Is brain-ecosystem-mcp safe to install?

SaferSkills independently audited brain-ecosystem-mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

plugins/tradecraft/skills/brain-ecosystem-mcp/SKILL.md · 1 file

plugins/tradecraft/skills/brain-ecosystem-mcp/SKILL.md64.0 KB · Markdown

name: brain-ecosystem-mcp

description: Brain Ecosystem — 3 autonomous self-learning AI brains as MCP servers for Claude Code. Brain (280 tools): error memory, code intelligence, autonomous research. Trading Brain (181 tools): adaptive trading, paper trading, signal learning, backtesting.

kind: reference

category: trading/orchestration

status: active

tags: [backtesting, brain, claude-code, ecosystem, mcp, orchestration, trading]

related_skills: [analyze, master-trading-workflow, trading-autopilot]

brain-ecosystem-mcp

USE FOR:

"self-learning AI brain for Claude Code"
"persistent memory MCP server"
"autonomous research agent with hypothesis testing"
"trading brain with adaptive strategies"
"MCP server with 280+ tools"
"AI that learns from errors across sessions"
"dream mode memory consolidation"

tags: [MCP, self-learning, autonomous, Claude-Code, Trading-Brain, error-memory, knowledge-graph, CCXT, persistent-memory, Hebbian] kind: tool category: mcp-integration

What Is Brain Ecosystem?

3 autonomous self-learning AI brains running as MCP servers, designed for Claude Code.

Repo: https://github.com/timmeck/brain-ecosystem
Install: npm install -g @timmeck/brain && brain setup
Architecture: Each brain = separate process, separate SQLite DB, separate port
Communication: IPC named pipes between brains (Hebbian synapse network)
Dashboard: Command Center at localhost:7790 (13 pages of metrics)

"117+ autonomous engines run in feedback loops — observing, detecting anomalies, forming hypotheses, testing and falsifying them statistically"

Three Brains

Brain	Port	Tools	Specialty
Brain	7777-7778	280	Error memory, code intelligence, autonomous research
Trading Brain	7779-7780	181	Adaptive trading, paper trading, signal learning, backtesting
Marketing Brain	7781-7782	177	Content strategy, cross-platform optimization
Command Center	7790	—	Unified dashboard, 13 monitoring pages

Installation

# Main brain
npm install -g @timmeck/brain
brain setup        # configures Claude Code MCP automatically

# Trading brain
npm install -g @timmeck/trading-brain
trading setup

# Marketing brain
npm install -g @timmeck/marketing-brain
marketing setup

For Cursor/Windsurf/Cline (HTTP/SSE):

{
  "mcpServers": {
    "brain": {
      "url": "http://localhost:7778/sse"
    }
  }
}

Brain (280 MCP Tools)

Core Capabilities

Error Memory: Tracks every error across sessions, learns solutions, never repeats
Code Intelligence: Understands codebase structure, dependencies, patterns
Autonomous Research: Multi-step roadmaps, hypothesis generation + falsification
Knowledge Graph: Persistent cross-session knowledge with entity relationships
Dream Mode: Offline memory consolidation (runs when idle)
Self-modification: Can edit own code with human approval gates

Research Engine

Goal → decompose → sub-goals → hypotheses
    → test statistically (17 falsification methods)
    → confirm/reject → update knowledge graph
    → synthesize report

Data Sources

Brave Search + Playwright (web research)
Firecrawl (deep page extraction)
GitHub (code assimilation from repos)
Vision: Anthropic + Ollama (image analysis)

Trading Brain (181 MCP Tools)

Capabilities

Adaptive strategies: Learn which signals work, weight by performance
Paper trading: Simulate with real market data before live
Signal learning: Identify patterns that predicted past moves
Backtesting: Automated strategy evaluation with feedback loops
Live data: CCXT WebSocket (100+ exchanges) + CoinGecko

How It Adapts

Trade executed → outcome recorded
→ signal that preceded it gets weighted up/down
→ strategy parameters auto-adjusted
→ anomaly detection flags regime changes
→ hypothesis: "this pattern no longer works" → test → confirm → disable

Self-Learning Architecture

Input (error/trade/content event)
    ↓
117 autonomous engines in parallel feedback loops:
    - AnomalyDetector
    - HypothesisGenerator
    - StatisticalFalsifier
    - PatternRecognizer
    - KnowledgeGraphUpdater
    - DreamConsolidator (offline)
    ↓
Hebbian synapse: brains share relevant discoveries
    ↓
Knowledge persists in SQLite → available next session

Dream Mode (Memory Consolidation)

When idle (no active requests):

Brain enters "dream mode":
1. Replays recent experiences
2. Identifies patterns not obvious during active processing
3. Prunes weak connections (low-weight knowledge)
4. Strengthens high-value patterns
5. Prepares summaries for fast retrieval next session

Claude Code Integration

After brain setup, Claude Code gets access to all 280 Brain tools:

# In Claude Code session — brain remembers across sessions:
> "You made an error with X last week"
→ Brain recalls error memory: exact context + solution applied

> "Research async patterns in Python"  
→ Brain creates 5-step research roadmap, executes autonomously,
  synthesizes findings into knowledge graph entry

> "What patterns have worked for BTCUSDT this month?"
→ Trading Brain queries signal performance history → ranked list

Key Advantages Over Standard Memory Tools

Feature	Brain Ecosystem	Standard MCP Memory
Error learning	✓ (auto, cross-session)	Manual
Hypothesis testing	✓ (statistical)	✗
Dream consolidation	✓	✗
Self-modification	✓ (human-gated)	✗
Trading brain	✓ (181 tools)	✗
Inter-brain comms	✓ (Hebbian)	✗
Vision	✓ (Anthropic+Ollama)	✗

KNOWLEDGE INJECTION: AntV MCP Server Chart

Source: https://github.com/antvis/mcp-server-chart

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: mcp-server-chart

name: mcp-server-chart description: > AntV MCP Server Chart - MCP server generating 26+ chart types via AntV. Tools: generate_bar_chart, generate_line_chart, generate_pie_chart, generate_network_graph, generate_sankey, generate_treemap, generate_spreadsheet, etc. npx @antv/mcp-server-chart. Works with Claude, VSCode, Dify. USE FOR:

generate charts via MCP
bar/line/pie/scatter chart from data
network graph visualization
sankey treemap funnel chart
Claude generates charts automatically

tags: [MCP, charts, AntV, visualization, bar, line, pie, network-graph, sankey] kind: tool category: mcp-integration

What Is mcp-server-chart?

MCP server generating 26+ visualization types using AntV.

Repo: https://github.com/antvis/mcp-server-chart
Install: npm install -g @antv/mcp-server-chart

MCP Config (Claude Code / Desktop)

{
  "mcpServers": {
    "mcp-server-chart": {
      "command": "npx",
      "args": ["-y", "@antv/mcp-server-chart"]
    }
  }
}

Available Tools (generate_* pattern)

Standard:    area, bar, column, line, pie, scatter, dual_axes
Statistical: boxplot, histogram, violin
Flow:        funnel, sankey, treemap
Hierarchy:   mind_map, fishbone, org_chart
Network:     network_graph, venn
Geographic:  district_map, path_map, pin_map
Other:       radar, word_cloud, liquid, spreadsheet

Usage in Claude

User: "Plot this data as a bar chart: [data]"
Claude: calls generate_bar_chart({ data: [...], xField: "x", yField: "y" })
→ returns chart image/URL

KNOWLEDGE INJECTION: Dify

Source: https://github.com/langgenius/dify

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: dify-llm-platform

name: dify-llm-platform description: > Dify - open-source LLM app development platform. Visual canvas for AI workflows, RAG pipelines (PDF/PPT ingestion), agent builder (50+ tools: Google, DALL-E, Wolfram), LLMOps observability, BaaS APIs. Supports GPT, Claude, Llama3, Mistral, 100+ models. Self-host (Docker) or cloud (200 free GPT-4 calls). USE FOR:

build LLM app with visual workflow
RAG pipeline from documents
AI agent with tools
self-hosted ChatGPT alternative
LLMOps monitoring

tags: [Dify, LLM, RAG, agent, workflow, visual, self-hosted, open-source, GPT, Claude] kind: platform category: ai-agent-builder

What Is Dify?

