ClawHub is a public registry where OpenClaw users publish, version, search, and install text-based agent skills and OpenClaw packages. It provides web browsing, a CLI-oriented API, moderation, vector search, and artifact hosting for code plugins, bundle plugins, and experimental whole-agent packages. The catalogue skills and agents are entries that can be discovered or used through this registry.
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 agentmods add skills/openclaw/clawhub/convex-agentnpx skills add openclaw/clawhub --skill convex-agentgit clone --depth 1 https://github.com/openclaw/clawhubWrote 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/openclaw/clawhub/convex-agent)<a href="https://agentmods.dev/skills/openclaw/clawhub/convex-agent"><img src="https://agentmods.dev/badge/skills/openclaw/clawhub/convex-agent.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00025 | $0.00227 |
| Opus 5 | $0.00013 | $0.00113 |
| Sonnet 5 | $0.00005 | $0.00045 |
| Haiku 4.5 | $0.00003 | $0.00023 |
Grade A, and why
convex-agent 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 5d 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.
What it actually says
Add an AI agent / RAG backend
Install @convex-dev/agent for durable threads, message history, tool-calls, and vector search/RAG — the backend for an in-app AI agent.
Workflow
- Install @convex-dev/agent + add to convex.config.ts.
- Define the agent (model, tools, instructions); store the LLM key via the
envmicro power. - Create threads + stream messages; persist history in Convex.
- For RAG: embed docs into a vector index and retrieve in the tool.
Rules
- Keep the LLM API key in Convex env (use the
envmicro power), never client-side. - Run model calls in actions ('use node' if the SDK needs it).
- Persist threads/messages in Convex for durability + reactivity.
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.
- 5d ago First seen · 24 lines · 25 tokens per session scan A d903a27a7d0b
convex-agent is a skill published in the GitHub repository openclaw/clawhub (9,391 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 227 once invoked, about $0.0001 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
doc-parse
将 PDF/PPT/Excel/Word 等多格式文档解析为结构化 Markdown，并输出元数据与解析置信度，作为 RAG 与四色卡片的数据底座。.
retrieve
Search a knowledge base using Amazon Bedrock RAG (Retrieval-Augmented Generation). Use when users want to find information from uploaded documents, ask questions about stored knowledge, or retrieve relevant content from the knowledge base. Returns matched content with source references and URLs.
chatbi-mvp
Use when building a ChatBI (conversational BI) MVP from scratch, need to understand the 5 core capabilities (NL2SQL, multi-turn dialogue, RAG knowledge base, data visualization, intelligent attribution), or want to reference SuperSonic's architecture to guide implementation.
deepseek-v4-flash-vision-video-rag
基于DeepSeek视觉大模型（deepseek-v4-flash-vision-exp）的视频理解与问答（video RAG）。当用户提到视频、录像、mp4、监控、游戏录像、影片、动画，并想提问、查找、定位、总结或理解其内容，或问"什么时候发生的""某一秒在干什么"、要时间戳定位、片段回放时，使用本skill。支持剧情理解、事件时间线、OCR字幕/HUD识别；回答带 [MM:SS] 时间戳引用，并生成可播放片段与关键帧展示给用户。无音频分析能力。.
graphify
Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPHREPORT.md.
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.