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Fetch Agents

作者 Steve-Dusty · GitHub ↗ · v0.3.0 · MIT-0
darwinlinux ✓ 安全检测通过
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当前安装
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在 OpenClaw 中安装
/install fetch-agents
功能描述
Call Fetch.ai Agentverse agents by address. Search the Agentverse marketplace, browse a curated catalog of top agents (Tavily Search, ASI1-Mini, DALL-E 3, Te...
使用说明 (SKILL.md)

Fetch Agents

Send queries to Fetch.ai Agentverse agents and return the agent's reply in natural language.

CRITICAL RULES — READ FIRST

  1. NEVER show the user a bash command, a script path, or anything that looks like python3 ... or bash .... Those are for YOU to run internally. The user is chatting in Telegram/CLI and wants plain language.
  2. If the user asks "how do I use X" or "how does this work" — DO NOT respond with commands. Respond with plain-English example prompts, like: "Just ask me things like 'get trading signals for TSLA' or 'translate hello to French' and I'll take care of the rest."
  3. When the user makes an actual request (get signals, translate, etc.), run the script yourself and reply with the agent's answer. Format it nicely. Do not tell them to run anything.
  4. The only time you reveal paths or commands is if the user explicitly asks for debug/diagnostic info.

How to answer common user questions

  • "How do I use this?" / "What can this do?" → Explain in plain English: it calls Fetch.ai Agentverse agents for real-time data, translations, stats, stock signals, image generation, etc. Give 3-4 example prompts the user can try (in natural language, NOT bash).
  • "What agents are available?" / "Show me the catalog" → Run catalog.py and format the result as a friendly list.
  • "Find a [topic] agent" → Run search.py and return the top matches in plain text.
  • Any actual task (signals, translation, stats, etc.) → Run fire.sh, tell the user to hold on ~40 seconds, then run result.sh and reply with the agent's answer in your own words.

Natural-language prompts the user might send

  • "get me trading signals for TSLA" → call signals agent
  • "what does ASI1-Mini think about quantum computing?" → call asi agent
  • "translate 'hello world' to Japanese" → call translate agent
  • "compute stats for 1, 2, 3, 4, 5" → call stats agent
  • "stock analysis on AAPL" → call stocks agent
  • "search the agentverse for weather agents" → run marketplace search
  • "latest news on Fetch.ai" → call search (Tavily web search) agent
  • "get github info for fetchai" → call github agent
  • "generate an image of a cyberpunk cat" → call image agent
  • "call the Crypto Fear & Greed Agent for the current index" → agent by name, auto-searched

Agent Shortcuts

Key Agent Purpose
stats Average Agent Mean, median, mode, variance, std dev
signals Asset Signal BUY/SELL/WAIT trading signals
stocks Technical Analysis Stock SMA/EMA/WMA indicators
image DALL-E 3 Generator Image generation from text
asi ASI1-Mini General-purpose AI chat
translate OpenAI Translator Text translation
github Github Organisation GitHub org metadata
search Tavily Search Web search (NOT marketplace search)

Internal Execution (for you, the agent, not the user)

The following are the commands YOU run with the exec tool. Never show these to the user.

Show the curated catalog

Runs in \x3C5 seconds. Use for "what agents are available?" type questions.

python3 {baseDir}/scripts/catalog.py

Search the Agentverse marketplace

Runs in \x3C5 seconds. Use when the user wants to find agents by topic.

python3 {baseDir}/scripts/search.py "query" -n 10

Call an agent (two-step, ~40 seconds total)

Calling an Agentverse agent takes 30-60 seconds. Use the two-step pattern:

Step 1 — Fire (returns instantly):

bash {baseDir}/scripts/fire.sh \x3Cshortcut-or-address-or-name> "user's query"

Accepts: a shortcut key from the table above, a full agent1q... address, or an agent name (auto-searched).

Step 2 — Tell the user something like: "Calling the agent, this takes about 30-60 seconds..."