Open-source LLM application development platform.

Repo: https://github.com/langgenius/dify
Stars: 100k+
Deploy: Docker Compose (2 CPU, 4GB RAM) or dify.ai cloud

Core Capabilities

Visual Workflow Canvas: drag-and-drop LLM pipeline builder
RAG: ingest PDFs, PPTs, web pages → vector search → grounded answers
Agent Builder: Function Calling or ReAct agents + 50+ built-in tools
Model Hub: GPT-4o, Claude, Llama3, Mistral, Gemini, + OpenAI-compatible
LLMOps: trace every call, monitor cost, replay prompts
BaaS API: REST API for any app to call your workflow

Docker Install

git clone https://github.com/langgenius/dify
cd dify/docker
cp .env.example .env
docker compose up -d
# Access: http://localhost/install

Agent Tools (50+)

Google Search, Bing, DuckDuckGo, Wikipedia, DALL-E, Stable Diffusion, WolframAlpha, Weather API, News API, Code execution, Web scraping, + custom tools

KNOWLEDGE INJECTION: Open WebUI

Source: https://github.com/open-webui/open-webui

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: open-webui

name: open-webui description: > Open WebUI - self-hosted, offline-capable AI platform. Ollama + OpenAI-compatible backends. RAG with 9 vector DBs, web search (15+ providers), image gen (DALL-E/ComfyUI), voice/video chat, Python function calling, enterprise auth (LDAP/OAuth/SCIM). Docker install. Privacy-first local AI deployment. USE FOR:

self-hosted ChatGPT alternative
local Ollama web interface
offline AI with RAG
multi-model comparison
enterprise private AI deployment

tags: [Open-WebUI, Ollama, self-hosted, RAG, local-AI, privacy, ChatGPT-alternative] kind: platform category: ai-agent-builder

What Is Open WebUI?

Extensible self-hosted AI platform — runs fully offline.

Repo: https://github.com/open-webui/open-webui
Supports: Ollama (local LLMs) + any OpenAI-compatible API

Quick Install

# With Ollama bundled
docker run -d -p 3000:8080 --gpus=all   -v ollama:/root/.ollama -v open-webui:/app/backend/data   --name open-webui ghcr.io/open-webui/open-webui:ollama

# Existing Ollama
docker run -d -p 3000:8080   --add-host=host.docker.internal:host-gateway   -v open-webui:/app/backend/data   --name open-webui ghcr.io/open-webui/open-webui:main
# Access: http://localhost:3000

Key Features vs ChatGPT

Feature	Open WebUI	ChatGPT
Self-hosted	Yes	No
Offline	Yes	No
Local models	Ollama	No
RAG	9 vector DBs	Limited
Web search	15+ providers	Yes
Image gen	DALL-E/ComfyUI/A1111	DALL-E only
Python tools	Native	Sandboxed
Cost	Free	$20/mo

KNOWLEDGE INJECTION: Awesome MCP Servers (Reference)

Source: https://github.com/punkpeye/awesome-mcp-servers

Routed to: claude-ai-tools.md

Date: 2026-03-18

Awesome MCP Servers — Ecosystem Reference

500+ MCP servers across 40+ categories. Key ones by domain:

Development & Code

Server	What It Does
GitHub MCP	Repos, PRs, issues, code search
GitLab MCP	GitLab API integration
Filesystem MCP	Local file read/write/search
Git MCP	git log, diff, branch operations
Docker MCP	Container management
Kubernetes MCP	Cluster insights, kubectl

AI & Agents

Server	What It Does
Memory MCP	Persistent knowledge graph
Sequential Thinking	Chain-of-thought reasoning
Fetch/Browser	Web content retrieval
Playwright MCP	Browser automation
AgentShield	Security vulnerability scanning

Data & Databases

Server	What It Does
PostgreSQL MCP	Schema inspection + queries
MongoDB MCP	Document DB queries
Elasticsearch MCP	Search and analytics
Snowflake MCP	Data warehouse queries
SQLite MCP	Local database

Communication

Server	What It Does
Gmail MCP	Email read/send/search
Slack MCP	Channel messages, search
Telegram MCP	Bot messages
Discord MCP	Server interaction

Productivity

Server	What It Does
Notion MCP	Pages, databases, blocks
Jira MCP	Issues, sprints, projects
Google Calendar MCP	Events, scheduling
Airtable MCP	Base/table operations

Finance & Trading

Server	What It Does
Crypto APIs	Price feeds, portfolio
Payment MCP	Stripe, payment processing
Multi-cloud cost	Cloud cost analysis

Search & Data

Server	What It Does
Brave Search	Web search
Firecrawl	Deep web scraping
EXA MCP	AI-powered search
75+ data extractors	Specialized data sources

Full list: https://github.com/punkpeye/awesome-mcp-servers

KNOWLEDGE INJECTION: Everything Claude Code

Source: https://github.com/affaan-m/everything-claude-code

Routed to: claude-ai-tools.md

Date: 2026-03-18

Everything Claude Code — Production Optimization Reference

Complete system for maximizing Claude Code performance (10+ months production-tested).

Components Overview

Component	Count	Purpose
Agents	21-25	Specialized subagents (planner, architect, reviewer, security, language-specific)
Skills	102+	Domain workflows (TDD, security review, frontend/backend patterns)
Commands	52-57	Slash commands: /tdd, /plan, /code-review, /build-fix, /e2e
Rules	29-34	Universal + language-specific (TS, Python, Go, Swift, PHP, Java)
Hooks	8-20+	PreToolUse, PostToolUse, Stop, SessionStart automations
MCP	14+	Pre-configured: GitHub, Supabase, Vercel, Railway

Key Slash Commands

/tdd           - Test-driven development workflow
/plan          - Architecture planning
/code-review   - Security + quality review
/build-fix     - Build error diagnosis
/e2e           - End-to-end test generation
/multi-plan    - Multi-agent orchestration
/instinct-status - Check learned patterns
/evolve        - Extract patterns from session into skills

Token Optimization Strategies

Use Haiku/Sonnet for simple tasks, Opus for complex reasoning
Compress context with /compact before long sessions
Use subagents with limited scope (avoid full context bleed)
Skills reduce re-explanation overhead
Auto-extract patterns → reusable skills over time

Hooks Patterns

{
  "hooks": {
    "PreToolUse": ["secret-detector", "format-checker"],
    "PostToolUse": ["session-persist", "pattern-extractor"],
    "Stop": ["summary-generator"],
    "SessionStart": ["context-loader", "instinct-injector"]
  }
}

Specialized Agents

planner: breaks task into subtasks, creates task graph
architect: system design, file structure decisions
code-reviewer: security + style + logic review
security-reviewer: OWASP, injection, auth vulnerabilities
go/python/ts-reviewer: language-specific best practices

KNOWLEDGE INJECTION: Cherry Studio

Source: https://github.com/CherryHQ/cherry-studio

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: cherry-studio

name: cherry-studio description: > Cherry Studio - desktop AI client for Windows/Mac/Linux. Unifies 20+ AI providers: OpenAI, Anthropic Claude, Gemini, Ollama (local), LM Studio, Qwen, Kimi. 300+ pre-configured assistants, multi-model simultaneous chat, MCP server support, document processing (PDF/Office/images), no environment setup needed. USE FOR:

unified desktop AI client
multi-model comparison chat
local + cloud AI in one app
300+ pre-built AI assistants
MCP integration desktop

tags: [Cherry-Studio, desktop, multi-model, Ollama, Claude, GPT, Gemini, MCP, assistants] kind: tool category: ai-agent-builder

What Is Cherry Studio?

