Claude Code Templates is a command-line tool and catalogue for configuring Anthropic’s Claude Code with agents, commands, settings, hooks, integrations, skills, and project templates. Developers use it to browse and install reusable components for their coding workflows. The catalogue includes many of these Claude Code components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add davila7/claude-code-templates --skill agents-autogptgit clone --depth 1 https://github.com/davila7/claude-code-templatesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/davila7/claude-code-templates/agents-autogpt)<a href="https://agentmods.dev/skills/davila7/claude-code-templates/agents-autogpt"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/agents-autogpt.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 48 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 55 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.02233 |
| Opus 5 | $0.00019 | $0.01117 |
| Sonnet 5 | $0.00008 | $0.00447 |
| Haiku 4.5 | $0.00004 | $0.00223 |
Grade A, and why
autogpt-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
6 near-identical copies found in the catalogue:
- autogpt-agents — 100% identical, 0 lines differ
- autogpt-agents — 100% identical, 0 lines differ
- autogpt-agents — 100% identical, 0 lines differ
- agents-autogpt — 98% identical, 9 lines differ
- research-autogpt — 95% identical, 13 lines differ
- autogpt-agents — 89% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 404 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoGPT - Autonomous AI Agent Platform
Comprehensive platform for building, deploying, and managing continuous AI agents through a visual interface or development toolkit.
When to use AutoGPT
Use AutoGPT when:
- Building autonomous agents that run continuously
- Creating visual workflow-based AI agents
- Deploying agents with external triggers (webhooks, schedules)
- Building complex multi-step automation pipelines
- Need a no-code/low-code agent builder
Key features:
- Visual Agent Builder: Drag-and-drop node-based workflow editor
- Continuous Execution: Agents run persistently with triggers
- Marketplace: Pre-built agents and blocks to share/reuse
- Block System: Modular components for LLM, tools, integrations
- Forge Toolkit: Developer tools for custom agent creation
- Benchmark System: Standardized agent performance testing
Use alternatives instead:
- LangChain/LlamaIndex: If you need more control over agent logic
- CrewAI: For role-based multi-agent collaboration
- OpenAI Assistants: For simple hosted agent deployments
- Semantic Kernel: For Microsoft ecosystem integration
Quick start
Installation (Docker)
# Clone repository
git clone https://github.com/Significant-Gravitas/AutoGPT.git
cd AutoGPT/autogpt_platform
# Copy environment file
cp .env.example .env
# Start backend services
docker compose up -d --build
# Start frontend (in separate terminal)
cd frontend
cp .env.example .env
npm install
npm run dev
Access the platform
- Frontend UI: http://localhost:3000
- Backend API: http://localhost:8006/api
- WebSocket: ws://localhost:8001/ws
Architecture overview
AutoGPT has two main systems:
AutoGPT Platform (Production)
- Visual agent builder with React frontend
- FastAPI backend with execution engine
- PostgreSQL + Redis + RabbitMQ infrastructure
AutoGPT Classic (Development)
- Forge: Agent development toolkit
- Benchmark: Performance testing framework
- CLI: Command-line interface for development
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 404 lines · 39 tokens per session scan A 4b16c070da99
autogpt-agents is a skill published in the GitHub repository davila7/claude-code-templates (30,559 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 2,233 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.