678 skills · sorted by relevance
Reviews Cherry Studio code and documentation against product decisions, architecture rules, implementation checks, and conventions. It selects a review approach based on scope and available agents, then reports findings with guidance for fixes.
Set up Antigravity 2.0 Agent Manager alongside Antigravity IDE to coordinate parallel development agents. Assign roles and folder boundaries, review their changes in the editor, and use Git checkpoints to manage shared work.
Inventory local skills within a requested scope, rank candidates against a task, and prepare a coordination plan showing their order and data flow. Project assignments help prioritize relevant skills without automatically launching agents or executing skills.
Plan and build AI agents through selected stages of design, implementation, tool integration, memory, and evaluation. The workflow points to specialized skills for single agents, coordinated agent teams, and stateful workflows using CrewAI or LangGraph.
Design AI agents that use tools, plan tasks, retain relevant context, and recover from failures. Choose execution patterns, permission boundaries, memory handling, and runtime limits, and assess whether coordinating multiple agents is justified for the task.
Test how agent workflows recover from repeatable simulated failures, including tool timeouts, worker restarts, and lost memory. Produces a fault matrix, event timeline, and verdict showing whether state, safety boundaries, and retry budgets were preserved.
Organize complex work for a team of AI agents by splitting tasks, defining dependencies, and documenting ownership and completion criteria. Provides strategies for tracking progress, spotting blockers, and redistributing work when workloads become uneven.
Organize feature development across multiple agents by dividing work, assigning one owner per file, and agreeing on interfaces before implementation. Provides strategies for shared files, branches, and integration so agents can build separate parts of the same system.
Set up OpenMAIC and generate interactive classrooms with multiple AI agents. The skill guides hosted or local use, model configuration, and classroom generation, and also supports customizing the product or building with its SDK, a set of development tools.
Coordinate a small AI development team with planning, implementation, and optional independent testing roles. Adapt the process to the work’s complexity, prepare pull requests, and preserve decisions and next steps so later sessions can continue.
Add controls to AI agents that use APIs, databases, and files. Use Python patterns for tool permissions, approval requirements, call limits, suspicious input checks, audit logs, and trust scores that guide delegation between agents.
Research stock and cryptocurrency strategies through historical testing, factor analysis, and options pricing. Analyze trading journals, extract rules from profitable trades, and generate reports comparing simulated results with actual trading outcomes.
Find relevant QwenPaw documentation and source files for questions about setup, configuration, skills, memory, and other features. It maps question keywords to likely file paths so the agent can read and verify the right material.
Find the QwenPaw documentation and source files most relevant to a question. This skill maps topics such as installation, configuration, skills, and memory to likely reading paths, helping an agent focus its investigation.
Coordinate specialized agents in Claude Code to divide development and review tasks, pass findings between stages, and combine results into one report with prioritized recommendations and action items.
Coordinates specialist agents in Claude Code to work from product requirements through design, implementation, testing, deployment, and operations. Includes task queues, saved progress, parallel reviews, and recovery workflows for managing extended development work.
Design AI agents that plan tasks, call tools, and retain useful information. Covers coordination between agents, evaluation, and debugging, with practical patterns for execution limits, error handling, and tracing what an agent does.
Design systems where multiple AI agents share work, generate tool schemas that define tool inputs for Anthropic and OpenAI, and analyze execution logs. Outputs include role assignments, communication diagrams, and recommendations for addressing cost, latency, and failures.
Examine strategic decisions through a structured meeting of AI agents representing executive roles. Gather independent recommendations, challenge assumptions, summarize disagreements, and record approved decisions with action owners and deadlines after the founder reviews the proposal.
Design Google Cloud systems where multiple AI agents process live audio, video, or text and respond in real time. Guides requirements discovery, architecture diagrams, infrastructure code, deployment instructions, and validation for technical guidance or safety monitoring workloads.
Create and audit the files and routines that guide coding agents across sessions. It organizes instructions, progress tracking, verification, scope boundaries, and handoffs, with scripts for setup, structural checks, and shareable assessment reports.
Apply over 240 reusable prompt templates to summarize content, extract insights, improve writing, review code, or analyze security threats. It reads each template directly and returns results in the format defined for that task.
Compare options through a debate among agents given distinct expertise and positions for the topic. Choose a three-round discussion with a visible transcript and synthesis, or a one-round check that presents initial perspectives.
Research topics across multiple sources, cross-check findings, and verify source URLs before producing a report with confidence labels. Choose from quick lookups to iterative investigations that preserve findings, with community sources prioritized for questions about public reactions.