waza
Engineering skills for Claude: think (architecture), design (UI), check (code review), hunt (debugging), write (prose), learn (research), read (URL/PDF fetch), health (config audit). Triggers on slash commands or intent.
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Memory Engine v1.5.2
A memory and learning system for Claude Code, built with hooks and markdown.
mengram-memory
Long-term memory with 3 types (facts, events, workflows). Remember user preferences, past conversations, and learned procedures across sessions. Use when recalling what the user said before, saving important info, getting user context, or tracking completed workflows.
Io.Github.MCP Hive/Mcp Hive Proxy
MCP Hive is a gateway to paid commercial-grade MCP servers. Learn more at https://mcp-hive.com
openclaw-self-healing
4-tier autonomous self-healing system for OpenClaw Gateway with persistent learning, reasoning logs, and multi-channel alerts. Features Claude Code as Level 3 emergency doctor for AI-powered diagnosis and repair.
Knowledge Graph Mcp
Knowledge graph MCP for student learning with spaced repetition and mastery tracking
learning-goal
Guide the learner through a structured goal-setting exercise grounded in research on Mental Contrasting with Implementation Intentions (MCII). The exercise helps developers set concrete learning goals, visualize meaningful outcomes, anticipate realistic obstacles, and build if-then plans to overcome them.
trl-training
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
biome-developer
General development best practices and common gotchas when working on Biome. Use for avoiding common mistakes, understanding Biome-specific patterns (AST, syntax nodes, string extraction, embedded languages), and learning technical tips.
learned-codex-proxy-tool-calls-parse
Parse ChatGPT Codex backend SSE for tool_calls (content_part.added, output_item.done, function_call_arguments.delta/done) and return OpenAI Chat Completions format. Use when implementing or fixing tool call parsing in a Codex reverse proxy (OpenAI-compatible /v1/chat/completions) that consumes Responses API-style SSE.
Io.Github.Varun29ankuS/Shodh Memory
Cognitive memory for AI agents — semantic search, Hebbian learning, knowledge graphs.
code-memory
Captures and retrieves structural understanding of source code gained during investigations. Use after reading and understanding code to save context for future sessions, or before investigating code to check for existing context. Stores summaries in memory/ files mirroring source paths. Triggers: "save what I learned", "remember this code", "check if we know about", "investigate", or any post-investigation context capture.
clone-patterns
Learn From the Best â analyze patterns from any codebase and apply them to yours. Use when user wants to adopt best practices from another repo, compare code quality, or learn how top projects are structured.
Bolor Brain Mcp
Cognitive architecture MCP server with memory, learning, and contextual understanding
flashcard
Create and edit flashcards for spaced repetition learning. Use when the user wants to create, modify, format, or troubleshoot flashcards in their notes.
ax-crew
Guide for building multi-agent AI systems with ax-crew. Use when creating agent crews, configuring agents, using MCP servers, shared state, sub-agents, streaming, ACE learning, function registries, metrics/cost tracking, telemetry, or agent workflows with @amitdeshmukh/ax-crew.
OMEGA Memory
Persistent memory, coordination, and learning for AI agents. Local-first, 25 MCP tools.
synalinks
Build neuro-symbolic LLM applications with Synalinks framework. Use when working with DataModel, Program, Generator, Module, training LLM pipelines, in-context learning, structured output, JSON operators, Branch/Decision control flow, FunctionCallingAgent, RAG/KAG, or Keras-like LLM workflows.
Environment Diagnostic
You are a diagnostic assistant. Check the user's learning environment and report issues clearly.
hindsight-docs
Complete Hindsight documentation for AI agents. Use this to learn about Hindsight architecture, APIs, configuration, and best practices.
agentic-data-science-competition
AI Agent-driven Kaggle competition workflow. Learn from real competition experience: score stabilization patterns, submission troubleshooting, kernel workflows, GPU task delegation, and the spec-driven development approach that achieved top leaderboard positions. Use when: working on any Kaggle competition, analyzing submission failures, setting up automated pipelines, or replicating top notebook solutions.
wrapup
Wrap up and save the current session summary to the session log. Use at end of session when the user says 'bye', 'wrap up', 'save session', or an end-of-session signal is detected. /wrapup writes to 07-logs/ only. Do NOT use for: promoting insights to memory/ (use recap), synthesizing a topic across sessions (use distill), or teaching a single preference (use learn).
design-workflow
Spec-first workflow for designers who use Claude Code to design in Figma. Covers components and full interfaces/screens. Use when a designer wants to: (1) write or review a component or screen spec, (2) generate a Figma design via MCP, (3) review and iterate on a design, (4) close or abandon work. Triggers: "spec", "design", "screen", "review", "done", "drop", "learn", "sync", "status", "setup", "workflow", "what's next", "new component", "new screen", or any design request.
Harness â Multi-Agent Orchestration
Orchestrate complex tasks through Planning â Generation ��� Evaluation â Retro. Fresh sub-agents per checkpoint prevent drift. Retro accumulates learning across tasks.
ACE â Learn from Traces
This skill ships `learn_from_traces.py`, a script that reads OpenClaw session transcripts, feeds them through the ACE learning pipeline, and writes an updated skillbook to disk.
