Hermes Agent ☤

The self-improving AI agent built by Nous Research. It's the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions. Run it on a $5 VPS, a GPU cluster, or serverless infrastructure that costs nearly nothing when idle. It's not tied to your laptop — talk to it from Telegram while it works on a cloud VM.

Use any model you want — Nous Portal, OpenRouter (200+ models), z.ai/GLM, Kimi/Moonshot, MiniMax, OpenAI, or your own endpoint. Switch with hermes model — no code changes, no lock-in.

Quick Install

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

Works on Linux, macOS, WSL2, and Android via Termux. The installer handles the platform-specific setup for you.

Android / Termux: The tested manual path is documented in the Termux guide. On Termux, Hermes installs a curated .[termux] extra because the full .[all] extra currently pulls Android-incompatible voice dependencies.

Windows: Native Windows is not supported. Please install WSL2 and run the command above.

After installation:

source ~/.bashrc    # reload shell (or: source ~/.zshrc)
hermes              # start chatting!

Getting Started

hermes              # Interactive CLI — start a conversation
hermes model        # Choose your LLM provider and model
hermes tools        # Configure which tools are enabled
hermes config set   # Set individual config values
hermes gateway      # Start the messaging gateway (Telegram, Discord, etc.)
hermes setup        # Run the full setup wizard (configures everything at once)
hermes claw migrate # Migrate from OpenClaw (if coming from OpenClaw)
hermes update       # Update to the latest version
hermes doctor       # Diagnose any issues

📖 Full documentation →

CLI vs Messaging Quick Reference

Hermes has two entry points: start the terminal UI with hermes, or run the gateway and talk to it from Telegram, Discord, Slack, WhatsApp, Signal, or Email. Once you're in a conversation, many slash commands are shared across both interfaces.

For the full command lists, see the CLI guide and the Messaging Gateway guide.

Documentation

All documentation lives at hermes-agent.nousresearch.com/docs:

(Preview — first 8 000 chars. View full README ↗)

Karpathy-Inspired Claude Code Guidelines

A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.

The Problems

From Andrej's post:

"The models make wrong assumptions on your behalf and just run along with them without checking. They don't manage their confusion, don't seek clarifications, don't surface inconsistencies, don't present tradeoffs, don't push back when they should."

"They really like to overcomplicate code and APIs, bloat abstractions, don't clean up dead code... implement a bloated construction over 1000 lines when 100 would do."

"They still sometimes change/remove comments and code they don't sufficiently understand as side effects, even if orthogonal to the task."

The Solution

Four principles in one file that directly address these issues:

The Four Principles in Detail

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs.

LLMs often pick an interpretation silently and run with it. This principle forces explicit reasoning:

• State assumptions explicitly — If uncertain, ask rather than guess
• Present multiple interpretations — Don't pick silently when ambiguity exists
• Push back when warranted — If a simpler approach exists, say so
• Stop when confused — Name what's unclear and ask for clarification

2. Simplicity First

Minimum code that solves the problem. Nothing speculative.

Combat the tendency toward overengineering:

• No features beyond what was asked
• No abstractions for single-use code
• No "flexibility" or "configurability" that wasn't requested
• No error handling for impossible scenarios
• If 200 lines could be 50, rewrite it

The test: Would a senior engineer say this is overcomplicated? If yes, simplify.

3. Surgical Changes

Touch only what you must. Clean up only your own mess.

When editing existing code:

• Don't "improve" adjacent code, comments, or formatting
• Don't refactor things that aren't broken
• Match existing style, even if you'd do it differently
• If you notice unrelated dead code, mention it — don't delete it

When your changes create orphans:

• Remove imports/variables/functions that YOUR changes made unused
• Don't remove pre-existing dead code unless asked

The test: Every changed line should trace directly to the user's request.

4. Goal-Driven Execution

Define success criteria. Loop until verified.

Transform imperative tasks into verifiable goals:

For multi-step tasks, state a brief plan:

1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]

Strong success criteria let the LLM loop independently. Weak criteria ("make it work") require constant clarification.

Install

Option A: Claude Code Plugin (recommended)

From within Claude Code, first add the marketplace:

/plugin marketplace add forrestchang/andrej-karpathy-skills

Then install the plugin:

/plugin install andrej-karpathy-skills@karpathy-skills

This installs the guidelines as a Claude Code plugin, making the skill available across all your projects.

Option B: CLAUDE.md (per-project)

New project:

curl -o CLAUDE.md https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md

Existing project (append):

echo "" >> CLAUDE.md
curl https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md >> CLAUDE.md

Key Insight

From Andrej:

"LLMs are exceptionally good at looping until they meet specific goals... Don't tell it what to do, give it success criteria and watch it go."

The "Goal-Driven Execution" principle captures this: transform imperative instructions into declarative goals with verification loops.

How to Know It's Working

These guidelines are working if you see:

• Fewer unnecessary changes in diffs — Only requested changes appear
• Fewer rewrites due to overcomplication — Code is simple the first time
• Clarifying questions come before implementation — Not after mistakes
• Clean, minimal PRs — No drive-by refactoring or "improvements"

Customization

These guidelines are designed to be merged with project-specific instructions. Add them to your existing CLAUDE.md or create a new one.

