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.
npx skills add https://github.com/warpdotdev/common-skills/tree/main/skills/reproduce-bug-reportLaunch 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.
This repo contains 16 individual skills — each has its own dedicated page.
Guides creation, revision, and review of Warp- or Oz-branded assets. Use when working on launch pages, docs, HTML/CSS components, UI mockups, prompts, social assets, copy, presentations, or any other branded deliverable that should look and sound unmistakably Warp or Oz.
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.
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.
Balance visual design with ATS compatibility for creative roles
Generates article cover images with 5 dimensions (type, palette, rendering, text, mood) combining 11 color palettes and 7 rendering styles. Supports cinematic (2.35:1), widescreen (16:9), and square (1:1) aspects. Use when user asks to "generate cover image", "create article cover", or "make cover".
Audit and fix visual hierarchy, spacing, color, and depth in web UIs. Use when the user mentions "my UI looks off", "fix the design", "Tailwind styling", "color palette", "visual hierarchy", "design system", "spacing scale", or "component styling". Also trigger when building consistent design tokens, creating dark mode themes, improving data visualization clarity, or polishing UI details before launch. Covers grayscale-first workflow, constrained design scales, shadows, and component styling. For typeface selection, see web-typography. For usability audits, see ux-heuristics.
将数据可视化为图表。当用户需要生成柱状图、折线图、饼图、散点图、雷达图、桑基图、思维导图、流程图等图表时调用此技能，通过 curl 工具调用 AntV API 生成图表图片
Generate and edit images with OpenAI GPT-Image-2 via inference.sh CLI. Models: GPT-Image-2. Capabilities: text-to-image, image editing, inpainting, mask-based editing, multi-image reference, batch generation. Use for: product mockups, marketing visuals, image editing, concept art, inpainting, photo manipulation. Triggers: gpt image, gpt-image-2, openai image, chatgpt image, dall-e, dalle, openai image generation, gpt image edit, gpt inpainting, openai dall-e, gpt 4o image
Turn any codebase into an interactive knowledge graph using Claude Code skills — explore, search, and ask questions about any project visually.