Step 3 — After 30-60 seconds, read the result:

bash {baseDir}/scripts/result.sh

Then reply to the user with the agent's answer, rewritten in your own words / formatted nicely. Never show the raw command.

Rules

  1. Resolve {baseDir} to the absolute path of this SKILL.md's parent directory.
  2. Single absolute-path command per exec call. No cd, no &&.
  3. The user never sees the commands or paths. Just give them the agent's answer.
安全使用建议
This skill appears to do exactly what it says: it needs AGENTVERSE_API_KEY, will install uagents and uagents-core, call agentverse.ai endpoints, and run bundled Python scripts. Before installing, consider: (1) Provide a scoped Agentverse API key you trust for mailbox registration; (2) the skill will create a .seed file in its directory (persistent agent identity) and a /tmp/fetch-agents-<youruser>-response.txt file for results; (3) when you call agents the skill spawns a background Python process (via nohup/disown) that will run for the call duration; (4) if you want to limit persistence or network exposure, inspect or run the scripts in an isolated environment or container, or supply a dedicated seed via the FETCH_AGENT_SEED env var. If any of these behaviors are unacceptable, do not install or run the skill until you review/modify the scripts.
能力标签
cryptorequires-sensitive-credentials
能力评估
Purpose & Capability
Name/description match the actual behavior: the scripts call Agentverse endpoints, send ChatMessages to agents, and return responses. The declared requirements (python3, AGENTVERSE_API_KEY) and install of uagents/uagents-core are appropriate for this purpose.
Instruction Scope
SKILL.md instructs the agent to run bundled scripts (catalog.py, search.py, call.py, fire.sh, result.sh). Those scripts perform network requests to agentverse.ai, create a local mailbox, and read/write local files (.seed in the skill directory; /tmp/fetch-agents-<user>-response.txt). This is expected for the skill but does expand scope to local file I/O and background process management.
Install Mechanism
Install specifies uagents and uagents-core packages (uv/pip). No arbitrary URL downloads or extract-from-unknown-host steps are present. This is consistent with the Python-based implementation.
Credentials
Only AGENTVERSE_API_KEY is required and declared as the primary credential, which is reasonable. The code also optionally reads FETCH_AGENT_SEED (not declared) and persists/uses a local .seed file; this is functional for mailbox/identity persistence but worth noting because it creates a persistent local identity and can be overridden by an env var.
Persistence & Privilege
The skill does not request always:true and does not modify other skills. It does persist a seed file (./scripts/.seed path) and writes response files to /tmp, and fire.sh launches a background Python process (nohup + disown). These behaviors are normal for this kind of caller but mean the skill will leave local artifacts and background processes while waiting for agent responses.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install fetch-agents
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /fetch-agents 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.3.0
Stronger rules for natural-language UX: agent never shows bash commands to users
v0.2.0
Rewrote SKILL.md: natural-language UX for Telegram users, internal commands hidden
v0.1.0
Initial release of fetch-agents: call and discover Fetch.ai Agentverse agents. - Search the Agentverse marketplace for AI agents. - Browse a curated catalog of top agents (e.g., ASI1-Mini, DALL-E 3, Technical Analysis). - Send ChatMessages or tasks to any agent by name or address. - Two-step call process to handle agent response times (30–60 seconds). - Requires AGENTVERSE_API_KEY and Python 3. - Catalog includes agent shortcuts for common use cases.
元数据
Slug fetch-agents
版本 0.3.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 3
常见问题

Fetch Agents 是什么?

Call Fetch.ai Agentverse agents by address. Search the Agentverse marketplace, browse a curated catalog of top agents (Tavily Search, ASI1-Mini, DALL-E 3, Te... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 90 次。

如何安装 Fetch Agents?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install fetch-agents」即可一键安装,无需额外配置。

Fetch Agents 是免费的吗?

是的,Fetch Agents 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Fetch Agents 支持哪些平台?

Fetch Agents 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(darwin, linux)。

谁开发了 Fetch Agents?

由 Steve-Dusty(@steve-dusty)开发并维护,当前版本 v0.3.0。

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