For project-specific rules, add sections like:

## Project-Specific Guidelines

- Use TypeScript strict mode
- All API endpoints must have tests
- Follow the existing error handling patterns in `src/utils/errors.ts`

Tradeoff Note

These guidelines bias toward caution over speed. For trivial tasks (simple typo fixes, obvious one-liners), use judgment — not every change needs the full rigor.

The goal is reducing costly mistakes on non-trivial work, not slowing down simple tasks.

License

MIT

(Preview — first 8 000 chars. View full README ↗)

# DeepTutor: Agent-Native Personalized Tutoring [](https://www.python.org/downloads/) [](https://nextjs.org/) [](LICENSE) [](https://github.com/HKUDS/DeepTutor/releases) [](#) [](https://discord.gg/eRsjPgMU4t) [](./Communication.md) [](https://github.com/HKUDS/DeepTutor/issues/78) [Features](#-key-features) · [Get Started](#-get-started) · [Explore](#-explore-deeptutor) · [TutorBot](#-tutorbot--persistent-autonomous-ai-tutors) · [CLI](#%EF%B8%8F-deeptutor-cli--agent-native-interface) · [Community](#-community--ecosystem) [🇨🇳 中文](assets/README/README_CN.md) · [🇯🇵 日本語](assets/README/README_JA.md) · [🇪🇸 Español](assets/README/README_ES.md) · [🇫🇷 Français](assets/README/README_FR.md) · [🇸🇦 العربية](assets/README/README_AR.md) · [🇷🇺 Русский](assets/README/README_RU.md) · [🇮🇳 हिन्दी](assets/README/README_HI.md) · [🇵🇹 Português](assets/README/README_PT.md)

📰 News

[2026.4.4] Long time no see! ✨ DeepTutor v1.0.0 is finally here — an agent-native evolution featuring a ground-up architecture rewrite, TutorBot, and flexible mode switching under the Apache-2.0 license. A new chapter begins, and our story continues!

[2026.2.6] 🚀 We've reached 10k stars in just 39 days! A huge thank you to our incredible community for the support!

[2026.1.1] Happy New Year! Join our Discord, WeChat, or Discussions — let's shape the future of DeepTutor together!

[2025.12.29] DeepTutor is officially released!

📦 Releases

[2026.4.10] v1.0.0-beta.4 — Embedding progress tracking with HTTP 429 rate limit retry, cross-platform start tour dependency management, and case-insensitive MIME validation fix.

[2026.4.8] v1.0.0-beta.3 — Remove litellm dependency with native OpenAI/Anthropic SDK providers, Windows Math Animator compatibility, robust JSON parsing for LLM outputs, Guided Learning KaTeX & navigation fixes, and full i18n coverage for Chinses.

[2026.4.7] v1.0.0-beta.2 — Runtime cache invalidation for hot settings reload, MinerU nested output support, mimic WebSocket fix, Python 3.11+ minimum, and CI improvements.

[2026.4.4] v1.0.0-beta.1 — Agent-native architecture rewrite (DeepTutor 2.0) with two-layer plugin model (Tools + Capabilities), CLI & SDK entry points, TutorBot multi-channel bot agent, Co-Writer, Guided Learning, and persistent memory.

✨ Key Features

• Unified Chat Workspace — Five modes, one thread. Chat, Deep Solve, Quiz Generation, Deep Research, and Math Animator share the same context — start a conversation, escalate to multi-agent problem solving, generate quizzes, then deep-dive into research, all without losing a single message.
• Personal TutorBots — Not chatbots — autonomous tutors. Each TutorBot lives in its own workspace with its own memory, personality, and skill set. They set reminders, learn new abilities, and evolve as you grow. Powered by nanobot.
• AI Co-Writer — A Markdown editor where AI is a first-class collaborator. Select text, rewrite, expand, or summarize — drawing from your knowledge base and the web. Every piece feeds back into your learning ecosystem.
• Guided Learning — Turn your materials into structured, visual learning journeys. DeepTutor designs multi-step plans, generates interactive pages for each knowledge point, and lets you discuss alongside each step.
• Knowledge Hub — Upload PDFs, Markdown, and text files to build RAG-ready knowledge bases. Organize insights across sessions in color-coded notebooks. Your documents don't just sit there — they actively power every conversation.
• Persistent Memory — DeepTutor builds a living profile of you: what you've studied, how you learn, and where you're heading. Shared across all features and TutorBots, it gets sharper with every interaction.
• Agent-Native CLI — Every capability, knowledge base, session, and TutorBot is one command away. Rich terminal output for humans, structured JSON for AI agents and pipelines. Hand DeepTutor a SKILL.md and your agents can operate it autonomously.

🚀 Get Started

Option A — Setup Tour (Recommended)

A single interactive script that walks you through everything: dependency installation, environment configuration, live connection testing, and launch. No manual .env editing needed.

git clone https://github.com/HKUDS/DeepTutor.git
cd DeepTutor

# Create a Python environment
conda create -n deeptutor python=3.11 && conda activate deeptutor
# Or: python -m venv .venv && source .venv/bin/activate

# Launch the guided tour
python scripts/start_tour.py

The tour asks how you'd like to use DeepTutor:

• Web mode (recommended) — Picks a dependency profile, installs everything (pip + npm), then spins up a temporary server and opens the Settings page in your browser. A four-step guided tour walks you through LLM, Embedding, and Search provider setup with live connection testing. Once complete, DeepTutor restarts automatically with your configuration.
• CLI mode — A fully interactive terminal flow: choose a dependency profile, install dependencies, configure providers, verify connections, and apply — all without leaving the s

(Preview — first 8 000 chars. View full README ↗)

VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning

English | 中文

👋 Join our community for discussion and support!
Feishu  |  Discord

VoxCPM is a tokenizer-free Text-to-Speech system that directly generates continuous speech representations via an end-to-end diffusion autoregressive architecture, bypassing discrete tokenization to achieve highly natural and expressive synthesis.

