agent-ops-context-map
Analyze the codebase to create a concise, LLM-optimized structured overview in .agent/map.md.
agent-ops-create-python-project
Create a plan and issues for implementation of a production-ready Python project with proper structure, tooling, and best practices.
agent-ops-create-skill
Create new AgentOps skills via interactive interview. Supports from-scratch and clone modes with tiered complexity.
agent-ops-create-technical-docs
Create focused, specific technical documentation for codebase sections. Analyzes code, identifies topics, presents options before writing. Supports code blocks with line numbers.
agent-ops-critical-review
Deep, excruciating code review. Use anytime to analyze code for correctness, edge cases, security, performance, and design issues. Not tied to baseline—this is pure code analysis.
agent-ops-debugging
Systematic debugging approaches for isolating and fixing software defects. Use when something isn't working and the cause is unclear.
agent-ops-dependencies
Dependency management, updates, and security advisory handling. Use when adding, updating, or auditing project dependencies.
agent-ops-docs
Documentation management for README, CHANGELOG, API docs, and user-facing documentation. Use when creating or updating project documentation.
agent-ops-dogfood
Dogfooding discovery agent — establish human-approved project baseline from public docs without code inspection
agent-ops-focus-scan
Analyze issues to identify the next work item and update focus.md. Enforces issue-first workflow and confidence-based batch limits.
agent-ops-git-analysis
Analyze git repository for insights: contributor stats, commit patterns, branch health, and change analysis. Outputs actionable reports.
agent-ops-git-story
Generate narrative summaries from git history for onboarding, retrospectives, changelogs, and exploration. LLM-enhanced when available, works without LLM too.
agent-ops-git-worktree
Manage git worktrees for isolated development. Create, list, remove, and work in worktrees.
agent-ops-git
Manage git operations safely. Includes stale state detection, branch/commit management. Never pushes without explicit user confirmation.
agent-ops-github
Bidirectional sync between agent-ops issues and GitHub Issues
agent-ops-guide
Interactive workflow guide. Use when user is unsure what to do next, needs help navigating AgentOps, or wants to understand available tools.
agent-ops-housekeeping
Comprehensive project hygiene: archive issues, validate schema, clean clutter, align docs, check git, update ignores.
agent-ops-idea
Capture loosely structured ideas, enrich with research, and create backlog issues. Use when user has a raw concept that needs fleshing out.
agent-ops-impl-details
Extract, plan, or propose implementation details at configurable depth levels (low/normal/extensive). Outputs to reference files for team discussion and handoff.
agent-ops-implementation
Implement only after a validated/approved plan. Use for coding: small diffs, frequent tests, no refactors, stop on ambiguity.
agent-ops-install
Install AgentOps into a new or existing project. Handles .agent/ setup and .github/ merging.
agent-ops-interview
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.
agent-ops-migrate
Migrate a project into another, ensuring functionality and validating complete content transfer. Use for monorepo consolidation, template upgrades, or codebase mergers.
agent-ops-mkdocs
MkDocs documentation site management: initializing, updating, building, and deploying
agent-ops-optimize-instructions
Optimize agent instruction files by extracting sections into separate files and referencing them. Reduces context size while preserving information.
agent-ops-plan-preview
Transform implementation plans into concise stakeholder-friendly summaries with file change overviews, component listings, and optional flow diagrams.
agent-ops-planning
Produce a thorough plan before implementation. Use for planning tasks: clarify unknowns, create plan iterations based on confidence level, validate each, then finalize.
agent-ops-potential-discovery
Analyze incoming content (text, files, folders, URLs) to extract purpose, create summaries, and identify potential value for the current project.
agent-ops-project-sections
Identify and map different sections of a software project (API, frontend, database, CLI, domain). Use for context scoping and architecture documentation.
prove-your-worth
Ruthlessly audit project features for justification. Challenge every feature to prove its value with evidence or face removal. Uses MCP tools for research.
agent-ops-recovery
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.
agent-ops-report
Generate markdown reports from issues. Filter by type, priority, epic, date range. Supports summary, detailed, progress, completion, velocity, and backlog analysis views.
agent-ops-research
Deep topic research with optional issue creation from findings. Use for researching technologies, patterns, libraries, or any topic requiring investigation.
agent-ops-retrospective
Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.
agent-ops-selective-copy
Create clean git branches from feature work, excluding agent-ops files. Use for PR preparation.
agent-ops-tasks
Create, refine, and manage issues. Use for creating new issues from loose ideas, refining ambiguous issues, bulk operations, or JSON export.
agent-ops-tools
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.
agent-ops-validation
Pre-commit and pre-merge validation checks. Use before committing changes or declaring work complete to ensure all quality gates pass.
agent-ops-versioning
Manage semantic versioning, changelog generation, and release notes. Auto-generates entries from completed issues or git diff.
agent-orchestration
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agent-orchestration-improve-agent
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
agent-orchestration-multi-agent-optimize
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
agent-orchestration-patterns
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.
agent-orchestration-planner
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".
agent-orchestrator-manager
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
Agent Orchestrator
Coordinate multiple AI agents and skills for complex workflows
agent-organizer
Expert in designing, orchestrating, and managing multi-agent systems (MAS). Specializes in agent collaboration patterns, hierarchical structures, and swarm intelligence. Use when building agent teams, designing agent communication, or orchestrating autonomous workflows.
agent-os-framework
Generate standardized .agent-os directory structure with product documentation, mission, tech-stack, roadmap, and decision records. Enables AI-native workflows.
agent-parser
End-to-end resume parsing (detect format → extract fields). Uses a combination of format detection, text extraction, and LLM parsing to normalize resume data.
agent-patterns
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