agent-dotnet-core-expert
Expert .NET Core specialist mastering .NET 8 with modern C# features. Specializes in cross-platform development, minimal APIs, cloud-native applications, and microservices with focus on building high-performance, scalable solutions.
agent-dx-optimizer
Expert developer experience optimizer specializing in build performance, tooling efficiency, and workflow automation. Masters development environment optimization with focus on reducing friction, accelerating feedback loops, and maximizing developer productivity and satisfaction.
agent-electron-pro
Desktop application specialist building secure cross-platform solutions. Develops Electron apps with native OS integration, focusing on security, performance, and seamless user experience.
agent-embedded-systems
Expert embedded systems engineer specializing in microcontroller programming, RTOS development, and hardware optimization. Masters low-level programming, real-time constraints, and resource-limited environments with focus on reliability, efficiency, and hardware-software integration.
agent-error-coordinator
Expert error coordinator specializing in distributed error handling, failure recovery, and system resilience. Masters error correlation, cascade prevention, and automated recovery strategies across multi-agent systems with focus on minimizing impact and learning from failures.
agent-error-detective
Expert error detective specializing in complex error pattern analysis, correlation, and root cause discovery. Masters distributed system debugging, error tracking, and anomaly detection with focus on finding hidden connections and preventing error cascades.
agent-evals
Design and implement evaluation frameworks for AI agents. Use when testing agent reasoning quality, building graders, doing error analysis, or establishing regression protection. Framework-agnostic concepts that apply to any SDK.
agent-evaluation
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
agent-evaluator
Deterministic custom subagent selection helper. Use when you need a reproducible, auditable decision on which custom subagents to activate for a user query (runs scripts/agent_evaluator.py).
agent-exec
Use the agent-exec CLI to run Codex/Claude/Cursor and manage skills for this repo.
agent-expert-creation
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.
agent-exporter
Upload tailored resume to cloud or attach to user session. Handles final formatting and delivery.
agent-extend
Extend the Strategy Coach agent with new capabilities. Use when user says "add coaching phase", "new research pillar", "extend agent", "add strategic output", or asks to enhance the AI coaching methodology.
agent-ide
TS/JS 程式碼智能重構工具。重命名、移動檔案、清理 dead code、修改函式簽章時自動更新所有引用。
agent-ops-code-review-interactive
Interactive code review for agent iterations. Captures comments, tracks resolution status, and integrates with git diffs.
agent-ops-constitution
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.
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.
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