VoxCPM2 is the latest major release — a 2B parameter model trained on over 2 million hours of multilingual speech data, now supporting 30 languages, Voice Design, Controllable Voice Cloning, and 48kHz studio-quality audio output. Built on a MiniCPM-4 backbone.

✨ Highlights

• 🌍 30-Language Multilingual — Input text in any of the 30 supported languages and synthesize directly, no language tag needed
• 🎨 Voice Design — Create a brand-new voice from a natural-language description alone (gender, age, tone, emotion, pace …), no reference audio required
• 🎛️ Controllable Cloning — Clone any voice from a short reference clip, with optional style guidance to steer emotion, pace, and expression while preserving the original timbre
• 🎙️ Ultimate Cloning — Reproduce every vocal nuance: provide both reference audio and its transcript, and the model continues seamlessly from the reference, faithfully preserving every vocal detail — timbre, rhythm, emotion, and style (same as VoxCPM1.5)
• 🔊 48kHz High-Quality Audio — Accepts 16kHz reference audio and directly outputs 48kHz studio-quality audio via AudioVAE V2's asymmetric encode/decode design, with built-in super-resolution — no external upsampler needed
• 🧠 Context-Aware Synthesis — Automatically infers appropriate prosody and expressiveness from text content
• ⚡ Real-Time Streaming — RTF as low as ~0.3 on NVIDIA RTX 4090, and ~0.13 accelerated by Nano-VLLM
• 📜 Fully Open-Source & Commercial-Ready — Weights and code released under the Apache-2.0 license, free for commercial use

Arabic, Burmese, Chinese, Danish, Dutch, English, Finnish, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Norwegian, Polish, Portuguese, Russian, Spanish, Swahili, Swedish, Tagalog, Thai, Turkish, Vietnamese

Chinese Dialect: 四川话, 粤语, 吴语, 东北话, 河南话, 陕西话, 山东话, 天津话, 闽南话

News

• [2026.04] 🔥 We release VoxCPM2 — 2B, 30 languages, Voice Design & Controllable Voice Cloning, 48kHz audio output! Weights | Docs | Playground
• [2025.12] 🎉 Open-source VoxCPM1.5 weights with SFT & LoRA fine-tuning. (🏆 #1 GitHub Trending)
• [2025.09] 🔥 Release VoxCPM Technical Report.
• [2025.09] 🎉 Open-source VoxCPM-0.5B weights (🏆 #1 HuggingFace Trending)

Contents

• Quick Start
• Installation
• Python API
• CLI Usage
• Web Demo
• Production Deployment
• Models & Versions
• Performance
• Fine-tuning
• Documentation
• Ecosystem & Community
• Risks and Limitations
• Citation

🚀 Quick Start

Installation

pip install voxcpm

Requirements: Python ≥ 3.10 (<3.13), PyTorch ≥ 2.5.0, CUDA ≥ 12.0. See Quick Start Docs for details.

Python API

🗣️ Text-to-Speech

from voxcpm import VoxCPM
import soundfile as sf

model = VoxCPM.from_pretrained(
  "openbmb/VoxCPM2",
  load_denoiser=False,
)

wav = model.generate(
    text="VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.",
    cfg_value=2.0,
    inference_timesteps=10,
)
sf.write("demo.wav", wav, model.tts_model.sample_rate)
print("saved: demo.wav")

If you prefer downloading from ModelScope first, you can use:

pip install modelscope

from modelscope import snapshot_download
snapshot_download("OpenBMB/VoxCPM2", local_dir='./pretrained_models/VoxCPM2') # specify the local directory to save the model

from voxcpm import VoxCPM
import soundfile as sf
model = VoxCPM.from_pretrained("./pretrained_models/VoxCPM2", load_denoiser=False)

wav = model.generate(
    text="VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.",
    cfg_value=2.0,
    inference_timesteps=10,
)
sf.write("demo.wav", wav, model.tts_model.sample_rate)

🎨 Voice Design

Create a voice from a natural-language description — no reference audio needed. Format: put the description in parentheses at the start of text(e.g. "(your voice description)The text to synthesize."):

wav = model.generate(
    text="(A young woman, gentle and sweet voice)Hello, welcome to VoxCPM2!",
    cfg_value=2.0,
    inference_timesteps=10,
)
sf.write("voice_design.wav", wav, model.tts_model.sample_rate)

🎛️ Controllable Voice Cloning

Upload a reference audio. The model clones the timbre, and you can still use control instructions to adjust speed, emotion, or style.

wav = model.generate(
    text="This is a cloned voice generated by VoxCPM2.",
    reference_wav_path="path/to/voice.wav",
)
sf.write("clone.wav", wav, model.tts_model.sample_rate)

wav = model.generate(
    text="(slightly faster, cheerful tone)This is a cloned voice with style control.",
    reference_wav_path="path/to/voice.wav",
    cfg_value=2.0,
    inference_timesteps=10,
)
sf.write("controllable_clone.wav", wav, model.tts_model.sample_rate)

🎙️ Ultimate Cloning

Provide both the reference audio and its exact transcri

(Preview — first 8 000 chars. View full README ↗)

OpenDataLoader PDF

PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.

