Leitet die Erstellung, Überarbeitung und Prüfung von Warp- oder Oz-markierten Assets. Verwenden Sie es bei der Arbeit an Launch-Seiten, Dokumentationen, HTML/CSS-Komponenten, UI-Mockups, Prompts, Social-Media-Assets, Texten, Präsentationen oder anderen markierten Liefergegenständen, die eindeutig wie Warp oder Oz aussehen und klingen sollen.
Brandalf ist ein KI-Agent für die Markenkonformität speziell für Warp- und Oz-Produktlinien. Es hilft Entwicklern und Designern, sicherzustellen, dass alle Inhalte und Assets – von Codekomponenten über Texte bis hin zu sozialen Medien – die visuelle und sprachliche Identität der Marke Warp oder Oz widerspiegeln. Der Agent unterstützt die Bearbeitung von Launch-Seiten, Dokumentationen, HTML/CSS-Komponenten, UI-Mockups, Prompts und Präsentationen, indem er auf konsistente Schriftarten, Farben, Tonalität und Terminologie achtet. Brandalf arbeitet eng mit Warp-Benutzern zusammen, um Abweichungen von der Markenrichtlinie zu identifizieren und Verbesserungen vorzuschlagen, und ist damit ein unverzichtbares Werkzeug für die interne Qualitätssicherung und Markenpflege.
npx skills add https://github.com/warpdotdev/common-skills/tree/main/skills/brandalfLeitet die Erstellung, Überarbeitung und Prüfung von Warp- oder Oz-markierten Assets. Verwenden Sie es bei der Arbeit an Launch-Seiten, Dokumentationen, HTML/CSS-Komponenten, UI-Mockups, Prompts, Social-Media-Assets, Texten, Präsentationen oder anderen markierten Liefergegenständen, die eindeutig wie Warp oder Oz aussehen und klingen sollen.
This repo contains 16 individual skills — each has its own dedicated page.
Compare a pull request's implementation against spec context in spec_context.md and feed any material mismatches into review.json. Use during PR review when approved or repository spec context is available.
Run a model-diverse subagent council to investigate the same problem from multiple perspectives, compare findings, and produce a final recommendation. Use this skill whenever the user asks for a council, second opinions, multiple agents/models to evaluate one question, parallel investigation, red-team/blue-team comparison, or help deciding between competing technical approaches.
Create a pull request in the warp repository for the current branch. Use when the user mentions opening a PR, creating a pull request, submitting changes for review, or preparing code for merge.
Diagnose CI failures for a PR using the GitHub CLI, extract error logs, and generate a plan to fix them. Use when the user asks to check CI status, pull CI issues, triage test failures, or investigate PR build failures.
Fix compilation errors, linting issues, and test failures in the warp Rust codebase. Covers presubmit checks, WASM-specific errors, and running specific tests. Use when the user hits build errors, clippy or fmt failures, test failures, or needs to run or interpret presubmit before a PR.
Implement an approved feature from PRODUCT.md and TECH.md, keeping specs and code aligned in the same PR as implementation evolves. Use after the product and tech specs are approved and the next step is building the feature.
Generate a static interactive D3 walkthrough of a pull request. Use when the user wants a zoomable PR map, graph/canvas PR orientation, or alternate visualization of PR system components, data flow, code dependencies, and user actions.
Launch Oz cloud agents with computer use to reproduce UI-focused bug reports, capture visual evidence, and report reproduction findings. Use when investigating a specific interactive or visual bug from an issue, ticket, support report, or prompt.
Resolve Git merge conflicts by extracting only unresolved paths, conflict hunks, and compact diffs instead of loading whole files into context. Use when a merge, rebase, cherry-pick, or stash pop stops on conflicts, when `git status` shows unmerged paths, or when files contain conflict markers.
Walk users through PR review comments, fetching and displaying them first when needed, collect per-comment response decisions, apply requested fixes, and preview GitHub replies and resolutions before posting. Use when responding to PR review comments on the current branch.
Review a pull request diff and write structured feedback to review.json for the workflow to publish. Use when reviewing a checked-out PR from local artifacts like pr_diff.txt and pr_description.txt and producing machine-readable review output instead of posting directly to GitHub.
Drive a spec-first workflow for substantial features by writing PRODUCT.md before implementation, writing TECH.md when warranted, and keeping both specs updated as implementation evolves. Use when starting a significant feature, planning agent-driven implementation, or when the user wants product and tech specs checked into source control.
Create or update skills by generating, editing, or refining SKILL.md files in this repository. Use when authoring new skills or revising the structure, frontmatter, or guidance for existing ones.
Validate that a branch or pull request implementation matches introduced product, technical, security, and related specs. Use when reviewing or finishing a spec-driven change and resolving mismatches between checked-in specs and implementation.
Write a PRODUCT.md spec for a significant user-facing feature in Warp, focused on detailed behavior and validation. Use when the user asks for a product spec, desired behavior doc, or PRD, wants to define feature behavior before implementation, or when the feature is substantial or behaviorally ambiguous enough that a written spec would improve implementation or review.
Write a TECH.md spec for a significant Warp feature after researching the current codebase and implementation constraints. Use when the user asks for a technical spec, implementation plan, or architecture doc tied to a product spec.
ElevenLabs text-to-speech with mac-style say UX.
Oracle CLI second-model review/debug/refactor/design with selected files, dry-run token checks, API or browser engine.
Capture and automate macOS UI with the Peekaboo CLI.
Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
Build a throwaway prototype to flesh out a design — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route.
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".