pydantic

Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), workspaces, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add…

Extend Pydantic AI agents with capabilities from pydantic-ai-harness -- the Coder coding agent, file and shell tools in local or sandboxed workspaces (Modal, E2B, Sprites, smolvm), Code Mode, sub-agents and planning, memory and skills, context compaction,…

Add support for a newly-released language or image generation model in pydantic-ai (e.g. openai:gpt-5.6, anthropic:claude-sonnet-5, openai:gpt-image-2). Use when a provider ships a new model id and you need to wire literals, profile flags, adapters, and tests…

Open and advance a PR — write a current title and body, label it, review every push subject to the mechanical-refresh exception, watch CI, and triage every comment. Use whenever you open a PR or push a commit to one.

Migrate Python LangChain Deep Agents applications to Pydantic AI and Pydantic AI Harness. Use when the source uses the upstream `deepagents` package or demonstrably reproduces its middleware, backends, skills, memory, subagents, or sandbox contracts. Use…

Find and address unresolved PR review comments for the current branch, then continue the canonical push, reply, reaction, and resolution workflow.

Use as the final documentation gate before a `pydantic-ai-harness` capability PR merges. Verifies that a user-facing change keeps the capability README under `src/pydantic_ai_harness/pydantic_ai_harness/` and its `docs/harness/` page in sync with each other…

Migrate Python LangChain, LangGraph, or Deep Agents applications to Pydantic AI and, when the source uses harness features, Pydantic AI Harness. Use for LangChain agents, chains, LCEL, direct LangGraph graphs, persistence, interrupts, streaming, and…

How to write prose that reads like human technical documentation rather than LLM output. Use whenever writing or editing docstrings, comments, limitations/ docs, READMEs, commit messages or PR descriptions, and when prose reads as smooth, salesy or generic.

Read the review comments left by the known agent reviewers and repo maintainers on the current PR, resolve and reply. Use when asked to deal with PR comments, review feedback, or bot review findings.

Security review of the current branch against its merge base — sandbox escapes, memory errors, panics and resource-limit bypasses. Use when reviewing changes for security risk, or before merging anything touching the heap/ module, path_security.rs, the wire…

Fetch coverage diff from Codecov for the current branch or a specific PR. Shows uncovered lines, patch coverage, and overall coverage change.

Run and test Python code in a dedicated playground directory. Use when you need to execute Python scripts, test code snippets, investigate CPython behavior, or experiment with Python without affecting the main codebase.

Review the current branch against its merge base for bugs, CPython divergence, sandbox escapes, resource-limit escapes, performance regressions, verbose comments and missing ./limitations/ or docs/ updates. Use for a general pre-merge review of a branch or PR.

Check whether the common Python code an LLM would plausibly write still works on this branch, testing real cases in ./playground against CPython. Use to find behaviour that diverges from CPython or trips up ordinary idiomatic code.

Rewrite excessively verbose comments and docstrings added by this branch, and delete tautologous ones. Use after writing a feature to tighten up its comments; this skill edits code rather than only reporting.

Migrate Python LangChain, LangGraph, or Deep Agents applications to Pydantic AI and, when the source uses harness features, Pydantic AI Harness. Use for LangChain agents, chains, LCEL, direct LangGraph graphs, persistence, interrupts, streaming, and…

Add Pydantic Logfire observability to application code — traces, logs, metrics, and AI/agent spans. Use when the user asks to add or configure Logfire, observability, tracing, logging, or monitoring; maximize useful telemetry; or understand what an app is…

Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add…

Extend Pydantic AI agents with batteries-included capabilities from pydantic-ai-harness -- Code Mode (collapse many tool calls into one sandboxed Python execution), a filesystem and shell, sub-agents, planning, context compaction, and more. Use when the user…

Query and analyze Logfire telemetry data — traces, logs, spans, metrics, summaries, and SQL results. Use this skill when the user asks to "query logfire", "search traces", "find logs", "query data", "search spans", "look up errors in logfire", "get metrics…

Open or return Logfire project pages, live views, trace links, and Explore pages in the Codex browser without querying telemetry first. Use this skill when the user asks to "open in Logfire", "show in the live view", "open Explore", "open the UI", "show in…

Evaluate Python AI/agent code against a dataset of test cases using pydantic_evals, and review results in Logfire's Datasets & Experiments UI. Also covers redirecting an existing Braintrust Eval() suite to Logfire with no code changes. Use this skill whenever…

Monitor hosts, Docker containers, Kubernetes clusters, database/queue/cache servers, and cloud-provider metrics with Pydantic Logfire — no application code required. Use this skill whenever the user asks to "monitor my host/server/VM", "monitor my Docker…

Add Pydantic Logfire observability to application code — traces, logs, metrics, and AI/agent spans. Use when the user asks to add or configure Logfire, observability, tracing, logging, or monitoring; maximize useful telemetry; or understand what an app is…

Run offline Python (`pydantic_evals`) or Node.js (`logfire/evals`) evaluations and review them in Logfire. Also redirect existing Braintrust `Eval()` suites. Use for eval setup, test datasets, AI scoring, agent checks, LLM judges, Braintrust migration, or…

Monitor hosts, Docker containers, Kubernetes clusters, database/queue/cache servers, and cloud-provider metrics with Pydantic Logfire — no application code required. Use this skill whenever the user asks to "monitor my host/server/VM", "monitor my Docker…

Entry point for Pydantic Logfire — an observability, monitoring, and evals platform. Use this skill when the user asks to "set up Logfire", "add Logfire to my project", "get me set up properly with Logfire", "send as much data as would be useful", mentions…

Create a PR in which all dependencies are updated to their latest versions and CI is passing. Only user initiated.

Extend Pydantic AI agents with batteries-included capabilities from pydantic-ai-harness -- Code Mode (collapse many tool calls into one sandboxed Python execution), a filesystem and shell, sub-agents, planning, context compaction, and more. Use when the user…

Migrate Python LangChain Deep Agents applications to Pydantic AI and Pydantic AI Harness. Use when the source uses the upstream `deepagents` package or demonstrably reproduces its middleware, backends, skills, memory, subagents, or sandbox contracts. Use…

Branch-local durable PR state -- issue brief, decisions log, and session handoffs. Read the brief, decisions, and latest handoff at session start; append decisions as you work; write a handoff only on user request or explicit session turnover.

What to do when you open a PR and every time you push -- label the PR, watch CI to green, triage every review comment to a reply and a reaction, and escalate genuine design trade-offs to maintainers. Use whenever you open a PR or push a commit to one.

Bootstrap branch-context on an existing PR. Writes issue-brief.md from the linked issue(s) + current PR state, and backfills pr-decisions.md with decision-bearing entries from already-resolved review threads. Use when picking up a PR mid-flight (yours or…

Add a new LLM model (or provider) to genai-prices pricing data, or change the price of one that is already there. Use when asked to add/update pricing for a model — e.g. "add grok 4.5", "add the new Claude", "update openai o5 prices", "provider X cut its…

Add a new inference provider to genai-prices, including provider data, matching, usage extractors, update automation, cassettes, agentic checks, generated artifacts, and Python/JavaScript/Go parity tests. Use for a new provider, not a new model on an existing…

Pydantic is a Python data validation and serialization library, based on type hints. Use this skill whenever you need to do relatively complex data modeling using Pydantic, e.g. when adding constraints, defining a model hierarchy with subclasses, etc.

Create a deck with deckx. Use when the user mentions "deckx", "deck" or "slides", asks to build a slide deck from MDX, asks to convert a brand palette into a deck stylesheet, or asks how to convert a deckx HTML deck into a PDF. Covers project layout,…