Unified desktop AI client — no setup required.

Repo: https://github.com/CherryHQ/cherry-studio
Platforms: Windows, macOS, Linux
Models: 20+ providers, local (Ollama/LM Studio) + cloud

Supported Providers

OpenAI (GPT-4o) · Anthropic (Claude) · Google (Gemini) · Mistral Ollama (local) · LM Studio (local) · Qwen · Kimi · Baidu · iFlytek OpenRouter · Perplexity · Poe · + more

Key Features

300+ assistants: pre-configured prompts for coding, writing, analysis
Multi-model chat: send same prompt to multiple models simultaneously
Document processing: PDF, Word, Excel, PowerPoint, images
MCP support: connect MCP servers (with Marketplace planned)
WebDAV sync: sync conversations across devices
No setup: download → login/API key → use immediately

KNOWLEDGE INJECTION: AionUi

Source: https://github.com/iOfficeAI/AionUi

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: aionui-desktop-agent

name: aionui-desktop-agent description: > AionUi - free open-source multi-agent desktop platform (macOS/Windows/Linux). Auto-detects Claude Code, Codex, Qwen Code CLI tools. 20+ AI providers. Cron scheduling for 24/7 unattended automation. File management, Excel AI, PowerPoint generation, image recognition. Remote access via Telegram/Lark/DingTalk. USE FOR:

multi-agent desktop automation
unify Claude Code + Codex in one UI
cron scheduled AI tasks
file batch rename organize
Excel AI processing
remote AI control via Telegram

tags: [AionUi, desktop-agent, multi-agent, Claude-Code, automation, scheduling, Telegram, remote] kind: tool category: ai-agent-builder

What Is AionUi?

Multi-agent desktop platform — AI agents operate autonomously on your computer.

Repo: https://github.com/iOfficeAI/AionUi
Platforms: macOS · Windows · Linux
Install: download desktop app, sign in with Google or API key

Key Differentiators

Auto-detects existing Claude Code, Codex, Qwen Code CLIs → unifies them
Built-in agent: no CLI setup needed (full file/web/code capabilities)
Multi-agent: run Claude Code + Codex simultaneously, independent contexts

Supported Models (20+ providers)

Gemini · Anthropic Claude · OpenAI · Qwen · Kimi · Baidu · Ollama · LM Studio

Automation Capabilities

Feature	Description
Cron scheduling	Natural language task descriptions, runs 24/7
File management	Batch rename, intelligent classification
Excel processing	AI analysis, formatting, report generation
Document generation	PowerPoint, Word, Markdown automated creation
Image operations	Text-to-image, editing, recognition

Remote Access

WebUI (browser access)
Telegram bot integration
Lark/Feishu (enterprise)
DingTalk

KNOWLEDGE INJECTION: Awesome Agent Skills (VoltAgent)

Source: https://github.com/VoltAgent/awesome-agent-skills

Routed to: claude-ai-tools.md

Date: 2026-03-18

Awesome Agent Skills — Curated Registry (549+ skills)

Real-world agent skills from 40+ organizations. Works with Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot.

Source: https://github.com/VoltAgent/awesome-agent-skills

Notable Skills by Category

Official / Anthropic

anthropics/docx — Create, edit, analyze Word documents
PDF, presentations, design, web artifacts

Infrastructure & DevOps

Vercel, Cloudflare, Netlify, AWS, Google Cloud skills
HashiCorp Terraform code generation
Kubernetes, Docker, CI/CD workflows

Databases & Data

Supabase, Neon, ClickHouse, Tinybird

Dev Frameworks

React, Next.js, React Native, Expo, WordPress

AI/ML

Hugging Face, Replicate, fal.ai, OpenAI integration

Security (Trail of Bits — 23 skills)

Insecure defaults detection
Property-based testing + smart contracts
Semgrep rule creation for vulnerability detection

Product & SaaS

Content strategy planning
Pricing/packaging/monetization strategy
CAC, LTV, payback period calculations (saas-economics-efficiency-metrics)
Investor materials, fundraising

Web3/Crypto

Binance trading tools
Blockchain interaction skills

Security warning: Skills are curated, not audited. Review before install. Scanners: Snyk Skill Security Scanner, Agent Trust Hub.

KNOWLEDGE INJECTION: wshobson/ai-trading-crew (112 agents, 146 skills)

Source: https://github.com/wshobson/agents

Routed to: claude-ai-tools.md

Date: 2026-03-18

wshobson/ai-trading-crew — 112 Agents + 146 Skills System

Production Claude Code plugin system: 112 agents, 146 skills, 79 tools, 72 plugins.

Architecture

72 focused single-purpose plugins (1-6 per category)
23 categories, mix-and-match install
3-tier model: Opus (complex) / Sonnet (mid) / Haiku (simple)
Progressive disclosure: skills load only when activated

Install

/plugin marketplace add wshobson/agents
/plugin install python-development
/plugin install security-audit

KNOWLEDGE INJECTION: Ruflo (Enterprise Agent Orchestration)

Source: https://github.com/ruvnet/ruflo

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: ruflo-agent-orchestration

name: ruflo-agent-orchestration description: > Ruflo (formerly Claude Flow) - enterprise multi-agent orchestration for Claude Code. 60+ specialized agents in swarms (hierarchical/mesh/ring/star topologies). Self-learning SONA architecture, HNSW vector search (150x-12500x faster retrieval), EWC++ anti-forgetting, Mixture of Experts routing. MCP server. npx ruflo@latest init USE FOR:

enterprise multi-agent orchestration
Claude Code swarm of agents
self-learning agent routing
60+ specialized agents
MCP server for Claude Code

tags: [ruflo, multi-agent, swarm, orchestration, Claude-Code, MCP, self-learning, HNSW] kind: framework category: ai-agent-builder

What Is Ruflo?

Enterprise AI agent orchestration platform built on Claude Code.

Repo: https://github.com/ruvnet/ruflo
Install: npx ruflo@latest init --wizard
Claude Code MCP: claude mcp add ruflo -- npx ruflo@latest mcp start

Key Differentiators vs CrewAI/LangGraph

Feature	Ruflo	CrewAI	LangGraph
Self-learning	SONA (0.05ms adapt)	No	No
Anti-forgetting	EWC++	No	No
Vector retrieval	HNSW (12500x faster)	Basic	Basic
Agent topologies	4 (hier/mesh/ring/star)	Hierarchical	Graph
Swarm size	Unlimited	Limited	Limited

Installation

curl -fsSL https://cdn.jsdelivr.net/gh/ruvnet/claude-flow@main/scripts/install.sh | bash
# or
npx ruflo@latest init --wizard

Usage

npx ruflo@latest --agent coder --task "Implement user authentication"
npx ruflo@latest --list                    # list all 60+ agents
npx ruflo@latest mcp start                 # start MCP server

Swarm Topologies

Hierarchical: Queen agent → Worker agents (tree structure)
Mesh:         All agents peer-to-peer (collaborative)
Ring:         Sequential pipeline (A→B→C→A)
Star:         Central coordinator → spoke agents

KNOWLEDGE INJECTION: alirezarezvani/claude-skills (204 skills)

Source: https://github.com/alirezarezvani/claude-skills

Routed to: claude-ai-tools.md

Date: 2026-03-18

claude-skills — 204 Production Skills Library

Most comprehensive open-source Claude Code skills library (204 skills, 266 Python CLI tools). Compatible with 11 AI coding platforms.

