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Agent skills
pydantic-ai

Topic: pydantic-ai

127 skills in this topic.

rag Search, retrieve and analyze documents using RAG (Retrieval Augmented Generation).
ggozad/haiku.rag 510
rag-rlm Computational analysis of the knowledge base via code execution in a sandboxed Python interpreter. Use for questions requiring counting, aggregation, statistics, data traversal, comparison across documents, or any task best answered by writing Python code. Examples: "how many pages?", "compare table 3 across documents", "calculate average word count", "extract all email addresses".
ggozad/haiku.rag 510
pydanticai-docs Use this skill whenever the user is working with the Pydantic AI framework — including building AI agents, defining structured outputs with Pydantic models, wiring up tools/function calling, configuring model providers (OpenAI, Anthropic, Gemini, etc.), managing dependencies via agent context, handling streaming responses, or debugging agent runs. Trigger this skill even for adjacent tasks like "how do I make my agent return JSON", "set up a multi-step agent", "add a tool to my agent", or "validate LLM output with Pydantic" — any time Pydantic AI is mentioned or implied as the target framework.
DougTrajano/pydantic-ai-skills 207
arxiv-search Search arXiv preprint repository for papers in physics, mathematics, computer science, quantitative biology, and related fields.
DougTrajano/pydantic-ai-skills 207
web-research Use this skill for requests related to web research; it provides a structured approach to conducting comprehensive web research.
DougTrajano/pydantic-ai-skills 207
fetch-pr-feedback Fetch review comments from a PR and evaluate with receive-feedback skill
existential-birds/beagle 44
deepagents-architecture Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.
existential-birds/beagle 44
fix-llm-artifacts Applies fixes from a prior review-llm-artifacts run, with safe/risky classification
existential-birds/beagle 44
review-ios Comprehensive iOS/SwiftUI code review with optional parallel agents
existential-birds/beagle 44
gen-test-plan Analyze repo, detect stack, trace changes to user-facing entry points, generate YAML test plan
existential-birds/beagle 44
widgetkit-code-review Reviews WidgetKit code for timeline management, view composition, configurable intents, and performance. Use when reviewing code with import WidgetKit, TimelineProvider, Widget protocol, or @main struct Widget.
existential-birds/beagle 44
agent-architecture-analysis Perform 12-Factor Agents compliance analysis on any codebase. Use when evaluating agent architecture, reviewing LLM-powered systems, or auditing agentic applications against the 12-Factor methodology.
existential-birds/beagle 44
adr-writing Write Architectural Decision Records following MADR template. Applies Definition of Done criteria, marks gaps for later completion. Use when generating ADR documents from extracted decisions.
existential-birds/beagle 44
axum-code-review Reviews axum web framework code for routing patterns, extractor usage, middleware, state management, and error handling. Use when reviewing Rust code that uses axum, tower, or hyper for HTTP services. Covers axum 0.7+ patterns including State, Path, Query, Json extractors.
existential-birds/beagle 44
wish-ssh-code-review Reviews Wish SSH server code for proper middleware, session handling, and security patterns. Use when reviewing SSH server code using charmbracelet/wish.
existential-birds/beagle 44
react-router-v7 React Router v7 best practices for data-driven routing. Use when implementing routes, loaders, actions, Form components, fetchers, navigation guards, protected routes, or URL search params. Triggers on createBrowserRouter, RouterProvider, useLoaderData, useActionData, useFetcher, NavLink, Outlet.
existential-birds/beagle 44
pydantic-ai-model-integration Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.
existential-birds/beagle 44
pydantic-ai-dependency-injection Implement dependency injection in PydanticAI agents using RunContext and deps_type. Use when agents need database connections, API clients, user context, or any external resources.
existential-birds/beagle 44
liveview-code-review Reviews Phoenix LiveView code for lifecycle patterns, assigns/streams usage, components, and security. Use when reviewing LiveView modules, .heex templates, or LiveComponents.
existential-birds/beagle 44
review-feedback-schema Schema for tracking code review outcomes to enable feedback-driven skill improvement. Use when logging review results or analyzing review quality.
existential-birds/beagle 44
draft-docs Generate first-draft technical documentation from code analysis
existential-birds/beagle 44
tutorial-docs Tutorial patterns for documentation - learning-oriented guides that teach through guided doing
existential-birds/beagle 44
go-data-persistence Data persistence patterns in Go covering raw SQL with sqlx/pgx, ORMs like Ent and GORM, connection pooling, migrations with golang-migrate, and transaction management. Use when implementing database access, designing repositories, or managing schema migrations.
existential-birds/beagle 44
ai-elements Vercel AI Elements for workflow UI components. Use when building chat interfaces, displaying tool execution, showing reasoning/thinking, or creating job queues. Triggers on ai-elements, Queue, Confirmation, Tool, Reasoning, Shimmer, Loader, Message, Conversation, PromptInput.
existential-birds/beagle 44