notification configs, A2A extensions, Agent Card signing, or any of the six SDK package names.

Expert guidance for the Model Context Protocol (MCP), the JSON-RPC 2.0 protocol connecting LLM apps to external tools and data. Covers spec versions 2024-11-05 through 2025-11-25 (architecture, transports, Resources, Prompts, Tools, Sampling, Roots, Elicitation, Tasks, lifecycle, OAuth 2.1, utilities) plus the official language SDKs: the Rust SDK `rmcp` (full guide — `ServerHandler`/`ClientHandler`, `#[tool_router]`/`#[tool_handler]`/`#[task_handler]` macros, `StreamableHttpService`, Cargo features, SEP-1319 tasks, `tokio::io::duplex` test harness) and the TypeScript SDK `@modelcontextprotocol/sdk` (incl. the `pkce-challenge` Vite/bundler resolver failure). Use when building MCP servers or clients in any language, working with transports or OAuth, or debugging SDK-specific issues. Always invoke for questions mentioning MCP, modelcontextprotocol, `rmcp`, tools/list, tools/call, sampling/createMessage, elicitation/create, Streamable HTTP, Mcp-Session-Id, `pkce-challenge`, or `@modelcontextprotocol/sdk`.

Expert guidance for implementing the A2UI (Agent to UI) protocol — covering surfaces, components, data binding, catalogs, message streams (v0.9 / v0.9.1: createSurface / updateComponents / updateDataModel / deleteSurface; v1.0 adds callFunction / functionResponse / actionResponse; v0.8: surfaceUpdate / dataModelUpdate / beginRendering), the A2A extension binding, custom component catalogs (schemas, renderers, negotiation, versioning, graceful degradation), client/renderer architecture (MessageProcessor / SurfaceModel / ComponentImplementation / Binder), custom functions, the sendDataModel flag, surfaceProperties, and v0.8 → v0.9 → v0.9.1 → v1.0 migration. Always invoke for any question that mentions A2UI, surfaceUpdate, beginRendering, createSurface, updateComponents, updateDataModel, sendDataModel, callFunction, functionResponse, actionResponse, surfaceProperties, a2uiClientCapabilities, supportedCatalogIds, catalogId, basic_catalog, formatString, ChildList, ComponentId, or agent-driven UI streaming.

Expert guidance for AG-UI (Agent–User Interaction Protocol) — the open, event-based protocol connecting AI agents to user-facing apps. Covers the run lifecycle, event types (lifecycle, text, tool-call, state, activity, reasoning), RunAgentInput, AbstractAgent/HttpAgent, multimodal messages, shared state via STATE_SNAPSHOT/STATE_DELTA (RFC 6902 JSON Patch), frontend-defined tools, interrupt-aware HITL with RunAgentInput.resume, encrypted reasoning for ZDR, capability discovery, middleware, serialization with parentRunId branching, the TypeScript (`@ag-ui/client`, `@ag-ui/core`) and Python (`ag-ui-protocol`) SDKs plus community SDKs. Use whenever someone builds or consumes AG-UI, wires up CopilotKit, LangGraph, CrewAI, Mastra, Pydantic AI, LlamaIndex, Agno, AG2, Microsoft Agent Framework, Google ADK, AWS Strands, or Bedrock AgentCore, or mentions AG-UI, ag_ui, RunStartedEvent, STATE_DELTA, TOOL_CALL_START, REASONING_*, AbstractAgent, HttpAgent, or EventEncoder. Always invoke.

Eval-driven prompt refinement for Anthropic / Claude API prompts. Turn vibes-based prompt tweaking into a measurable loop: look at real failures, build a dataset, pick a grading approach (code-graded, model-graded, or both), scaffold a runnable eval (Anthropic SDK, `promptfoo`, or Claude Console), run it, analyze failures by category, propose targeted edits, re-run, compare. Invoke whenever the user wants to evaluate, improve, compare, A/B test, regress-test, or iterate on a prompt — even if they don't say "eval". Phrases like "is this prompt good?", "make this prompt better", "compare these two prompts", "my prompt fails on X", "tests for this prompt", "edge cases this classifier misses", "switch to Haiku without regressions", "evaluate this RAG answer", "grade this agent's tool use" all qualify. Covers code-graded, model-graded (LLM-as-judge), RAG-specific (faithfulness, answer-relevance), tool-use / agent evals, dataset design, and production patterns (capability vs regression suites, CI/CD).

Expert guidance for implementing the View Transition API — covering same-document (SPA) transitions with `document.startViewTransition()`, cross-document (MPA) transitions with `@view-transition`, customizing animations via CSS pseudo-elements (`::view-transition-old`, `::view-transition-new`, `::view-transition-group`), per-element animations with `view-transition-name`, JavaScript control via the `ViewTransition` promises (`ready`, `finished`, `updateCallbackDone`), context-aware transition types with `:active-view-transition-type()`, and graceful fallbacks for unsupported browsers. Use this skill when someone wants page transition animations, shared-element transitions, slide/fade/circular-reveal effects, or asks about `startViewTransition`, `@view-transition`, `view-transition-name`, `::view-transition-*` pseudo-elements, or the `ViewTransition` object — even if they just say "smooth page transitions" or "animate between routes".

Write, format, and review conventional commit messages following the Conventional Commits 1.0.0 spec and this project's house rules. Use this skill whenever the user asks you to write a commit message, format or fix a commit message, check whether a commit message is valid, explain the commit convention, suggest a type or scope, or describe what changes to make before committing. Trigger even when the user doesn't say "conventional commits" explicitly — phrases like "how should I word this commit", "what type is this", "write a commit for", "is this commit message ok", or just pasting a raw description and asking for a commit all count.

Org-internal engineering standards for this organization's projects. Invoke whenever scaffolding a new repo, auditing an existing one, setting up CI, or configuring tooling files like `Cargo.toml`, `rust-toolchain.toml`, `justfile`, `.editorconfig`, `.markdownlint-cli2.yaml`, `tsconfig.json`, `package.json`, `pnpm-lock.yaml`, `bunfig.toml`, `pyproject.toml`, `uv.lock`, `.python-version`, or `*.tf`. Also invoke for work involving `axum`, `utoipa`, `markdownlint-cli2`, `uv`, `ruff`, `pyright`, `pytest`, or Node package-manager setup (pnpm is the org default). Rust and Python are fully documented: Cargo workspace inheritance, MSRV pinning, `just` as task runner, `cargo-llvm-cov` coverage, hermetic-vs-live test split, Axum + utoipa OpenAPI; uv workspaces, packaged `src` layout, ruff, pyright strict, pytest live-marker tiers. TypeScript, Node.js, Bun, Terraform, Rust libraries, and Rust GraphQL are stubs — invoke anyway so the user can define the convention rather than receive an improvised one.

Eval-driven prompt refinement that runs entirely inside Claude Code via the Agent/Task tool — no Python, no SDK, no API key. Each candidate run and each judge call executes in an isolated subagent with a fresh context window, so samples are independently graded and the main session stays focused on synthesis and iteration. Trivially parallel: spawn N candidate + M judge subagents in one assistant message. Invoke when the user wants to evaluate, A/B test, regress-test, or iterate on a prompt directly inside Claude Code, especially when they reference subagents, the Task/Agent tool, or "test this prompt without writing code". Phrases like "use Claude Code to evaluate this prompt", "spawn subagents to test", "parallel-test these variants", "A/B these prompts in Claude Code", "grade this rubric with subagents", or "iterate this prompt with fresh contexts" qualify. Pairs with the broader `prompt-evaluation` skill for shared dataset-design and binary-judge methodology.