Skill Domains

Domain	Skills	Notable
Engineering Core	25	Architecture, DevOps, security, AI/ML
Engineering POWERFUL	30	Advanced tier skills
Playwright Testing	9+	Test generation, migration
Product Management	13	Strategy, UX research, analytics
Marketing	43	7 specialized pods
Project Management	6	PM, scrum, Jira/Confluence
Regulatory/Quality	12	FDA, ISO, MDR, GDPR
C-Level Advisory	28	Full executive suite
Business & Growth	4	Customer success, sales
Finance	2	Financial analysis, SaaS metrics

Skill Structure

skill-name/
├── SKILL.md          # structured instructions + workflow
├── tools/            # Python CLI tools (standard library only)
│   └── tool_name.py
├── scripts/          # optional automation scripts
└── references/       # templates + domain knowledge

Install

# Claude Code plugin marketplace
/plugin install alirezarezvani/claude-skills/engineering

# Manual: copy skill folder to ~/.claude/skills/
cp -r marketing-pod ~/.claude/skills/

KNOWLEDGE INJECTION: ARIS (Auto Research in Sleep)

Source: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: aris-auto-research

name: aris-auto-research description: > ARIS - Autonomous ML research methodology using Claude Code + GPT adversarial reviewer. 4 workflows: idea discovery, experiment bridge, auto-review loop, paper writing. 20 composable skills. State file recovery (no daemon needed). GPU automation via SSH. Proven: borderline reject (5/10) -> submission-ready (7.5/10) in 4 overnight rounds. USE FOR:

autonomous ML research overnight
auto paper review and improvement loop
GPU experiment automation SSH
academic paper writing Claude Code
cross-model adversarial review (Claude + GPT)

tags: [research, autonomous, ML, paper-writing, GPU, Claude-Code, adversarial-review, overnight] kind: methodology category: ai-agent-builder

What Is ARIS?

Autonomous ML research methodology — Claude Code executes, GPT-5.4 reviews adversarially.

Repo: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep
Result: borderline reject → submission-ready in 4 overnight rounds (5/10 → 7.5/10)
Philosophy: cross-model collaboration avoids local minima of self-critique

4 Workflows

Workflow 1:   Idea Discovery
              literature survey → 8-12 ideas → novelty check → GPU pilots → ranked report

Workflow 1.5: Experiment Bridge
              reads plan → implements code → validates small-scale → deploys full GPU suite

Workflow 2:   Auto Review Loop (key workflow)
              GPT-5.4 reviews paper → identifies gaps → Claude Code writes experiments
              → SSH deploys to GPU → monitors → rewrites sections → repeat (max 4 rounds)

Workflow 3:   Paper Writing
              narrative → claims-evidence matrix → figures/tables → LaTeX → PDF → 2-round auto-improve

Slash Commands

/research-pipeline "your direction"   # full pipeline
/idea-discovery "topic"               # workflow 1
/experiment-bridge                    # workflow 1.5
/auto-review-loop "paper scope"       # workflow 2 (overnight key)
/paper-writing "NARRATIVE_REPORT.md"  # workflow 3

# With overrides:
/research-pipeline "topic" -- AUTO_PROCEED: false, wandb: true

State Recovery (Persistence)

Each skill saves progress to JSON after every round:
  REVIEW_STATE.json
  AUTO_REVIEW.md

If session terminates mid-loop:
  Restart Claude Code → skill reads state → resumes from last checkpoint

GPU Automation

Configure in project CLAUDE.md:

SSH_HOST: your-gpu-server.com
SSH_USER: ubuntu
CONDA_ENV: research
SLURM: true  # if using SLURM cluster

Skills handle: ssh → rsync code → conda activate → run experiments → fetch results

KNOWLEDGE INJECTION: AI Maestro

Source: https://github.com/23blocks-OS/ai-maestro

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: ai-maestro-orchestration

name: ai-maestro-orchestration description: > AI Maestro - centralized dashboard for managing 35+ AI agents across machines. Peer mesh network (no central server), agent-to-agent messaging (AMP protocol), Code Graph visualization, Kanban task tracking, persistent memory, tmux auto-discovery. Gateway: Slack/Discord/Email/WhatsApp. curl install, Node.js 18+, tmux required. USE FOR:

manage many AI agents from one dashboard
agent-to-agent communication
multi-machine AI coordination
Code Graph codebase visualization
tmux session management for agents

tags: [AI-Maestro, multi-agent, dashboard, tmux, mesh-network, AMP, Code-Graph, Kanban] kind: tool category: ai-agent-builder

What Is AI Maestro?

Centralized dashboard for managing distributed AI agent workforces.

Repo: https://github.com/23blocks-OS/ai-maestro
Use case: built for devs managing 35+ agents across terminals

Install

curl -fsSL https://raw.githubusercontent.com/23blocks-OS/ai-maestro/main/scripts/remote-install.sh | sh
# Requirements: Node.js 18+, tmux

Core Features

Feature	Description
Auto-discovery	Finds existing tmux sessions automatically
Peer mesh	No central server — equal-status machines
AMP protocol	Agent-to-Agent Messaging with priority + crypto signatures
Code Graph	Interactive codebase visualization, delta indexing
Kanban	Task dependencies + status tracking
Persistent memory	Cross-session contextual continuity
War rooms	Multi-agent team assembly
Gateways	Slack, Discord, Email, WhatsApp routing

Agent Messaging Protocol (AMP)

Message types: command, query, response, broadcast
Priority: critical > high > normal > low
Signature: cryptographic for auth
Push notifications: real-time delivery

KNOWLEDGE INJECTION: Babysitter (Agent Workflow Control)

Source: https://github.com/a5c-ai/babysitter

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: babysitter-agent-control

name: babysitter-agent-control description: > Babysitter - deterministic agent workflow framework. Process-as-code (JS functions), mandatory stops between steps, enforced quality gates, event-sourced audit journal. 50-67% token compression. 2000+ pre-built process templates. 4 modes: interactive, autonomous (yolo), planning, continuous (forever monitoring). USE FOR:

deterministic agent workflow (no hallucination drift)
quality convergence loops
human-in-the-loop agent control
resume interrupted agent workflows
parallel dependent task execution
2000+ pre-built process templates

tags: [babysitter, deterministic, agent-control, quality-gates, audit-journal, workflow, Claude-Code] kind: framework category: ai-agent-builder

What Is Babysitter?

Deterministic agent workflow framework — agents can only execute what the process permits.

Repo: https://github.com/a5c-ai/babysitter
Install: via Claude Code plugin marketplace

Core Mechanisms

Process as Code  → JS functions define exactly what agents may do
Mandatory Stops  → every step halts; process logic decides next action
Enforced Gates   → quality thresholds block progression
Event Journal    → immutable audit trail, replay from any checkpoint

4 Execution Modes

/babysitter:call     # Interactive — pauses for human approval
/babysitter:yolo     # Autonomous — fully automatic
/babysitter:plan     # Planning — review before executing
/babysitter:forever  # Continuous — indefinite monitoring

Quality Convergence

// Automated refinement until threshold met
process.qualityGate({
  check: () => runTests(),
  threshold: 0.95,          // 95% pass rate required
  maxRounds: 5,             // max refinement attempts
  onFail: "refine-code"     // action if threshold not met
})

Token Compression

50-67% context reduction built-in — summarizes completed steps, retains only active context.

