182 skills · sorted by relevance
Create a new sub-agent definition file (agents/*.md) for the Claude Code plugin. Use this skill when the user wants to add a new specialized agent, define a new agent type, or create an agent that handles a specific domain of tasks. Trigger whenever the user mentions creating, adding, or defining a new agent or sub-agent.
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Review code changes from a chosen Git reference against two separate criteria: documented coding standards and the original requirements. Independent reviewers check each criterion, then present findings separately so convention problems and requirement gaps remain visible.
Set up and operate Ruflo to coordinate multiple AI coding agents. This skill guides project initialization, shared memory, task assignment, health checks, and plugin discovery for work that needs coordinated or parallel execution.
Turn a music track into a HyperFrames video with text, images, and clips timed to its rhythm and energy. Additional visual assets are optional, and the workflow includes storyboard approval, visual checks, and final MP4 rendering.
Prepare Apache Airflow provider releases by classifying pending changes, inspecting pull request diffs for compatibility risks, and applying version bumps and changelog entries. Use existing breeze tooling to regenerate release documentation and consult the release manager on uncertain decisions.
Work through one difficult mathematics problem with a single agent, saving established arguments and dead ends as progress notes. Resumes from recorded work after interruptions and writes a self-contained proof file, including any unresolved parts.
Work through GitHub pull request feedback by classifying findings, fixing code, running repository checks, and posting replies. With suitable permissions, the skill repeats Copilot reviews until the latest code is reviewed and every thread has an agent response.
After explicit opt-in, captures original ideas in the user's exact words and identifies people and companies mentioned in conversations. Stores them in linked knowledge pages with sources, dated facts, and a brief capture log.
Delegate coding, analysis, refactoring, or reviews from Claude Code to Codex CLI. The skill explains unattended execution, permission settings, output capture, parallel tasks, and independent checks of changed files and test results.
Delegate coding, analysis, planning, and reviews from Codex to Claude Code through its command-line interface. Capture responses, resume sessions, or run independent tasks in parallel, then verify changed files and test results independently.
Give subagents or short sessions a compact set of working rules for checking evidence, changing unsuccessful approaches, and verifying results. It also defines agent roles and uses stern corporate-style commentary to push execution forward.
Design tools that agents can select and call correctly. Clarify descriptions, input rules, response formats, and recovery messages, then review overlapping tools and naming conventions using examples of successful and failed calls.
Write or refine Chinese spoken video scripts about AI technology, interview questions, and current topics. New scripts follow research and outline approval, while revisions provide specific wording changes and reasons for the edits.
Independently checks code changes against the original issue, runs tests, and verifies that edits stay within scope. Produces an approval, rejection, or request for human judgment, supported by test results and specific findings.
Review code changes with an independent agent from a different model provider. After tests pass, check a fixed diff against acceptance criteria, send blocking issues back to the implementer, and repeat until review passes.
Test Omnigent’s GitHub Copilot SDK integration on a live local server. Build test agents, run real conversations, and inspect authentication, model selection, tool connections, policy enforcement, and leftover processes to investigate integration failures.
Test polly's coordination of coding agents using a scripted mock language model and live command-line tools. Check startup, task dispatch, safety rules, parallel delegation, and whether real agents make appropriate orchestration decisions.
Test Omnigent’s Cursor SDK integration against a live local server. Build test agents, run real conversations, and check authentication, model selection, and tool connections while investigating empty responses or bridge processes left after shutdown.
Test Omnigent’s Antigravity SDK integration against a live local server. Build test agents and run real conversations to check Gemini authentication, model selection, tool connections, and failures across the full execution path.
Run independent development tasks in parallel, each with a separate git worktree and implementation agent. Each agent opens its own pull request, which receives cross-review before being handed to a human for review and merging.
Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation. Use when spawning sub-agents to complete scoped work.
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Deeply analyze any empirical economics PDF using the five-question framework (五问框架): research question, identification strategy, core estimand, robustness logic, and scholarly contribution. Converts the PDF to Markdown, dispatches 5 parallel sub-agents (one per question), then compiles a 2000–3000 Chinese-character report with a transferable methodology checklist. Optimized for empirical economics papers in top journals (AER, QJE, JPE, RFS, RoF, JFE, etc.). Use this skill whenever the user wants to: - systematically analyze an empirical economics paper - apply the 五问框架 (five-question framework) to a paper - extract transferable research design elements from a top-journal paper - understand a paper's identification strategy, estimand, or robustness logic - run /five-questions, /five-q, or /paper-five-q-analysis - "帮我做五问分析", "用五问框架分析这篇论文", "analyze this paper with the five-question framework"
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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".
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Plan and execute large refactor or rewrite efforts efficiently with parallel multi-agent analysis and implementation. Use when a user asks to refactor many files, split workstreams, analyze a target code area, and coordinate sub-agents with clear ownership and dependency-aware execution.
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