mcp-skillset

Use this skill whenever the user wants high-quality semantic retrieval, cross-textbook synthesis, reference verification, or authority-ranked question answering over the PageIndex corpus. Trigger on requests like "find which texts cover X", "compare sources", "retrieve semantically", "verify this claim from my textbooks", "build a reading list from the corpus", "cluster by topic", or any PageIndex question where naïve filename search would miss relevant material. This skill teaches a state-of-the-art hybrid retrieval workflow over the PageIndex catalog and live MCP extraction surface.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.

Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.

Real-time observability dashboard for multi-agent Claude Code sessions. Visualize agent interactions, tool usage, and session flows in real-time through a web dashboard. Track multiple agents running in parallel with swim lane visualization, event filtering, and live charts. **Key Features:** - 🔴 Real-time event streaming via WebSocket - 📊 Agent swim lanes showing parallel execution - 🔍 Event filtering by agent, session, event type - 📈 Live charts for tool usage patterns - 💾 Filesystem-based (no database required) **Inspired by [@indydevdan](https://github.com/indydevdan)**'s work on multi-agent observability. **Our approach:** Filesystem + in-memory streaming vs. indydevdan's SQLite database approach.

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.

Routes tasks to appropriate Task tool agents (subagents). Triggers on complex multi-step tasks, research, code review, exploration, or any task benefiting from specialized agent execution. Matches intent to agent types.

Create irresistible offers and pitches using Alex Hormozi's methodology from $100M Offers. Guides through value equation, guarantee frameworks, pricing psychology, and creating offers "too good not to take" for any product or service.

Routes analysis and debugging tasks. Triggers on analyze, debug, troubleshoot, review, audit, security, performance, optimize, investigate, trace.

Systematic API design methodology with 6 validated patterns covering parameter categorization, safe refactoring, audit-first approach, automated validation, quality gates, and example-driven documentation. Use when designing new APIs, improving API consistency, implementing breaking change policies, or building API quality enforcement. Provides deterministic decision trees (5-tier parameter system), validation tool architecture, pre-commit hook patterns. Validated with 82.5% cross-domain transferability, 37.5% efficiency gains through audit-first refactoring.

Manage Apple Reminders via the `remindctl` CLI on macOS (list, add, edit, complete, delete). Supports lists, date filters, and JSON/plain output.

Extract clean article content from URLs (blog posts, articles, tutorials) and save as readable text. Use when user wants to download, extract, or save an article/blog post from a URL without ads, navigation, or clutter.

Achieve comprehensive baseline (V_meta ≥0.40) in iteration 0 to enable rapid convergence. Use when planning iteration 0 time allocation, domain has established practices to reference, rich historical data exists for immediate quantification, or targeting 3-4 iteration convergence. Provides 4 quality levels (minimal/basic/comprehensive/exceptional), component-by-component V_meta calculation guide, and 3 strategies for comprehensive baseline (leverage prior art, quantify baseline, domain universality analysis). 40-50% iteration reduction when V_meta(s₀) ≥0.40 vs <0.20. Spend 3-4 extra hours in iteration 0, save 3-6 hours overall.

Git-native issue tracking with first-class dependency support. Issues stored alongside code in .beads/ directory, dual-persisted in SQLite (queries) and JSONL (git-friendly history). Hash-based IDs, DAG dependencies, daemon mode with RPC, and LSP-inspired multi-workspace support.

This skill should be used when working with BMAD (BMad-CORE) v6-alpha projects. BMAD is a universal human-AI collaboration platform with specialized modules for software development (BMM), agent building (BMB), creative intelligence (CIS), and project management (BMD). Use this skill to understand agent workflows, command patterns, scale-adaptive methodology, and effective utilization of the four-phase development system.

Unified router for development, infrastructure, and documentation tasks. Absorbs development-router + infrastructure-router + documentation-router.

High-performance graph analysis for beads issue tracker using 9 metrics (PageRank, Betweenness, HITS, Critical Path, etc). Provides AI-driven task prioritization, dependency analysis, and architectural health monitoring via robot protocol.

Comprehensive CI/CD pipeline methodology with quality gates, release automation, smoke testing, observability, and performance tracking. Use when setting up CI/CD from scratch, build time over 5 minutes, no automated quality gates, manual release process, lack of pipeline observability, or broken releases reaching production. Provides 5 quality gate categories (coverage threshold 75-80%, lint blocking, CHANGELOG validation, build verification, test pass rate), release automation with conventional commits and automatic CHANGELOG generation, 25 smoke tests across execution/consistency/structure categories, CI observability with metrics tracking and regression detection, performance optimization including native-only testing for Go cross-compilation. Validated in meta-cc with 91.7% pattern validation rate (11/12 patterns), 2.5-3.5x estimated speedup, GitHub Actions native with 70-80% transferability to GitLab CI and Jenkins.

Consult official Claude Code documentation from docs.claude.com using selective fetching. Use this skill when working on Claude Code hooks, skills, subagents, MCP servers, or any Claude Code feature that requires referencing official documentation for accurate implementation. Fetches only the specific documentation needed rather than loading all docs upfront.

Routes tasks to locally installed CLI tools using semantic matching. Triggers on tasks requiring shell commands, file operations, code search, data processing, visualization, or external tool invocation. Uses cli-index for semantic routing.