Run Resumption

/babysitter:observe    # live dashboard
/babysitter:resume     # continue interrupted workflow from checkpoint

Pre-Built Process Library

2,000+ templates covering:

Code review + refactoring
Test generation
Documentation
Security audits
Data pipeline validation
Content generation

KNOWLEDGE INJECTION: Skills Manager (jiweiyeah)

Source: https://github.com/jiweiyeah/Skills-Manager

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: skills-manager-desktop

name: skills-manager-desktop description: > Skills Manager - desktop app (Tauri 2.0 + React 19) for managing AI skills across multiple coding assistants. Write a skill once, sync via symlinks to Claude Code, Codex, Opencode simultaneously. Granular enable/disable per tool. Cross-platform (macOS/Windows/Linux). Monaco Editor for in-app skill editing. USE FOR:

manage Claude Code skills across multiple AI tools
sync skills via symlinks no duplication
enable/disable skills per tool
visual skill editor desktop app
cross-tool skill management

tags: [skills-manager, Claude-Code, Codex, skills-sync, symlinks, Tauri, desktop, cross-platform] kind: tool category: ai-agent-builder

What Is Skills Manager?

Centralized desktop app to manage AI assistant skills across Claude Code, Codex, Opencode, etc.

Repo: https://github.com/jiweiyeah/Skills-Manager
Stack: Tauri 2.0 (Rust) + React 19 + TypeScript + Tailwind CSS v4 + Radix UI + Monaco Editor
Install: download from Releases (.dmg / .msi / .exe / .deb / .AppImage / .rpm)

Key Features

Unified hub: one place to write, edit, and organize skills
Smart sync: symlinks prevent file duplication across tools
Granular control: enable/disable skills per AI tool independently
In-app editor: Monaco Editor for rich code/markdown editing
Auto-detect: finds installed AI tools and their skill directories automatically

Supported Tools

Claude Code: ~/.claude/skills/
Codex CLI: ~/.codex/skills/
Opencode: ~/.opencode/skills/
Custom tools: configurable paths

Windows Note

Requires Administrator privileges or Developer Mode enabled for symlink creation.

KNOWLEDGE INJECTION: Claude Code Karma

Source: https://github.com/JayantDevkar/claude-code-karma

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: claude-code-karma

name: claude-code-karma description: > Claude Code Karma - local-first analytics dashboard for Claude Code sessions. Reads ~/.claude/ JSONL files, serves via FastAPI (port 8000), displays in SvelteKit UI (port 5173). No cloud, no accounts, no telemetry. Shows token usage, costs, cache hit rates, tool/agent distribution, file operations, plugin/skill/hook inventory, live session monitoring. USE FOR:

visualize Claude Code session analytics
token usage and cost tracking
cache hit rate analysis
tool and agent usage statistics
file operation monitoring
plugin/skill/hook inventory dashboard

tags: [claude-code-karma, analytics, dashboard, token-usage, costs, sessions, FastAPI, SvelteKit] kind: tool category: ai-agent-builder

What Is Claude Code Karma?

Local analytics dashboard for your Claude Code sessions.

Repo: https://github.com/JayantDevkar/claude-code-karma
Architecture: ~/.claude/ JSONL → FastAPI (8000) → SvelteKit (5173) → SQLite index
No external calls — completely local

Install

git clone https://github.com/JayantDevkar/claude-code-karma.git
cd claude-code-karma

# Terminal 1 — backend
cd api && pip install -e ".[dev]" && pip install -r requirements.txt
uvicorn main:app --reload --port 8000

# Terminal 2 — frontend
cd frontend && npm install && npm run dev
# Access: http://localhost:5173

Dashboard Features

View	What It Shows
Session Browser	All sessions with search/filter, real-time status
Analytics	Token usage, costs, cache hit rates, tool distribution
Project Organization	Workspaces by git repo + activity tracking
Tool & Agent Tracking	Built-in + MCP tools + custom agents + usage stats
File & Task Management	File operations, task creation/completion
Live Monitoring	Real-time via Claude Code hooks (optional)
Plugin Ecosystem	Installed plugins, skills, commands, hooks

KNOWLEDGE INJECTION: Swing Skills (whynowlab)

Source: https://github.com/whynowlab/swing-skills

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: swing-trade-psychology-coach-firewall

name: swing-trade-psychology-coach-firewall description: > Swing - 6-skill AI trade-psychology-coach firewall suite. Prevents systematic AI reasoning failures: ambiguous execution, unverified claims, anchoring, sycophancy, hidden reasoning, optimism bias. Skills: swing-clarify (5W1H), swing-research (4-stage verification), swing-options (5 alternatives), swing-review (steel-man + 3-vector critique), swing-trace (assumption mapping), swing-mortem (5-category failure projection). npx skills add whynowlab/swing-skills --all. USE FOR:

prevent AI hallucination and overconfidence
5W1H request decomposition before execution
source-tiered research with cross-validation
generate 5 probability-weighted alternatives
steel-man critique of decisions
assumption mapping and confidence analysis
pre-mortem failure projection

tags: [swing, trade-psychology-coach-firewall, clarify, research, options, review, trace, mortem, reasoning, Claude-Code] kind: skills-suite category: ai-agent-builder

What Is Swing?

6-skill trade-psychology-coach firewall that addresses systematic AI reasoning failures.

Repo: https://github.com/whynowlab/swing-skills
Compatible: Claude Code (primary), Cursor, GitHub Copilot, Codex CLI

Install

npx skills add whynowlab/swing-skills --all   # all 6 skills

# Individual:
npx skills add whynowlab/swing-skills/swing-clarify
npx skills add whynowlab/swing-skills/swing-research

# Manual:
cp -r swing-skills/skills/* ~/.claude/skills/

The 6 Skills

Skill	Problem Solved	Method
swing-clarify	Ambiguous requests rushed	5W1H decomposition
swing-research	Unverified claims	4-stage verification, S/A/B/C source grading
swing-options	Anchoring on obvious answer	5 probability-weighted alternatives
swing-review	Sycophancy / no critique	Steel-man then 3-vector critical analysis
swing-trace	Hidden reasoning	Assumption map, decision forks, weakest-link
swing-mortem	Optimism bias	5-category failure projection + leading indicators

Usage

/swing-clarify Build me an auth system
/swing-research Is gRPC better than REST for mobile?
/swing-review We chose Kubernetes for our 3-person startup
/swing-options Which database for real-time leaderboard?
/swing-trace Why do you recommend microservices?
/swing-mortem We are migrating to microservices in Q3

Recommended Chain

clarify -> (research decision) -> options -> research -> review
        -> (risk analysis)     -> mortem

KNOWLEDGE INJECTION: Spec-Flow (echoVic)

Source: https://github.com/echoVic/spec-flow

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: spec-flow

name: spec-flow description: > Spec-Flow - structured feature development workflow for AI coding agents. 5 sequential phases with approval gates: Proposal -> Requirements (EARS syntax) -> Design -> Tasks -> Implementation. Generates living markdown docs in .spec-flow/ directory. 3 modes: Step, Batch, Phase. Triggers: "spec-flow", "spec mode", "need a plan", "structured development". Install: git clone into ~/.claude/skills. USE FOR:

structured feature development with AI
requirements specification EARS syntax
phase-gated AI coding workflow
living documentation .spec-flow directory
proposal -> requirements -> design -> tasks -> implementation

tags: [spec-flow, structured-development, requirements, EARS, phases, AI-workflow, Claude-Code] kind: skill category: ai-agent-builder

What Is Spec-Flow?

