Fetch.ai Innovation Lab Examples is a collection of runnable Python projects demonstrating autonomous agents, multi-agent systems, and agent-based workflows. It is intended for beginners, builders, and hackathon participants working with technologies such as uAgents, ASI:One, Agentverse, MCP, A2A, LangChain, CrewAI, Gemini, Claude, and OpenAI.
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 fetchai/innovation-lab-examples --skill agentverse-callergit clone --depth 1 https://github.com/fetchai/innovation-lab-examplesWrote 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/fetchai/innovation-lab-examples/agentverse-caller)<a href="https://agentmods.dev/skills/fetchai/innovation-lab-examples/agentverse-caller"><img src="https://agentmods.dev/badge/skills/fetchai/innovation-lab-examples/agentverse-caller/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fetchai/innovation-lab-examples/agentverse-caller"><img src="https://agentmods.dev/badge/skills/fetchai/innovation-lab-examples/agentverse-caller.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00098 | $0.01378 |
| Opus 5.5 | $0.00039 | $0.00551 |
| Sonnet 5.5 | $0.00020 | $0.00276 |
| Haiku 4.5 | $0.00010 | $0.00138 |
Grade A, and why
fetch-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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fetch Agents
Send queries to Fetch.ai Agentverse agents and return the agent's reply in natural language.
CRITICAL RULES — READ FIRST
- NEVER show the user a bash command, a script path, or anything that looks like
python3 ...orbash .... Those are for YOU to run internally. The user is chatting in Telegram/CLI and wants plain language. - 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."
- 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.
- 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.pyand format the result as a friendly list. - "Find a [topic] agent" → Run
search.pyand 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 runresult.shand reply with the agent's answer in your own words.
Natural-language prompts the user might send
- "get me trading signals for TSLA" → call
signalsagent - "what does ASI1-Mini think about quantum computing?" → call
asiagent - "translate 'hello world' to Japanese" → call
translateagent - "compute stats for 1, 2, 3, 4, 5" → call
statsagent - "stock analysis on AAPL" → call
stocksagent - "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
githubagent - "generate an image of a cyberpunk cat" → call
imageagent - "call the Crypto Fear & Greed Agent for the current index" → agent by name, auto-searched
What ships with it
9 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.
- 1mo ago First seen · 106 lines · 98 tokens per session scan A 5a1e3ee07226
fetch-agents is a skill published in the GitHub repository fetchai/innovation-lab-examples (1,170 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 1,378 once invoked, about $0.0004 per session on Opus 5.5. 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.
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