Diego Rodrigues de Sa e Souza

Standardized branch creation with type detection, issue ID extraction, and worktree setup. Creates working branches (-WB) and integrates with selective-copy for clean PRs.

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Create .agent/baseline.md and later compare against it. Use when capturing baseline build/lint/test results or investigating newly introduced findings.

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Systematically deconstruct written content into verifiable claims, validate each using search/documentation, and facilitate informed discussion through structured interviewing.

日本語の概要は準備中です。原文の説明を表示しています。

Multi-agent task orchestration for Claude Code. When given multiple tasks to execute, use this skill to spawn parallel worker agents through Sibyl's MetaOrchestrator instead of Claude Code's built-in Task tool. This enables true multi-process execution with budget controls, quality gates, and coordination.

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Analyze issues to identify the next work item and update focus.md. Enforces issue-first workflow and confidence-based batch limits.

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Coordinate multiple AI agents and skills for complex workflows

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Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order, fallback strategies, and cost limits. Use for "AI agents", "agentic workflows", "multi-step AI", or "autonomous systems".

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Analyze the codebase to create a concise, LLM-optimized structured overview in .agent/map.md.

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Dogfooding discovery agent — establish human-approved project baseline from public docs without code inspection

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Extract, plan, or propose implementation details at configurable depth levels (low/normal/extensive). Outputs to reference files for team discussion and handoff.

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Automatically applies when designing multi-agent systems. Ensures proper tool schema design with Pydantic, agent state management, error handling for tool execution, and orchestration patterns.

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This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.

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Orchestrates multi-agent workflows by delegating ALL tasks to spawned subagents via /spawn command. Parallelizes independent work, supervises execution, tracks progress in UUID-based output directories, and generates summary reports. Never executes tasks directly. Triggers on keywords: orchestrate, manage agents, spawn agents, parallel tasks, coordinate agents, multi-agent, orc, delegate tasks

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Expert agent organizer specializing in multi-agent orchestration, team assembly, and workflow optimization. Masters task decomposition, agent selection, and coordination strategies with focus on achieving optimal team performance and resource utilization.

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End-to-end resume parsing (detect format → extract fields). Uses a combination of format detection, text extraction, and LLM parsing to normalize resume data.

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Expert payment integration specialist mastering payment gateway integration, PCI compliance, and financial transaction processing. Specializes in secure payment flows, multi-currency support, and fraud prevention with focus on reliability, compliance, and seamless user experience.

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Comprehensive guide for the agent permissions and approval system. Use when an agent needs to request user approval for sensitive operations, when implementing new approval policies, when integrating platform-specific approval UIs (Slack buttons, WhatsApp replies, Dashboard modals), or when troubleshooting approval flows. Covers policy definition, platform adapters, and the complete approval lifecycle.

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Pre-commit and pre-merge validation checks. Use before committing changes or declaring work complete to ensure all quality gates pass.

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Guide for multi-platform skill compatibility across Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot, and other AI coding agents.

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Create well-structured prompts for AI agents using proven architecture patterns. Use when users ask to write agent prompts, system prompts, or agent instructions, or want to improve existing prompts that aren't working.

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Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.

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Write effective system prompts for TD AI agents. Covers role definition, constraint specification, output formatting, tool usage instructions, and prompt structure patterns.

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Install AgentOps into a new or existing project. Handles .agent/ setup and .github/ merging.

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Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

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