Browse the metahub catalog

Claude Code integration for MCP Task Orchestrator — schema-aware context, note-driven workflow

Specification quality framework for planning. Defines the minimum bar for what a plan must address — alternatives, non-goals, blast radius, risk flags, and test strategy. Referenced by schema guidance fields during queue-phase note filling. Use when filling requirements or design notes for any MCP work item.

Analyzes the current implementation run — evaluates schema effectiveness, delegation alignment, note quality, and plan-to-execution fit. Captures cross-session trends and proposes improvements when patterns repeat. Use after implementation runs, or when user says 'retrospective', 'session review', 'what did we learn', 'analyze this run', 'how did that go', 'evaluate our process', 'wrap up', 'end of session review'. Also use when the output style's retrospective nudge fires after complete_tree.

Review quality framework for the work-to-review transition gate. Guides verification of plan alignment, test quality, and code simplification before marking implementation complete. Referenced by schema guidance fields during review-phase note filling. Use when filling review-checklist notes or when asked to review completed implementation work.

End-to-end release automation — reads commits since last tag, infers semver bump, drafts changelog, creates release PR, merges it, waits for CI green, tags, and monitors the Docker build to completion. Use when the user says: prepare release, cut a release, bump version, create release PR, ship a new version, tag a release, deploy new version, or when all feature PRs are merged and it is time to release.

Assessment of plugin skill and hook changes needed after MCP or config changes. Evaluates skill references, hook context, config-format docs, and output style references. Invoked via skillPointer when filling plugin-impact notes.

Performance impact assessment for items with the needs-perf-review trait. Evaluates hot paths, query patterns, and measurement plans. Invoked via skillPointer when filling performance-baseline notes.

SQLite migration assessment for items with the needs-migration-review trait. Evaluates schema changes, table recreation patterns, data migration strategy, and Flyway migration correctness. Invoked via skillPointer when filling migration-assessment notes.

End-to-end workflow for taking MCP work items from backlog to merged PR. Handles git branching, schema-driven planning, implementation, independent review, and PR creation. Composes spec-quality, review-quality, and schema-workflow skills into a single pipeline. Use when a user says "implement this", "work on this item", "fix these bugs", "pick up the next task", "create a PR for this", "go through the backlog", or references specific MCP item IDs for implementation.

Guides the full lifecycle of a feature-implementation tagged MCP item (the feature container) — from queue through review. Creates or resumes the feature container, fills gate-enforced notes at each phase (requirements, design, implementation-notes, test-results), dispatches implementation subagents, and advances through queue, work, and review to terminal. Use when the user says: implement a feature, start a new feature, feature workflow, resume feature work, guide feature lifecycle, or references a feature-implementation item UUID.

Fast code-navigation toolkit for LLM agents. Use to explore codebases without reading whole files — get file shapes, public APIs, dependency graphs, call graphs, blast-radius analysis, and token-budgeted context.

Fast, AST-based code-navigation and log-squeezing toolkit — downloads the binary on install

Use when writing a complete SEO article. Includes the full anti-AI-slop ruleset (banned vocabulary, banned phrases, banned structural patterns) and voice rules. The agent researches the SERP itself if needed — no keyword data exports required.

Use when planning a topic cluster (hub + spokes) for a new content area. The agent researches the space, identifies the hub topic, maps the spokes, and produces a specific content plan with internal linking strategy.

Use when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing. The agent compares the page to the top-ranking competitors and produces a specific list of entities, subtopics, and relationships to add.

Use when auditing a specific page's SEO performance, content quality, and competitive position. The agent fetches the URL, Googles the primary keyword, reads the top 3 competitors, and produces a full 7-dimension audit — no exports, no analytics access required.

Use when planning link acquisition. Classifies the site's authority phase from site age and visible signals, then recommends phase-appropriate tactics from the bundled tactic playbook library. No backlink tool required.

Use when planning to rank for a specific keyword. The agent Googles it, reads the top 10, classifies intent, reads the top 3 competitor pages, and produces a 90-day ranking plan with intent, SERP analysis, and content recommendations.

Use when rewriting or refreshing an existing page that's underperforming. The agent fetches the URL, analyzes the current content, researches the SERP, and rewrites using the full anti-AI-slop ruleset — no data exports needed.

Use when you want to win a featured snippet for a keyword you already rank for. The agent checks the current snippet format, analyzes your content, and rewrites the relevant section to match what Google wants.

Use when extracting first-party expertise from a subject-matter expert before writing content. Produces a knowledge document of contrarian takes, specific examples, and surprising outcomes that AI can't fabricate.

Use when auditing a page for E-E-A-T signals. The agent reads the page and scores Experience, Expertise, Authoritativeness, and Trustworthiness — then tells you exactly what to add to each dimension.

Use when planning a new article. The agent Googles the keyword, reads the top 10 results, classifies intent, maps the content gap, and produces a writer-ready brief with structure, outline, and on-page artifacts. No keyword tool required.

A CLI tool for defining and running multi-agent workflows with the GitHub Copilot SDK

Lightweight knowledge graph for AI-assisted development. Impact analysis, code search, dependency tracking, and context generation.

Utilities for inspecting the local project workspace (list files, spot large folders, and determine where to focus).

@remote-mcp/client MCP server

@remote-mcp/example MCP server

Use when the user wants to push past conventional workflow limits with advanced performance techniques like parallel orchestration, streaming pipelines, or adaptive routing.

Use when the workflow feels over-engineered, has premature optimizations, unnecessary abstraction layers, or complexity beyond actual requirements.

Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project.

Use when the workflow feels too complex, has accumulated cruft, or has redundant steps and overlapping tools that need consolidation.

Use when the user wants to tailor a workflow for a specific industry, domain, or vertical with specialized expertise, terminology, and guardrails.

Analyze command history to identify which skills work, which fail, and where to improve.

Use when the workflow works but needs polish, or as the final step in a diagnose → fix → refine cycle before shipping.

Quick summary of the last session — commands run, files changed, and what to do next.

Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch.

Use when the workflow needs to self-correct, improve over time, or establish feedback loops and evaluation cycles.

Use when the workflow lacks error handling, has been failing in production, or needs retry logic, fallback strategies, and circuit breakers.

Use when the user wants to create templates, extract reusable patterns, document solutions, or build a pattern library from working workflows.

Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.

Use when the user wants to find problems, audit workflow quality, or get a comprehensive health check on their AI workflow.

Use when a single agent demonstrably cannot handle the task and multi-agent coordination is justified.

Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.

Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.

Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.

Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.

Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.