Agentic Jujutsu - AI Agent Version Control
> Quantum-ready, self-learning version control designed for multiple AI agents working simultaneously without conflicts.
algo-sensei
Your personal DSA & LeetCode mentor. Use for problem explanations, progressive hints, code reviews, mock interviews, pattern recognition, complexity analysis, and custom problem generation. Automatically adapts to your learning style and request type.
learn-on
Enable continuous learning mode for automatic insight extraction
claude-reflect
Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.
subtitles
Get subtitles from YouTube videos for translation, language learning, or reading along. Use when the user asks for subtitles, subs, foreign language text, or wants to read video content. Supports multiple languages and timestamped output for sync'd reading.
buck2-rule-basics
Guide users through writing their first Buck2 rule to learn fundamental concepts including rules, actions, targets, configurations, analysis, and select(). Use this skill when users want to learn Buck2 basics hands-on or need help understanding rule writing.
AI Analysis Guide
> AI/ML is the technology for extracting value from data. This skill systematically covers all aspects of AI analysis â from machine learning fundamentals, deep learning, natural language processing, and computer vision to practical model development workflows.
whats-new-include-content-rules
Content rules and formatting standards for ASP.NET Core What's New include files. Use when creating or editing include files in aspnetcore/release-notes/*/includes/. Covers heading levels, xref API references, link formatting, phrasing style, breaking change designations, contributor acknowledgments, file naming, and exclusions. Use for: What's New include file, release notes include, xref format, include content rules, H3 heading level, relative link Microsoft Learn, include file naming convention.
learning-opportunity
Pause development to learn a concept at 3 levels of depth. Triggers when unfamiliar patterns, new frameworks, or complex concepts appear during development.
assessment-design
Evidence-based assessment design with rubrics, feedback strategies, and formative checkpoints. Aligns each assessment to learning objectives using Bloom's taxonomy. Applies Nicol's 7 principles of good feedback practice. Reads from /learning-objectives manifest and extends it with assessment specs. (idstack)
self-learning-skills
Memory sidecar for agent work: recall before tasks, record learnings after tasks, review recommendations, optional backport bundles.
Brainbox
Hebbian memory for AI agents. Learns file patterns, predicts next tools, saves tokens.
add-memory
Record important information to long-term memory for learning user preferences, successful patterns, and error lessons. When you need to remember user preferences, save successful patterns, or record lessons from errors.
brev-cli
Manage GPU and CPU cloud instances with the Brev CLI for ML workloads and general compute. Use when users want to create instances, search for GPUs or CPUs, SSH into instances, open editors, copy files, port forward, manage organizations, or work with cloud compute. Supports fine-tuning, reinforcement learning, training, inference, batch processing, and other ML/AI workloads. Trigger keywords - brev, gpu, cpu, instance, create instance, ssh, vram, vcpu, A100, H100, cloud gpu, cloud cpu, remote machine, finetune, fine-tune, RL, RLHF, training, inference, deploy model, serve model, batch job.
0-to-1-launch
Launch new products from idea to first customers. Use when launching products, finding early adopters, building launch week playbooks, diagnosing why adoption stalls, or learning that press coverage does not equal growth. Includes the three-layer diagnosis, the 2-week experiment cycle, and the launch that got 50K impressions and 12 signups.
AI/ML Attack Surface
This skill should be used when the user asks about "AI security", "ML pipeline attacks", "prompt injection", "model deserialization", "unsafe model loading", "Jupyter injection", "LLM security", or needs to identify AI/ML-specific vulnerabilities in codebases that use machine learning frameworks.
oak
Find out what happened, what was decided, and what depends on what in your codebase. Use this skill whenever you need to: recall past decisions or discussions ("what did we decide about X?"), check what might break before refactoring ("what depends on this module?"), find conceptually similar code that grep would miss ("all the retry/backoff logic"), look up past bugs, gotchas, or learnings, query session history or agent run costs, store observations about the codebase, or understand how components connect end-to-end. Powered by semantic search, memory lookup, and direct SQL against the Oak CI database (.oak/ci/activities.db). Also use when the user mentions oak_search, oak_context, oak_remember, oak_resolve_memory, or asks to run queries against activities.db or oak.
Microsoft Learn MCP
Official Microsoft Learn MCP Server – real-time, trusted docs & code samples for AI and LLMs.
Help
MCP server for the mctx platform. Search documentation to learn how to scaffold, build, deploy, and
learn-from-chat
Extract 1-2 memorable lessons from the current conversation and format them as compact TIL (Today I Learned) notes with a daily spaced-repetition quiz reminder. Use when the user asks "what should I remember from this chat", "extract lessons", "generate my daily review", "what did I learn today", "summarize learnings", or finishes a technical conversation wanting to retain knowledge without re-asking AI next time.
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
self-learning
Autonomous skill generator that learns new technologies from the web. Use when, users want to learn about a new library/framework/tool, need to create a skill for an unfamiliar technology, want to research and document a technology's usage patterns, or invoke with `/learn <topic>`. This skill uses web search and browser tools to discover, extract, and synthesize documentation into a reusable skill.
agent-learnings
Log durable agent learnings as JSON entries for reuse across tasks.
BIGHUB
Decision learning for AI agent actions. Evaluate, score, decide, and learn from outcomes.