🔍 PDF parser for AI data extraction — Extract Markdown, JSON (with bounding boxes), and HTML from any PDF. #1 in benchmarks (0.907 overall). Deterministic local mode + AI hybrid mode for complex pages.

• How accurate is it? — #1 in benchmarks: 0.907 overall, 0.928 table accuracy across 200 real-world PDFs including multi-column and scientific papers. Deterministic local mode + AI hybrid mode for complex pages (benchmarks)
• Scanned PDFs and OCR? — Yes. Built-in OCR (80+ languages) in hybrid mode. Works with poor-quality scans at 300 DPI+ (hybrid mode)
• Tables, formulas, images, charts? — Yes. Complex/borderless tables, LaTeX formulas, and AI-generated picture/chart descriptions all via hybrid mode (hybrid mode)
• How do I use this for RAG? — pip install opendataloader-pdf, convert in 3 lines. Outputs structured Markdown for chunking, JSON with bounding boxes for source citations, and HTML. LangChain integration available. Python, Node.js, Java SDKs (quick start | LangChain)

♿ PDF accessibility automation — The same layout analysis engine also powers auto-tagging. First open-source tool to generate Tagged PDFs end-to-end (coming Q2 2026).

• What's the problem? — Accessibility regulations are now enforced worldwide. Manual PDF remediation costs $50–200 per document and doesn't scale (regulations)
• What's free? — Layout analysis + auto-tagging (Q2 2026, Apache 2.0). Untagged PDF in → Tagged PDF out. No proprietary SDK dependency (auto-tagging preview)
• What about PDF/UA compliance? — Converting Tagged PDF to PDF/UA-1 or PDF/UA-2 is an enterprise add-on. Auto-tagging generates the Tagged PDF; PDF/UA export is the final step (pipeline)
• Why trust this? — Built in collaboration with Dual Lab (veraPDF developers) based on PDF Association specifications, best practice guides and expertise of the PDF Community. Auto-tagging follows the Well-Tagged PDF specification, validated with veraPDF (collaboration)

Get Started in 30 Seconds

Requires: Java 11+ and Python 3.10+ (Node.js | Java also available)

Before you start: run java -version. If not found, install JDK 11+ from Adoptium.

pip install -U opendataloader-pdf

import opendataloader_pdf

# Batch all files in one call — each convert() spawns a JVM process, so repeated calls are slow
opendataloader_pdf.convert(
    input_path=["file1.pdf", "file2.pdf", "folder/"],
    output_dir="output/",
    format="markdown,json"
)

Annotated PDF output — each element (heading, paragraph, table, image) detected with bounding boxes and semantic type.

What Problems Does This Solve?

Capability Matrix

Extraction Benchmarks

opendataloader-pdf [hybrid] ranks #1 overall (0.907) across reading order, table, and heading extraction accuracy.

(Preview — first 8 000 chars. View full README ↗)

Superpowers

Superpowers is a complete software development workflow for your coding agents, built on top of a set of composable "skills" and some initial instructions that make sure your agent uses them.

How it works

It starts from the moment you fire up your coding agent. As soon as it sees that you're building something, it doesn't just jump into trying to write code. Instead, it steps back and asks you what you're really trying to do.

Once it's teased a spec out of the conversation, it shows it to you in chunks short enough to actually read and digest.

After you've signed off on the design, your agent puts together an implementation plan that's clear enough for an enthusiastic junior engineer with poor taste, no judgement, no project context, and an aversion to testing to follow. It emphasizes true red/green TDD, YAGNI (You Aren't Gonna Need It), and DRY.

Next up, once you say "go", it launches a subagent-driven-development process, having agents work through each engineering task, inspecting and reviewing their work, and continuing forward. It's not uncommon for Claude to be able to work autonomously for a couple hours at a time without deviating from the plan you put together.

There's a bunch more to it, but that's the core of the system. And because the skills trigger automatically, you don't need to do anything special. Your coding agent just has Superpowers.

Sponsorship

If Superpowers has helped you do stuff that makes money and you are so inclined, I'd greatly appreciate it if you'd consider sponsoring my opensource work.

Thanks!

• Jesse

Installation

Note: Installation differs by platform. Claude Code or Cursor have built-in plugin marketplaces. Codex and OpenCode require manual setup.

Claude Code Official Marketplace

Superpowers is available via the official Claude plugin marketplace

Install the plugin from Claude marketplace:

/plugin install superpowers@claude-plugins-official

Claude Code (via Plugin Marketplace)

In Claude Code, register the marketplace first:

/plugin marketplace add obra/superpowers-marketplace

Then install the plugin from this marketplace:

/plugin install superpowers@superpowers-marketplace

Cursor (via Plugin Marketplace)

In Cursor Agent chat, install from marketplace:

/add-plugin superpowers

or search for "superpowers" in the plugin marketplace.

