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Analyze changes and create a meaningful commit with agent authorship. Internal skill for evolve_loop.

Agent assignment matrix, blocker escalation, and TDM coordination patterns. Use when assigning work to specialists, managing blockers, or coordinating multi-agent workflows.

Creates specialized AI agents with optimized system prompts using the official 4-phase SOP methodology from Desktop .claude-flow, combined with evidence-based prompting techniques and Claude Agent SDK implementation. Use this skill when creating production-ready agents for specific domains, workflows, or tasks requiring consistent high-quality performance with deeply embedded domain knowledge.

Agent Curator Skill

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AI agent development standards including frontmatter structure, naming conventions, tool access patterns, model selection, and Bash-only file operations for .opencode/ folders

Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.

Claude Code agent generation system that creates custom agents and sub-agents with enhanced YAML frontmatter, tool access patterns, and MCP integration support following proven production patterns

Stop AI agents from secretly bypassing your rules. Mechanical enforcement with git hooks, secret detection, deployment verification, and import registries. Born from real production incidents: server crashes, token leaks, code rewrites. Works with Claude Code, Clawdbot, Cursor. Install once, enforce forever.

Generate comprehensive handoff documentation optimized for AI agent takeover by analyzing project structure, design docs, and codebase

Self-improvement loop for multi-agent workflows. Diagnose failures, improve tool descriptions, and learn from success/failure patterns.

Check and process messages from autonomous AILANG agents. Use when starting a session, after agent handoffs, or when checking for completion notifications.

Initialize or improve AGENTS.md files that define how coding agents operate in a repo. Use when asked to set up or replace an agent init command (Codex, Claude), standardize multi-agent behavior, or audit an existing AGENTS.md for clarity, commands, boundaries, and repo-specific context. For Claude Code, also create CLAUDE.md as a symlink to AGENTS.md.

Agent Inventor Skill

Quick reference for invoking CasareRPA agents via Task tool. AUTO-CHAIN ENABLED by default. Use when: invoking agents, running agent chains, task routing, choosing the right agent, understanding agent auto-chaining, Task tool usage.

Launches specialized Claude agents for targeted tasks. Analyzes requirements, selects appropriate agent, and executes with optimized configuration.

Manage agent fleet through CRUD operations and lifecycle patterns. Use when creating, commanding, monitoring, or deleting agents in multi-agent systems, or implementing proper resource cleanup.

Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.

Enable communication between AI coding agents using AI Maestro's dual-channel messaging system. Agents are identified by their agent ID or alias from the agent registry. Supports both SENDING and RECE

agent-observability

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

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

Create/update .agent/constitution.md. Use when commands/boundaries/constraints must be confirmed before baseline or code changes. Draft v0 from repo evidence, then interview user.

Create new AgentOps skills via interactive interview. Supports from-scratch and clone modes with tiered complexity.

Create focused, specific technical documentation for codebase sections. Analyzes code, identifies topics, presents options before writing. Supports code blocks with line numbers.

Systematic debugging approaches for isolating and fixing software defects. Use when something isn't working and the cause is unclear.

Dependency management, updates, and security advisory handling. Use when adding, updating, or auditing project dependencies.

Documentation management for README, CHANGELOG, API docs, and user-facing documentation. Use when creating or updating project documentation.

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

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

Comprehensive project hygiene: archive issues, validate schema, clean clutter, align docs, check git, update ignores.

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

Implement only after a validated/approved plan. Use for coding: small diffs, frequent tests, no refactors, stop on ambiguity.

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

Conduct structured interviews with the user. Use when multiple decisions need user input: ask ONE question at a time, wait for response, record answer, then proceed to next question.

Migrate a project into another, ensuring functionality and validating complete content transfer. Use for monorepo consolidation, template upgrades, or codebase mergers.

MkDocs documentation site management: initializing, updating, building, and deploying

Produce a thorough plan before implementation. Use for planning tasks: clarify unknowns, create plan iterations based on confidence level, validate each, then finalize.

Analyze incoming content (text, files, folders, URLs) to extract purpose, create summaries, and identify potential value for the current project.

Identify and map different sections of a software project (API, frontend, database, CLI, domain). Use for context scoping and architecture documentation.

Ruthlessly audit project features for justification. Challenge every feature to prove its value with evidence or face removal. Uses MCP tools for research.

Handle failures and errors during workflow. Use when build breaks, tests fail unexpectedly, or agent gets stuck. Semi-automatic recovery with user confirmation for destructive actions.

Create clean git branches from feature work, excluding agent-ops files. Use for PR preparation.

Create, refine, and manage issues. Use for creating new issues from loose ideas, refining ambiguous issues, bulk operations, or JSON export.

Detect available development tools at session start. Saves to .agent/tools.json and warns about missing required tools. Works with or without aoc CLI installed.

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

Manage semantic versioning, changelog generation, and release notes. Auto-generates entries from completed issues or git diff.

agent-orchestration