Phase-gated structured development workflow for AI coding agents.

Repo: https://github.com/echoVic/spec-flow
Install: cd ~/.claude/skills && git clone https://github.com/echoVic/spec-flow.git
Trigger: "spec-flow", "spec mode", "need a plan", or Chinese: "写个方案"

5 Phases (with human approval gates)

1. Proposal       → overview, scope, success criteria
2. Requirements   → EARS syntax specs (SHALL/WHEN/WHERE/IF clauses)
3. Design         → architecture, components, data models, interfaces
4. Tasks          → numbered implementation checklist, dependencies
5. Implementation → execute tasks one by one, verify each

Execution Modes

# Default (step-by-step, confirmation at each phase)
spec-flow: Build user authentication system

# Fast (skip confirmations)
spec-flow --fast: Add dark mode toggle

# Simple (skip design phase)
spec-flow --skip-design: Fix the login bug

Documentation Structure

.spec-flow/
  steering/     # project context, conventions
  active/       # in-progress feature docs
    feature-name/
      proposal.md
      requirements.md
      design.md
      tasks.md
  archive/      # completed features

EARS Requirements Format

WHEN user submits login form
IF credentials are valid
THE SYSTEM SHALL authenticate the user and redirect to dashboard

WHERE the user is not authenticated
THE SYSTEM SHALL redirect to login page

KNOWLEDGE INJECTION: HAM — Hierarchical Agent Memory

Source: https://github.com/kromahlusenii-ops/ham

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: ham-hierarchical-memory

name: ham-hierarchical-memory description: > HAM (Hierarchical Agent Memory) - reduces Claude Code token consumption 50% by replacing one massive CLAUDE.md with small scoped CLAUDE.md files at each directory level. Agent loads only 2-3 relevant files per session. Auto-detects project stack, self-maintaining (updates decision/pattern files as work progresses). Analytics dashboard at localhost:7777. Install: git clone into ~/.claude/skills/ham. USE FOR:

reduce Claude Code token usage by 50%
hierarchical scoped CLAUDE.md files
context reduction hundreds vs thousands of tokens
self-maintaining memory decision files
ham dashboard analytics token savings
benchmark baseline vs HAM performance

tags: [HAM, hierarchical-memory, token-reduction, CLAUDE.md, context-management, Claude-Code, analytics] kind: skill category: ai-agent-builder

What Is HAM?

Hierarchical Agent Memory — cuts Claude Code starting context from thousands of tokens to hundreds.

Repo: https://github.com/kromahlusenii-ops/ham
Install: git clone https://github.com/kromahlusenii-ops/ham.git ~/.claude/skills/ham
Token reduction: up to 50%

Core Concept

Before HAM:  One massive CLAUDE.md → loads entire project context every session
After HAM:   Small CLAUDE.md at each directory level → loads only 2-3 relevant files

Commands

# Setup
go ham              # auto-configures everything
ham update          # refresh memory files
ham status          # show current memory state
ham route           # show which files will load for current directory

# Analytics (web UI at localhost:7777)
ham dashboard       # open analytics dashboard
ham savings         # show token savings report
ham carbon          # environmental impact calculator

# Benchmarking
ham benchmark       # run benchmark vs baseline
ham baseline start  # start baseline measurement
ham baseline stop   # stop baseline, compute metrics

# Maintenance
ham audit           # check for stale/missing memory files
ham commands        # list all available commands

Memory Structure

project/
  CLAUDE.md              # global conventions, project overview
  src/
    CLAUDE.md            # frontend/backend patterns
    components/
      CLAUDE.md          # component-specific conventions
    api/
      CLAUDE.md          # API patterns, auth flows
  tests/
    CLAUDE.md            # testing patterns, fixtures

Self-Maintaining Cycle

Read:  agent reads directory CLAUDE.md before starting work
Write: agent updates CLAUDE.md after completing work
       (new patterns, decisions, gotchas discovered)

KNOWLEDGE INJECTION: geo-lint (IJONIS)

Source: https://github.com/IJONIS/geo-lint

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: geo-lint

name: geo-lint description: > geo-lint - first open-source linter for GEO (Generative Engine Optimization). 97 rules across 5 categories: GEO (AI citation readiness, E-E-A-T, RAG optimization), SEO (metadata/schema/keywords), content quality, technical (broken links/perf), i18n. Agent-first JSON output for auto-fix loops. Ensures content gets cited by ChatGPT, Perplexity, Google AI Overviews, Gemini. npm install -D @ijonis/geo-lint. Claude Code skill: /geo-lint audit. USE FOR:

optimize content for AI citation (GEO)
lint markdown for AI search visibility
automated fix loop until zero violations
E-E-A-T signal validation
RAG optimization for content
SEO + GEO combined content audit

tags: [geo-lint, GEO, SEO, AI-citation, content-optimization, RAG, E-E-A-T, Claude-Code, linter] kind: tool category: ai-agent-builder

What Is geo-lint?

First open-source linter for Generative Engine Optimization — ensures content gets cited by AI search engines.

Repo: https://github.com/IJONIS/geo-lint
Install: npm install -D @ijonis/geo-lint
Node.js 18+ required, zero peer dependencies

Install

npm install -D @ijonis/geo-lint

# Claude Code users:
curl -fsSL https://raw.githubusercontent.com/IJONIS/geo-lint/main/install.sh | bash

Commands

npx geo-lint                  # human-readable output
npx geo-lint --format=json    # machine-readable for AI agents
/geo-lint audit               # Claude Code skill: full site scan + fix
/content-creator setup        # create SEO/GEO-optimized content

97 Rules Across 5 Categories

Category	Rules	Focus
GEO	36	AI citation readiness, E-E-A-T signals, RAG optimization
SEO	34	Metadata, schema markup, keywords
Content Quality	14	Readability, word count, jargon
Technical	10	Broken links, performance
i18n	3	Translation pairs

Agent-First Fix Loop

geo-lint --format=json → violations with fix suggestions
→ AI agent modifies content
→ geo-lint re-runs
→ repeat until violations == 0

Each violation includes: file location, rule name, severity, plain-language fix instruction, machine-readable fix pattern.

Supported Formats

Markdown / MDX (native)
Extensible: Astro, HTML, CMS platforms

KNOWLEDGE INJECTION: Awesome Claude Code (hesreallyhim)

Source: https://github.com/hesreallyhim/awesome-claude-code

Routed to: claude-ai-tools.md

Date: 2026-03-18

Awesome Claude Code — 400+ Resource Reference

Curated index of 400+ Claude Code extensions, skills, tools, hooks, and workflows.