Codex

Tell Codex:

Fetch and follow instructions from https://raw.githubusercontent.com/obra/superpowers/refs/heads/main/.codex/INSTALL.md

Detailed docs: docs/README.codex.md

OpenCode

Tell OpenCode:

Fetch and follow instructions from https://raw.githubusercontent.com/obra/superpowers/refs/heads/main/.opencode/INSTALL.md

Detailed docs: docs/README.opencode.md

GitHub Copilot CLI

copilot plugin marketplace add obra/superpowers-marketplace
copilot plugin install superpowers@superpowers-marketplace

Gemini CLI

gemini extensions install https://github.com/obra/superpowers

To update:

gemini extensions update superpowers

Verify Installation

Start a new session in your chosen platform and ask for something that should trigger a skill (for example, "help me plan this feature" or "let's debug this issue"). The agent should automatically invoke the relevant superpowers skill.

The Basic Workflow

1. brainstorming - Activates before writing code. Refines rough ideas through questions, explores alternatives, presents design in sections for validation. Saves design document.

2. using-git-worktrees - Activates after design approval. Creates isolated workspace on new branch, runs project setup, verifies clean test baseline.

3. writing-plans - Activates with approved design. Breaks work into bite-sized tasks (2-5 minutes each). Every task has exact file paths, complete code, verification steps.

4. subagent-driven-development or executing-plans - Activates with plan. Dispatches fresh subagent per task with two-stage review (spec compliance, then code quality), or executes in batches with human checkpoints.

5. test-driven-development - Activates during implementation. Enforces RED-GREEN-REFACTOR: write failing test, watch it fail, write minimal code, watch it pass, commit. Deletes code written before tests.

6. requesting-code-review - Activates between tasks. Reviews against plan, reports issues by severity. Critical issues block progress.

7. finishing-a-development-branch - Activates when tasks complete. Verifies tests, presents options (merge/PR/keep/discard), cleans up worktree.

The agent checks for relevant skills before any task. Mandatory workflows, not suggestions.

What's Inside

Skills Library

Testing
- test-driven-development - RED-GREEN-REFACTOR cycle (includes testing anti-patterns reference)

Debugging
- systematic-debugging - 4-phase root cause process (includes root-cause-tracing, defense-in-depth, condition-based-waiting techniques)
- verification-before-completion - Ensure it's actually fixed

Collaboration
- brainstorming - Socratic design refinement
- writing-plans - Detailed implementation plans
- executing-plans - Batch execution with checkpoints
- dispatching-parallel-agents - Concurrent subagent workflows
- requesting-code-review - Pre-review checklist
- receiving-code-review - Responding to feedback
- using-git-worktrees - Parallel development branches
- finishing-a-development-branch - Merge/PR decision workflow
- subagent-driven-development - Fast iteration with two-stage review (spec compliance, then code quality)

Meta
- writing-skills - Create new skills following best practices (includes testing methodology)
- using-superpowers - Introduction to the skills system

Philosophy

• Test-Driven Development - Write tests first, always
• Systematic over ad-hoc - Process over guessing
• Complexity reduction - Simplicity as primary goal
• Evidence over claims - Verify before declaring success

Read more: Superpowers for Claude Code

Contributing

Skills live directly in this repository. To contribute:

1. Fork the repository
2. Create a branch for your skill
3. Follow the writing-skills skill for creating and testing new skills
4. Submit a PR

See skills/writing-skills/SKILL.md for the complete guide.

Updating

Skills update automatically when you update the plugin:

/plugin update superpowers

License

MIT License - see LICENSE file for details

Community

Superpowers is built by Jesse Vincent and the rest of the folks at Prime Radiant.

• Discord: Join us for community support, questions, and sharing what you're building with Superpowers
• Issues: https://github.com/obra/superpowers/issues
• Release announcements: Sign up to get notified about new versions

(Preview — first 8 000 chars. View full README ↗)

SEO Machine

A specialized Claude Code workspace for creating long-form, SEO-optimized blog content for any business. This system helps you research, write, analyze, and optimize content that ranks well and serves your target audience.

Overview

SEO Machine is built on Claude Code and provides:
- Custom Commands: /research, /write, /rewrite, /analyze-existing, /optimize, /performance-review, /publish-draft, /article, /priorities, plus specialized research and landing page commands
- Specialized Agents: Content analyzer, SEO optimization, meta element creation, internal linking, keyword mapping, editor, performance analysis, headline generator, CRO analyst, landing page optimizer
- Marketing Skills: 26 marketing skills for copywriting, CRO, A/B testing, email sequences, pricing strategy, and more
- Advanced SEO Analysis: Search intent detection, keyword density & clustering, content length comparison, readability scoring, SEO quality rating (0-100)
- Data Integrations: Google Analytics 4, Google Search Console, DataForSEO for real-time performance insights
- Context-Driven: Brand voice, style guide, SEO guidelines, and examples guide all content
- Workflow Organization: Structured directories for topics, research, drafts, and published content

Getting Started

Prerequisites

• Claude Code installed
• Anthropic API account

Installation

1. Clone this repository:

git clone https://github.com/[your-username]/seomachine.git
cd seomachine

1. Install Python dependencies for analysis modules:

pip install -r data_sources/requirements.txt

This installs:
- Google Analytics/Search Console integrations
- DataForSEO API client
- NLP libraries (nltk, textstat)
- Machine learning (scikit-learn)
- Web scraping tools (beautifulsoup4)

1. Open in Claude Code:

claude-code .