Perform bulk code refactoring operations like renaming variables/functions across files, replacing patterns, and updating API calls. Use when users request renaming identifiers, replacing deprecated code patterns, updating method calls, or making consistent changes across multiple locations. Activates on phrases like "rename all instances", "replace X with Y everywhere", "refactor to use", or "update all calls to".

The practice of restructuring and simplifying code continuously – reducing complexity, improving design, and keeping codebases clean.

BAIME-aligned refactoring protocol for Go hotspots (CLIs, services, MCP tooling) with automated metrics (e.g., metrics-cli, metrics-mcp) and documentation.

Executable documentation governance with compound engineering and abductive learning. Enforces the Seven Laws through type compilation, schema validation, and hookify-based enforcement. Implements programmatic compound engineering where K' = K ∪ crystallize(assess(τ)) for monotonic knowledge growth. Integrates abstracted abductive learning (OHPT protocol) for systematic debugging and pattern extraction. Trigger when writing code, debugging, establishing governance, or when mentioned vibecode, compound, abductive, or executable documentation. Self-validating and homoiconic.

Deep research skill for analyzing codebases systematically. Use when comprehensive codebase understanding is needed, particularly for new projects or when documenting architecture. This skill should be used when users request deep analysis, architecture documentation, or systematic codebase research starting from root-level directories.

Execute OpenAI Codex CLI for code analysis, refactoring, and automated editing. Also use for delegating complex debugging and research to GPT models for second opinions.

Routes tasks to reasoning frameworks and command patterns in commands-db. Triggers on structured development workflows, skill commands (sc:*), brainstorming, architecture, or any task matching command framework patterns. Uses reasoning-index for semantic routing.

Enforces official Claude Code component standards. Use when creating skills, agents, commands, or hooks. Validates existing components and offers remediation. Implements the "one skill = one specialized agent" architecture pattern.

Generate syntactically correct Claude Code configuration components (commands, subagents, skills, output styles) with optimal cross-component integration. Triggers on requests to create slash commands, agents, subagents, skills, output styles, Claude Code plugins, agentic workflows, or multi-agent pipelines. Ensures parameter validity per component type and semantic coherence across component references.

Self-improving holon implementing Plan→Execute→Assess→Compound workflow loops with explicit knowledge crystallization. Generalizes compound engineering beyond coding to any domain where work produces learnable signals: writing, research, learning, problem-solving, design. Use when: (1) task quality matters and can improve over time, (2) patterns emerge from repeated work, (3) institutional knowledge should accumulate, (4) future tasks should benefit from past learnings, (5) "compound", "learn from", "improve process", "capture learnings", or multi-step workflows with review cycles. Orchestrates parallel research agents, multi-lens assessment, and structured knowledge codification into searchable documentation with YAML frontmatter. Implements λο.τ → λ(ο,Κ).τ where Κ is accumulated knowledge that compounds interest-like over time.

Analyzes events through computer science lens using computational complexity, algorithms, data structures, systems architecture, information theory, and software engineering principles to evaluate feasibility, scalability, security. Provides insights on algorithmic efficiency, system design, computational limits, data management, and technical trade-offs. Use when: Technology evaluation, system architecture, algorithm design, scalability analysis, security assessment. Evaluates: Computational complexity, algorithmic efficiency, system architecture, scalability, data integrity, security.

Formal constraint theory unifying deontic logic (P/O/F/I operators), Juarrero's trichotomy (enabling/governing/constitutive), Hohfeldian rights (claim-duty, privilege-noright, power-liability, immunity-disability), and category-theoretic composition. Use when modelling permissions, obligations, prohibitions, rights structures, agent authority, governance systems, ontology validation, or any domain requiring formal constraint specification. Integrates with ontolog via λ-calculus mapping.

Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.

Orchestrates context retrieval from three CLI sources: limitless (personal life transcripts), research (online documentation/facts), pieces (local code/LTM). Use when external context is needed beyond the current codebase. Triggers on /context, /limitless, /research, /pieces, or balanced detection on complex prompts involving personal memory, technical documentation, or development history.

Routes context extraction requests to appropriate CLI tools. Triggers on /context, /limitless, /research, /pieces, or detected context needs from intent hook. Supports single, parallel, and augmented extraction modes.

Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScri

Guide for creating new skills in Kai's personal AI infrastructure. Use when user wants to create, update, or structure a new skill that extends capabilities with specialized knowledge, workflows, or tool integrations. Follows both Anthropic skill standards and PAI-specific patterns.

Create and validate skills. USE WHEN create skill, new skill, skill structure, canonicalize. SkillSearch('createskill') for docs.

Multi-perspective dialectical reasoning with cross-evaluative synthesis. Spawns parallel evaluative lenses (STRUCTURAL, EVIDENTIAL, SCOPE, ADVERSARIAL, PRAGMATIC) that critique thesis AND critique each other's critiques, producing N-squared evaluation matrix before recursive aggregation. Triggers on /critique, /dialectic, /crosseval, requests for thorough analysis, stress-testing arguments, or finding weaknesses. Implements Hegelian refinement enhanced with interleaved multi-domain evaluation and convergent synthesis.

Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.

Cursor is an AI-powered code editor and development environment that combines intelligent coding assistance with enterprise-grade features and workflow automation. It extends beyond basic AI code comp...