Agent Skills (Highlights)

Trail of Bits Security Skills — 12+ skills for code auditing + vulnerability detection
Everything Claude Code — Wide-ranging engineering domain skills
Fullstack Dev Skills — 65 skills across full-stack frameworks + Jira
Claude Scientific Skills — Research, engineering, finance, writing
Superpowers — SDLC planning through debugging competencies
TACHES Resources — Sub agents, skills, meta-skills, workflow adaptation
AgentSys — Task-to-production, PR management, multi-agent review
Compound Engineering Plugin — Error-to-learning discipline agents

Orchestrators

Claude Swarm — Multi-agent session orchestration
Claude Squad — Terminal app, multiple agents in separate workspaces
Claude Task Master — Task management for AI-driven development
Auto-Claude — Multi-agent kanban UI for full SDLC automation
Claude Code Flow — Code-first orchestration for autonomous agent cycles
TSK — Rust CLI delegating to sandboxed Docker agents
Happy Coder — Multi-instance control from phone/desktop

Key Tools

claudekit — CLI toolkit: auto-save, quality hooks, 20+ subagents
SuperClaude — Configuration framework with commands and personas
cchistory — Session command history like shell history
cclogviewer — HTML UI for JSONL conversation files
recall — Full-text session search with terminal interface
Container Use — Safe multi-agent isolation environments
Rulesync — Auto-generates configs for Claude Code, Cursor, others
claude-code-karma — Analytics dashboard for sessions/tokens/costs (see separate entry)
Vibe-Log — Local prompt analysis with session analytics

Hooks (Highlights)

parry — Prompt injection scanner detecting attacks/exfiltration
TDD Guard — Blocks TDD-violating changes in real-time
Dippy — Auto-approve safe bash, prompt for destructive operations
CC Notify — Desktop notifications for input needs + task completion
cchooks — Lightweight Python SDK with clean API

Status Lines

claude-code-statusline — 4-line statusline with themes and cost tracking
ccstatusline — Customizable: model, branch, token usage
claude-powerline — Vim-style powerline with real-time tracking
CCometixLine — High-performance Rust statusline with git integration

Key Slash Commands

Command	Purpose
/commit	Conventional commit with emojis
/create-pr	PR creation workflow
/tdd	TDD enforcement
/check	Code quality + security checks
/context-prime	Comprehensive project priming
/prd-generator	Product Requirements Document generation
/optimize	Performance bottleneck identification
/mermaid	ER diagram generation from SQL

Workflow Systems (Highlights)

RIPER Workflow — Research, Innovate, Plan, Execute, Review phases
AB Method — Spec-driven, large problems into focused missions
Ralph Wiggum Techniques — Autonomous loop with exit detection + safety
Claude Code PM — Project management with specialized agents + commands
Simone — Broader PM with documents, guidelines, processes
Spec-Flow — Phase-gated structured development (see separate entry)

Usage Monitors

Claude Code Usage Monitor — Real-time terminal with burn rate predictions
CC Usage — Dashboard for cost + token analysis
ccflare — Web UI usage dashboard with comprehensive metrics

Full index: https://github.com/hesreallyhim/awesome-claude-code

KNOWLEDGE INJECTION: Refly (refly-ai)

Source: https://github.com/refly-ai/refly

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: refly-agent-skill-builder

name: refly-agent-skill-builder description: > Refly - open-source platform transforming enterprise workflows into versioned agent skills. Copilot-led DSL compiler: describe workflow in natural language, compiles in <3 minutes. Intervenable runtime (pause/audit/redirect mid-execution). Exports to Claude Code, Cursor, MCP. 3000+ native integrations: Stripe, Slack, Salesforce, GitHub, MCP servers. Central skill registry, version-controlled, team-shareable. USE FOR:

codify business workflows as agent skills
natural language to agent skill compiler
intervenable runtime pause audit redirect
deploy skills as API webhook or Claude Code tool
3000+ tool integrations for agent skills

tags: [Refly, agent-skills, enterprise, workflow, DSL, MCP, Claude-Code, versioned, registry] kind: platform category: ai-agent-builder

What Is Refly?

Open-source platform for building versioned enterprise agent skills.

Repo: https://github.com/refly-ai/refly
Install: npm install -g @powerformer/refly-cli

Architecture

Input Layer:     3000+ tools, MCP servers, private connectors
Processing Layer: Vibe-driven DSL compiler + stateful intervenable runtime
Output Layer:    Claude Code, Cursor, APIs, webhooks, agent frameworks

Usage

npm install -g @powerformer/refly-cli

# Install a skill from registry
refly skill install <skill-id>

# Publish your skill
refly skill publish <skill-id>

# Execute workflow via API
curl -X POST https://instance/api/v1/workflows/{WORKFLOW_ID}/execute \
  -H "Authorization: Bearer API_KEY"

Key Differentiators

Under 3 minutes from natural language description to deployed skill
Intervenable: pause, audit, redirect agent logic during execution (compliance-friendly)
Universal export: skills become APIs, webhooks, or native Claude Code/Cursor tools
Central registry: version-controlled skills shareable across teams

KNOWLEDGE INJECTION: prompts.chat (f/prompts.chat)

Source: https://github.com/f/prompts.chat

Routed to: claude-ai-tools.md

Date: 2026-03-18

prompts.chat — World Largest Open-Source Prompt Library

143,000+ GitHub stars. Works with ChatGPT, Claude, Gemini, Llama, Mistral. Originally launched as Awesome ChatGPT Prompts (Dec 2022).

Access

# CLI
npx prompts.chat

# Claude Code plugin
/plugin install prompts.chat

# MCP server
# add to MCP config

# Browse online: https://prompts.chat/prompts
# CSV/Markdown/Hugging Face dataset available

Key Facts

143,000+ GitHub stars
40+ academic citations
Most liked dataset on Hugging Face
CC0 1.0 license (public domain, unrestricted use)
Featured by Forbes, Harvard, Columbia
Self-hosting option available

Prompt Engineering Guide

Free interactive guide with 25+ chapters on techniques:

Zero-shot, few-shot, chain-of-thought
Role prompting, output formatting
Task decomposition, context injection

Full prompt browser: https://prompts.chat/prompts

KNOWLEDGE INJECTION: LobeHub

Source: https://github.com/lobehub/lobehub

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: lobehub-platform

name: lobehub-platform description: > LobeHub - open-source AI platform for human-agent co-evolution. Web app + desktop + PWA. Multi-model (OpenAI, Ollama, 100+ providers), 10,000+ MCP plugins marketplace, agent builder, agent groups, personal memory, real-time internet search, chain-of-thought visualization, voice TTS/STT, image generation, knowledge base. Self-host via Docker or Vercel one-click. USE FOR:

self-hosted multi-model AI platform
agent builder with customizable skills
MCP marketplace 10000+ plugins
team agent groups collaboration
personal memory adaptive learning
chain-of-thought visualization

tags: [LobeHub, LobeChat, multi-model, self-hosted, agents, MCP, Ollama, GPT, Claude, voice] kind: platform category: ai-agent-builder

What Is LobeHub?

Open-source AI platform — web app, desktop, PWA with 10,000+ MCP plugins.

Repo: https://github.com/lobehub/lobehub
Main product: LobeChat — https://github.com/lobehub/lobe-chat

Quick Deploy

# Docker
docker run -d -p 3210:3210 lobehub/lobe-chat

# Vercel one-click (or Zeabur, Sealos, Alibaba Cloud)
# Set OPENAI_API_KEY or other provider credentials

Key Features

Feature	Details
Multi-model	OpenAI, Ollama (local), Anthropic, Gemini, 100+ providers
MCP Marketplace	10,000+ compatible plugins
Agent Builder	Custom agents with tools, memory, persona
Agent Groups	Team-based agent collaboration
Personal Memory	Adaptive learning across sessions
Internet Search	Real-time web integration
Chain of Thought	Visualize AI reasoning steps
Voice	TTS/STT conversational interface
Image Gen	Generation + visual recognition
Knowledge Base	File uploads, RAG

KNOWLEDGE INJECTION: claude-mem (thedotmack)

Source: https://github.com/thedotmack/claude-mem

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: claude-mem

name: claude-mem description: > claude-mem - persistent memory compression system for Claude Code. Auto-captures tool usage, generates semantic summaries, persists across sessions. Progressive disclosure (layered retrieval with token costs). mem-search skill for natural language history queries. 4 MCP tools: search, timeline, get_observations. Web viewer at localhost:37777. Private tag for sensitive content. Install via /plugin marketplace add thedotmack/claude-mem. USE FOR:

persistent Claude Code memory across sessions
semantic session history search
context compression and retrieval
web UI for memory visualization
MCP tools for memory queries

tags: [claude-mem, persistent-memory, Claude-Code, session-continuity, compression, MCP, search] kind: tool category: ai-agent-builder

What Is claude-mem?