1. Customize Context Files (Important!):

All context files are provided as templates. Fill them out with your company's information:

• context/brand-voice.md - Define your brand voice and messaging (see examples/castos/ for reference)
• context/writing-examples.md - Add 3-5 exemplary blog posts from your site
• context/features.md - List your product/service features and benefits
• context/internal-links-map.md - Map your key pages for internal linking
• context/style-guide.md - Fill in your style preferences
• context/target-keywords.md - Add your keyword research and topic clusters
• context/competitor-analysis.md - Add competitor analysis and insights
• context/seo-guidelines.md - Review and adjust SEO requirements

Quick Start: Check out examples/castos/ to see a complete real-world example of all context files filled out for a podcast hosting SaaS company.

Workflows

Creating New Content

1. Start with Research

/research [topic]

What it does:
- Performs keyword research
- Analyzes top 10 competitors
- Identifies content gaps
- Creates comprehensive research brief
- Saves to /research/ directory

Example:

/research content marketing strategies for B2B SaaS

2. Write the Article

/write [topic or research brief]

What it does:
- Creates 2000-3000+ word SEO-optimized article
- Maintains your brand voice from context/brand-voice.md
- Integrates keywords naturally
- Includes internal and external links
- Provides meta elements (title, description, keywords)
- Automatically triggers optimization agents
- Saves to /drafts/ directory

Example:

/write content marketing strategies for B2B SaaS

Agent Auto-Execution:
After writing, these agents automatically analyze the content:
- SEO Optimizer: On-page SEO recommendations
- Meta Creator: Multiple meta title/description options
- Internal Linker: Specific internal linking suggestions
- Keyword Mapper: Keyword placement and density analysis

3. Final Optimization

/optimize [article file]

What it does:
- Comprehensive SEO audit
- Validates all elements meet requirements
- Provides final polish recommendations
- Generates publishing readiness score
- Creates optimization report

Example:

/optimize drafts/content-marketing-strategies-2025-10-29.md

Updating Existing Content

1. Analyze Existing Post

/analyze-existing [URL or file path]

What it does:
- Fetches and analyzes current content
- Evaluates SEO performance
- Identifies outdated information
- Assesses competitive positioning
- Provides content health score (0-100)
- Recommends update priority and scope
- Saves analysis to /research/ directory

Examples:

/analyze-existing https://yoursite.com/blog/marketing-guide
/analyze-existing published/marketing-guide-2024-01-15.md

2. Rewrite/Update Content

/rewrite [topic or analysis file]

What it does:
- Updates content based on analysis findings
- Refreshes statistics and examples
- Improves SEO optimization
- Adds new sections to fill gaps
- Maintains what works from original
- Tracks changes made
- Saves to /rewrites/ directory

Example:

/rewrite marketing guide

Commands Reference

/research [topic]

Comprehensive keyword and competitive research for new content.

Output: Research brief in /research/brief-[topic]-[date].md

Includes:
- Primary and secondary keywords
- Competitor analysis (top 10)
- Content gaps and opportunities
- Recommended outline
- Internal linking strategy
- Meta elements preview

/write [topic]

Create long-form SEO-optimized article (2000-3000+ words).

Output: Article in /drafts/[topic]-[date].md

Includes:
- Complete article with H1/H2/H3 structure
- SEO-optimized content
- Internal and external links
- Meta elements (title, description, keywords)
- SEO checklist

Auto-Triggers:
- SEO Optimizer agent
- Meta Creator agent
- Internal Linker agent
- Keyword Mapper agent

/rewrite [topic]

Update and improve existing content.

Output: Updated article in /rewrites/[topic]-rewrite-[date].md

Includes:
- Rewritten/updated content
- Change summary
- Before/after comparison
- Updated SEO elements

/analyze-existing [URL or file]

Analyze existing blog posts for improvement opportunities.

Output: Analysis report in /research/analysis-[topic]-[date].md

Includes:
- Content health score (0-100)
- Quick wins (immediate improvements)
- Strategic improvements
- Rewrite priority and scope
- Research brief for rewrite

/optimize [file]

Final SEO optimization pass before publishing.

Output: Optimization report in /drafts/optimization-report-[topic]-[date].md

Includes:
- SEO score (0-100)
- Priority fixes
- Quick wins
- Meta element options
- Link enhancement suggestions
- Publishing readiness assessment

/publish-draft [file]

Publish article to WordPress via REST API with Yoast SEO metadata.

/article [topic]

Simplified article creation workflow.

/priorities

Content prioritization matrix using analytics data to identify highest-impact content tasks.

/scrub [file]

Remove AI watermarks and patterns from content (em-dashes, filler phrases, robotic patterns).

Research Commands

Landing Page Commands

(Preview — first 8 000 chars. View full README ↗)

Archon

The first open-source harness builder for AI coding. Make AI coding deterministic and repeatable.

Archon is a workflow engine for AI coding agents. Define your development processes as YAML workflows - planning, implementation, validation, code review, PR creation - and run them reliably across all your projects.

Like what Dockerfiles did for infrastructure and GitHub Actions did for CI/CD - Archon does for AI coding workflows. Think n8n, but for software development.

Why Archon?

When you ask an AI agent to "fix this bug", what happens depends on the model's mood. It might skip planning. It might forget to run tests. It might write a PR description that ignores your template. Every run is different.

Archon fixes this. Encode your development process as a workflow. The workflow defines the phases, validation gates, and artifacts. The AI fills in the intelligence at each step, but the structure is deterministic and owned by you.

