Führe einen Rat aus modellvielfältigen Unteragenten ein, um dasselbe Problem aus mehreren Perspektiven zu untersuchen, Ergebnisse zu vergleichen und eine endgültige Empfehlung zu erarbeiten. Verwende diese Fähigkeit, wenn der Benutzer nach einem Rat, Zweitmeinungen, mehreren Agenten/Modellen zur Bewertung einer Frage, parallelen Untersuchungen, Red-Team/Blue-Team-Vergleichen oder Hilfe bei der Entscheidung zwischen konkurrierenden technischen Ansätzen fragt.
Diese Fähigkeit ermöglicht es dir, einen Rat aus mehreren KI-Agenten (Unteragenten) mit unterschiedlichen Modellkonfigurationen einzuberufen. Jeder Unteragent untersucht ein gegebenes Problem aus seiner eigenen Perspektive, basierend auf einem ihm zugewiesenen Modell und einer spezifischen Rolle (z.B. Optimist, Pessimist, Sicherheitsfokus). Die Ergebnisse dieser parallelen Untersuchungen werden anschließend verglichen, so dass der Rat eine fundierte, finale Empfehlung abgeben kann. Dies ist besonders nützlich für komplexe Entscheidungen oder Bewertungen, bei denen eine einzelne Perspektive oder ein einzelnes Modell nicht ausreicht. Die Fähigkeit unterstützt nahtlos verschiedene Modelle, indem sie die Umgebungsvariable `COUNCIL_MODEL_PROVIDER` verwendet, und kann auf der Kommandozeile über den Befehl `/council` oder einfach durch die Anfrage an den Agenten, einen Rat einzuberufen, ausgelöst werden.
npx skills add https://github.com/warpdotdev/common-skills/tree/main/skills/councilInstalled? Explore more Produktivität & Zusammenarbeit skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
Führe einen Rat aus modellvielfältigen Unteragenten ein, um dasselbe Problem aus mehreren Perspektiven zu untersuchen, Ergebnisse zu vergleichen und eine endgültige Empfehlung zu erarbeiten. Verwende diese Fähigkeit, wenn der Benutzer nach einem Rat, Zweitmeinungen, mehreren Agenten/Modellen zur Bewertung einer Frage, parallelen Untersuchungen, Red-Team/Blue-Team-Vergleichen oder Hilfe bei der Entscheidung zwischen konkurrierenden technischen Ansätzen fragt.
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.
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.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.