Persistent memory compression system — context survives Claude Code session restarts.

Repo: https://github.com/thedotmack/claude-mem
Web UI: http://localhost:37777

Install

# Via Claude Code Plugin Marketplace (recommended)
/plugin marketplace add thedotmack/claude-mem
/plugin install claude-mem
# Restart Claude Code

# Via OpenClaw Gateway
curl -fsSL https://install.cmem.ai/openclaw.sh | bash

Features

Automatic: captures tool usage, generates semantic summaries with no manual action
Progressive Disclosure: layered retrieval showing token costs per memory level
mem-search skill: natural language queries into project history
Privacy: wrap content in <private> tags to exclude from memory
Citations: reference past observations by ID

MCP Tools

Tool	Purpose
search	Query memory index by natural language
timeline	Chronological context view
get_observations	Fetch full details by observation ID

Web Viewer (localhost:37777)

Real-time memory stream visualization + settings management.

KNOWLEDGE INJECTION: Continue (continuedev)

Source: https://github.com/continuedev/continue

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: continue-ai-code-review

name: continue-ai-code-review description: > Continue - open-source AI-powered code review platform running agents on every PR as GitHub status checks. Review rules stored as markdown in .continue/checks/ directories (source-controlled). Returns green pass or red with suggested diffs. VS Code extension available. CLI via npm @continuedev/cli (cn command). macOS/Linux/Windows install. Custom checks for security, best practices, team conventions. USE FOR:

AI code review on every pull request
source-controlled review rules markdown
GitHub status checks from AI agents
custom security and best practice checks
automated PR quality enforcement

tags: [Continue, AI-code-review, PR, GitHub-status-checks, CI, VS-Code, open-source] kind: tool category: ai-agent-builder

What Is Continue?

Open-source AI code review platform — agents run on every PR as GitHub status checks.

Repo: https://github.com/continuedev/continue
CLI command: cn

Install

# macOS/Linux
curl -fsSL https://install.continue.dev | bash

# Windows (PowerShell)
iwr https://install.continue.dev/windows -useb | iex

# Node.js alternative (Node 20+)
npm install -g @continuedev/cli

How It Works

1. Store review rules as markdown in .continue/checks/
2. Continue runs agents on every PR
3. Returns GitHub status check: green (pass) or red (with suggested diffs)
4. Teams enforce rules as required status checks

Review Rule Structure

.continue/
  checks/
    security.md       # security vulnerability rules
    best-practices.md # team coding standards
    custom-check.md   # any custom requirement

VS Code Extension

Available through VS Code marketplace — provides IDE-level AI assistance integrated with the same agent framework.

KNOWLEDGE INJECTION: memU (NevaMind-AI)

Source: https://github.com/NevaMind-AI/memU

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: memu-proactive-memory

name: memu-proactive-memory description: > memU - 24/7 always-on proactive memory framework for AI agents. Continuously captures user intent without explicit commands. Hierarchical 3-layer memory (Resource/Item/Category). Dual pathways: memorize() continuous learning + retrieve() RAG fast or LLM deep reasoning. 92.09% accuracy on benchmarks. Reduces LLM token costs via cached insights. Storage: in-memory or PostgreSQL (pgvector). pip install -e . Python 3.13+. USE FOR:

24/7 always-on agent memory
proactive user intent capture
hierarchical memory for AI agents
reduce LLM token costs via memory cache
RAG-based fast context retrieval
persistent agent memory PostgreSQL pgvector

tags: [memU, proactive-memory, AI-ai-agents, hierarchical-memory, RAG, pgvector, token-reduction, 24-7] kind: framework category: ai-agent-builder

What Is memU?

24/7 proactive memory framework — AI agents learn continuously without explicit prompts.

Repo: https://github.com/NevaMind-AI/memU
Cloud: memu.so
Python 3.13+ | OpenAI API key required

Install

pip install -e .
export OPENAI_API_KEY=your_api_key

Architecture

3-Layer Hierarchical Memory:
  Resource   → mountable data sources (docs, sessions, external feeds)
  Item       → individual memory facts with cross-references
  Category   → auto-organized topic clusters

Dual Retrieval Pathways:
  memorize() → continuous learning pipeline (immediate memory update)
  retrieve() → RAG fast (sub-second embedding) OR LLM deep (complex anticipation)

Storage Options

# In-memory (default)
python tests/test_inmemory.py

# PostgreSQL with pgvector (persistent)
docker run -d --name memu-postgres \
  -e POSTGRES_PASSWORD=postgres \
  -p 5432:5432 pgvector/pgvector:pg16
python tests/test_postgres.py

Examples

python examples/example_1_conversation_memory.py  # session persistence
python examples/example_2_skill_extraction.py     # learn user skills
python examples/example_3_multimodal_memory.py    # images + text

Performance

92.09% average accuracy on benchmark reasoning tasks
Sub-second RAG retrieval
Reduces redundant LLM calls via cached intent insights

KNOWLEDGE INJECTION: LangChain

Source: https://github.com/langchain-ai/langchain

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: langchain-framework

name: langchain-framework description: > LangChain - leading framework for building LLM-powered applications and agents. Core: chains (sequential LLM pipelines), agents (tool-using reasoning loops), memory (conversation + long-term), RAG (retrieval-augmented generation), 100+ integrations (OpenAI, Anthropic, HuggingFace, Pinecone, Chroma, etc.). Model-agnostic: swap providers without code changes. Ecosystem: LangGraph (orchestration), LangSmith (debug/deploy). pip install langchain. USE FOR:

build LLM application chains
tool-using agent with ReAct or function calling
RAG pipeline retrieval augmented generation
conversation memory management
swap LLM providers without changing code
integrate with vector stores Pinecone Chroma FAISS

tags: [LangChain, LLM, agents, chains, RAG, memory, tools, OpenAI, Anthropic, LangGraph, LangSmith] kind: framework category: ai-agent-builder

What Is LangChain?

Framework for building LLM-powered applications — agents, chains, RAG, memory, tools.

Repo: https://github.com/langchain-ai/langchain
Stars: 100k+
Ecosystem: LangChain + LangGraph + LangSmith

Install

pip install langchain
pip install langchain-openai        # OpenAI provider
pip install langchain-anthropic     # Anthropic/Claude
pip install langchain-community     # community integrations
# or
uv add langchain

Quick Start

from langchain.chat_models import init_chat_model

model = init_chat_model("openai:gpt-4o")
result = model.invoke("Explain quantum computing in one sentence")

# Or Claude:
model = init_chat_model("anthropic:claude-sonnet-4-6")

Core Components

Chains — Sequential LLM pipelines:

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template("Translate to French: {text}")
chain = prompt | ChatOpenAI(model="gpt-4o")
result = chain.invoke({"text": "Hello world"})

Agents — Tool-using reasoning loops:

from langchain.agents import create_react_agent, AgentExecutor
from langchain_community.tools import DuckDuckGoSearchRun

tools = [DuckDuckGoSearchRun()]
agent = create_react_agent(llm, tools, prompt)
executor