• Repeatable - Same workflow, same sequence, every time. Plan, implement, validate, review, PR.
• Isolated - Every workflow run gets its own git worktree. Run 5 fixes in parallel with no conflicts.
• Fire and forget - Kick off a workflow, go do other work. Come back to a finished PR with review comments.
• Composable - Mix deterministic nodes (bash scripts, tests, git ops) with AI nodes (planning, code generation, review). The AI only runs where it adds value.
• Portable - Define workflows once in .archon/workflows/, commit them to your repo. They work the same from CLI, Web UI, Slack, Telegram, or GitHub.

What It Looks Like

Here's an example of an Archon workflow that plans, implements in a loop until tests pass, gets your approval, then creates the PR:

# .archon/workflows/build-feature.yaml
nodes:
  - id: plan
    prompt: "Explore the codebase and create an implementation plan"

  - id: implement
    depends_on: [plan]
    loop:                                      # AI loop - iterate until done
      prompt: "Read the plan. Implement the next task. Run validation."
      until: ALL_TASKS_COMPLETE
      fresh_context: true                      # Fresh session each iteration

  - id: run-tests
    depends_on: [implement]
    bash: "bun run validate"                   # Deterministic - no AI

  - id: review
    depends_on: [run-tests]
    prompt: "Review all changes against the plan. Fix any issues."

  - id: approve
    depends_on: [review]
    loop:                                      # Human approval gate
      prompt: "Present the changes for review. Address any feedback."
      until: APPROVED
      interactive: true                        # Pauses and waits for human input

  - id: create-pr
    depends_on: [approve]
    prompt: "Push changes and create a pull request"

Tell your coding agent what you want, and Archon handles the rest:

You: Use archon to add dark mode to the settings page

Agent: I'll run the archon-idea-to-pr workflow for this.
       → Creating isolated worktree on branch archon/task-dark-mode...
       → Planning...
       → Implementing (task 1/4)...
       → Implementing (task 2/4)...
       → Tests failing - iterating...
       → Tests passing after 2 iterations
       → Code review complete - 0 issues
       → PR ready: https://github.com/you/project/pull/47

Previous Version

Looking for the original Python-based Archon (task management + RAG)? It's fully preserved on the archive/v1-task-management-rag branch.

Getting Started

Most users should start with the Full Setup - it walks you through credentials, installs the Archon skill into your projects, and gives you the web dashboard.

Already have Claude Code and just want the CLI? Jump to the Quick Install.

Full Setup (5 minutes)

Clone the repo and use the guided setup wizard. This configures credentials, platform integrations, and copies the Archon skill into your target projects.

# macOS/Linux
curl -fsSL https://bun.sh/install | bash

# Windows (PowerShell)
irm bun.sh/install.ps1 | iex

# macOS
brew install gh

# Windows (via winget)
winget install GitHub.cli

# Linux (Debian/Ubuntu)
sudo apt install gh

# macOS/Linux/WSL
curl -fsSL https://claude.ai/install.sh | bash

# Windows (PowerShell)
irm https://claude.ai/install.ps1 | iex

git clone https://github.com/coleam00/Archon
cd Archon
bun install
claude

Then say: "Set up Archon"

The setup wizard walks you through everything: CLI installation, authentication, platform selection, and copies the Archon skill to your target repo.

Quick Install (30 seconds)

Already have Claude Code set up? Install the standalone CLI binary and skip the wizard.

macOS / Linux

curl -fsSL https://archon.diy/install | bash

Windows (PowerShell)

irm https://archon.diy/install.ps1 | iex

Homebrew

brew install coleam00/archon/archon

Start Using Archon

Once you've completed either setup path, go to your project and start working:

cd /path/to/your/project
claude

Use archon to fix issue #42

What archon workflows do I have? When would I use each one?

The coding agent handles workflow selection, branch naming, and worktree isolation for you. Projects are registered automatically the first time they're used.

Important: Always run Claude Code from your target repo, not from the Archon repo. The setup wizard copies the Archon skill into your project so it works from there.

Web UI

Archon includes a web dashboard for chatting with your coding agent, running workflows, and monitoring activity. To start it, ask your coding agent to run the frontend from the Archon repo, or run bun run dev from the repo root yourself.

Register a project by clicking + next to "Project" in the chat sidebar - enter a GitHub URL or local path. Then start a conversation, invoke workflows, and watch progress in real time.

Key pages:
- Chat - Conversation interface with real-time streaming and tool call visualization
- Dashboard - Mission Control for monitoring running workflows, with filterable history by project, status, and date
- Workflow Builder - Visual drag-and-drop editor for creating DAG workflows with loop nodes
- Workflow Execution - Step-by-step progress view for any running or completed workflow

Monitoring hub: The sidebar shows conversations from all platforms - not just the web. Workflows kicked off from the CLI, messages from Slack or Telegram, GitHub issue interactions - everything appears in one place.

See the Web UI Guide for full documentation.

What Can You Automate?

Archon ships with workflows for common development tasks:

| Work

(Preview — first 8 000 chars. View full README ↗)

Kronos is the first open-source foundation model for financial candlesticks (K-lines),
trained on data from over 45 global exchanges.

Claudian

An Obsidian plugin that embeds AI coding agents (Claude Code, Codex, and more to come) in your vault. Your vault becomes the agent's working directory — file read/write, search, bash, and multi-step workflows all work out of the box.

