Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Implement a GitHub issue end-to-end — create a worktree branch, implement the feature with tests, create a draft PR, then iteratively resolve all CI failures and review comments until the PR is clean. Use when you need to fully implement a GitHub issue from start to merge-ready. Triggers on "implement issue", "resolve issue", "/issue-to-pr-resolver <number>".
Conventions for importing code in Devtools to avoid build errors. Covers cross-module imports, internal imports, and the "import * as" requirement.
Guidelines for building UI widgets using the MVP architecture in DevTools. Covers Widget lifecycle, lit-html views, and state management.
Backport a merged kumahq/kuma PR to release branches end-to-end — trigger the backport workflow if needed, find the generated backport PRs, resolve cherry-pick conflicts (module-path rewrites, older APIs), fix DCO, build+test per branch, force-push, clean up PR descriptions/labels, mark ready, and watch CI. Use whenever the user gives a kuma PR link and mentions backporting, backport PRs, cherry-pick conflicts on release-X.Y branches, or asks to fix CI on chore/backport-* PRs.
Workflow for merging a DevTools submodule into its parent module. Covers BUILD.gn consolidation and updating devtools_grd_files.gni.
Consolidates manual DOM creation, updates, and constructors into private helper methods and structured state interfaces.
High-level orchestrator for managing multi-pass migration of Chrome DevTools legacy components to the modern UI engineering vision (UI.Widget & Lit-html).
Promotes legacy views to modern UI.Widget classes, hooks up performUpdate() rendering, and exports default views.
Use this skill when the user wants Python data models, Pydantic models, dataclasses, TypedDicts, msgspec structs, or type-safe Python classes generated from OpenAPI, AsyncAPI, JSON Schema, GraphQL, JSON/YAML/CSV sample data, MCP tool schemas, Protocol Buffers, XML Schema, Apache Avro, or existing Python model objects. Use it for requests such as "Pydantic models from this schema", "types for this API", "parse this JSON into classes", or "convert this spec into Python models". Prefer the datamodel-codegen CLI over hand-written model code whenever an input schema, API spec, sample data file, or Python model object exists.