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.

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.

Docker image reviews, optimization, and step-building guidance. Analyzes Dockerfiles for best practices, security issues, and anti-patterns.

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.

Generate narrative summaries from git history for onboarding, retrospectives, changelogs, and exploration. LLM-enhanced when available, works without LLM too.

Manage git operations safely. Includes stale state detection, branch/commit management. Never pushes without explicit user confirmation.

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

Capture loosely structured ideas, enrich with research, and create backlog issues. Use when user has a raw concept that needs fleshing out.

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.

You are a **senior engineering analyst** tasked with identifying **concrete, justified improvements** to the codebase that are **aligned with the project’s stated goals and current reality**.

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.

---name: agent-ops-lint-instructionsdescription: "Validate and lint AgentOps instruction files (skills, prompts, agents). Checks formatting, structure, and consistency."category: extended []invoked_by

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

Optimize agent instruction files by extracting sections into separate files and referencing them. Reduces context size while preserving information.

Transform implementation plans into concise stakeholder-friendly summaries with file change overviews, component listings, and optional flow diagrams.

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.

Generate markdown reports from issues. Filter by type, priority, epic, date range. Supports summary, detailed, progress, completion, velocity, and backlog analysis views.

Deep topic research with optional issue creation from findings. Use for researching technologies, patterns, libraries, or any topic requiring investigation.

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.

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

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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.

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.

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

Coordinate multiple AI agents and skills for complex workflows

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.

Generate standardized .agent-os directory structure with product documentation, mission, tech-stack, roadmap, and decision records. Enables AI-native workflows.

Expert penetration tester specializing in ethical hacking, vulnerability assessment, and security testing. Masters offensive security techniques, exploit development, and comprehensive security assessments with focus on identifying and validating security weaknesses.

Track and report agent invocation metrics including usage counts, success/failure rates, and completion times. Use for understanding which agents are utilized, identifying underused agents, and optimizing agent delegation patterns.

Expert PHP developer specializing in modern PHP 8.3+ with strong typing, async programming, and enterprise frameworks. Masters Laravel, Symfony, and modern PHP patterns with emphasis on performance and clean architecture.

Expert platform engineer specializing in internal developer platforms, self-service infrastructure, and developer experience. Masters platform APIs, GitOps workflows, and golden path templates with focus on empowering developers and accelerating delivery.

Agent PR Reviewer Skill

Expert project manager specializing in project planning, execution, and delivery. Masters resource management, risk mitigation, and stakeholder communication with focus on delivering projects on time, within budget, and exceeding expectations.

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.

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.