Features & Usage

Open the chat sidebar from the ribbon icon or command palette. Select text and use the hotkey for inline edit. Everything works like Claude Code or Codex — talk to the agent, and it reads, writes, edits, and searches files in your vault.

Inline Edit — Select text or start at the cursor position + hotkey to edit directly in notes with word-level diff preview.

Slash Commands & Skills — Type / or $ for reusable prompt templates or Skills from user- and vault-level scopes.

@mention - Type @ to mention anything you want the agent to work with, vault files, subagents, MCP servers, or files in external directories.

Plan Mode — Toggle via Shift+Tab. The agent explores and designs before implementing, then presents a plan for approval.

Instruction Mode (#) — Refined custom instructions added from the chat input.

MCP Servers — Connect external tools via Model Context Protocol (stdio, SSE, HTTP). Claude manages vault MCP in-app; Codex uses its own CLI-managed MCP configuration.

Multi-Tab & Conversations — Multiple chat tabs, conversation history, fork, resume, and compact.

Requirements

• Claude provider: Claude Code CLI installed (native install recommended). Claude subscription/API or compatible provider (Openrouter, Kimi, etc.).
• Codex provider (optional): Codex CLI installed.
• Obsidian v1.4.5+
• Desktop only (macOS, Linux, Windows)

Installation

From GitHub Release (recommended)

1. Download main.js, manifest.json, and styles.css from the latest release
2. Create a folder called claudian in your vault's plugins folder:
   /path/to/vault/.obsidian/plugins/claudian/
3. Copy the downloaded files into the claudian folder
4. Enable the plugin in Obsidian:
5. Settings → Community plugins → Enable "Claudian"

Using BRAT

BRAT (Beta Reviewers Auto-update Tester) allows you to install and automatically update plugins directly from GitHub.

1. Install the BRAT plugin from Obsidian Community Plugins
2. Enable BRAT in Settings → Community plugins
3. Open BRAT settings and click "Add Beta plugin"
4. Enter the repository URL: https://github.com/YishenTu/claudian
5. Click "Add Plugin" and BRAT will install Claudian automatically
6. Enable Claudian in Settings → Community plugins

Tip: BRAT will automatically check for updates and notify you when a new version is available.

From source (development)

1. Clone this repository into your vault's plugins folder:
   bash cd /path/to/vault/.obsidian/plugins git clone https://github.com/YishenTu/claudian.git cd claudian

2. Install dependencies and build:
   bash npm install npm run build

3. Enable the plugin in Obsidian:

4. Settings → Community plugins → Enable "Claudian"

Development

# Watch mode
npm run dev

# Production build
npm run build

Tip: Copy .env.local.example to .env.local or npm install and setup your vault path to auto-copy files during development.

Privacy & Data Use

• Sent to API: Your input, attached files, images, and tool call outputs. Default: Anthropic (Claude) or OpenAI (Codex); configurable via environment variables.
• Local storage: Claudian settings and session metadata in vault/.claudian/; Claude provider files in vault/.claude/; transcripts in ~/.claude/projects/ (Claude) and ~/.codex/sessions/ (Codex).
• No telemetry: No tracking beyond your configured API provider.

Troubleshooting

Claude CLI not found

If you encounter spawn claude ENOENT or Claude CLI not found, the plugin can't auto-detect your Claude installation. Common with Node version managers (nvm, fnm, volta).

Solution: Find your CLI path and set it in Settings → Advanced → Claude CLI path.

Note: On Windows, avoid .cmd wrappers. Use claude.exe or cli.js.

Alternative: Add your Node.js bin directory to PATH in Settings → Environment → Custom variables.

npm CLI and Node.js not in same directory

If using npm-installed CLI, check if claude and node are in the same directory:

dirname $(which claude)
dirname $(which node)

If different, GUI apps like Obsidian may not find Node.js.

Solutions:
1. Install native binary (recommended)
2. Add Node.js path to Settings → Environment: PATH=/path/to/node/bin

Codex provider

Codex support is live but still needs more testing across platforms and installation methods. If you run into any bugs, please submit a GitHub issue.

Architecture

src/
├── main.ts                      # Plugin entry point
├── app/                         # Shared defaults and plugin-level storage
├── core/                        # Provider-neutral runtime, registry, and type contracts
│   ├── runtime/                 # ChatRuntime interface and approval types
│   ├── providers/               # Provider registry and workspace services
│   ├── security/                # Approval utilities
│   └── ...                      # commands, mcp, prompt, storage, tools, types
├── providers/
│   ├── claude/                  # Claude SDK adaptor, prompt encoding, storage, MCP, plugins
│   └── codex/                   # Codex app-server adaptor, JSON-RPC transport, JSONL history
├── features/
│   ├── chat/                    # Sidebar chat: tabs, controllers, renderers
│   ├── inline-edit/             # Inline edit modal and provider-backed edit services
│   └── settings/                # Settings shell with provider tabs
├── shared/                      # Reusable UI components and modals
├── i18n/                        # Internationalization (10 locales)
├── utils/                       # Cross-cutting utilities
└── style/                       # Modular CSS

Roadmap

• [x] 1M Opus and Sonnet models
• [x] Codex provider integration
• [ ] More to come!

License

Licensed under the MIT License.

Star History

Acknowledgments

• Obsidian for the plugin API
• Anthropic for Claude and the Claude Agent SDK
• OpenAI for Codex

(Preview — first 8 000 chars. View full README ↗)