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/agno-agi/agentos-render/create-agentnpx skills add agno-agi/agentos-render --skill create-agentgit clone --depth 1 https://github.com/agno-agi/agentos-renderWhat 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 | $0.00114 | $0.03110 |
| Opus 5 | $0.00057 | $0.01555 |
| Sonnet 5 | $0.00023 | $0.00622 |
| Haiku 4.5 | $0.00011 | $0.00311 |
Grade B, and why
create-agent scanned grade B with 2 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 2d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
`curl -sSf http://localhost:8000/health` returns 200. If not, ask the user to `docker compose up -d --build`. Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`curl -sSf http://localhost:8000/health` returns 200. If not, ask the user to `docker compose up -d --build`. This is a copy
100% identical to create-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create a New Agent
Coding-agent workflow: run as
/create-agentor by describing the task.
The platform is on http://localhost:8000 (RUNTIME_ENV=dev); code edits hot-reload.
0. Preconditions
curl -sSf http://localhost:8000/health returns 200. If not, ask the user to docker compose up -d --build.
1. Find the agent worth building
Be self-driving: ask only what needs a human, decide the rest, say what you decided. Two exchanges is the target. Use the harness's structured choice control for choice-shaped questions, plain prompts for free-form ones.
This skill builds lane 1 — a source file in agents/, governed by git. Lane 2 is a component Platform Builder composes at runtime; it can't touch the repo, so anything needing a code change (a toolkit the registry lacks, custom Python, a dependency, growing app/registry.py) is yours.
Check the id is free first — both lanes share one id space and code silently wins, hiding a Studio-built component under your file:
curl -s http://localhost:8000/agents | jq -r '.[] | "\(.id)\t\(.is_component)"'
They named an agent ("build me a GitHub PR reviewer") → ask nothing. Design it, state it in one message, start:
Building PR Reviewer (
pr-reviewer) — reads open PRs on a repo and summarizes what changed and what looks risky. UsesGithubTools; needsGITHUB_ACCESS_TOKEN, already in your.env. Building now — stop me if I've read it wrong.
They named a product (a URL, "an agent for Acme") → the product-agent pattern in Step 3. Ask nothing beyond the URL.
They want guidance → one question: "What's something you do every week that you'd rather hand off?" Dig once with a grounded follow-up (where do you look? what do you do with the result? what's the annoying part?), then propose one recommendation plus two alternates — name, one sentence, toolkit — grounded in the agno-docs MCP (.mcp.json). Skip the demo classics.
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.
- 2d ago First seen · 190 lines · 114 tokens per session scan B 3d602be6547b
create-agent is a skill published in the GitHub repository agno-agi/agentos-render (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 114 tokens to every session and 3,110 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). It is 100% identical to create-agent, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
deploy-platform
Deploy this AgentOS to production with this template's deploy scripts — preflight the provider CLI and account, run the up.sh script, complete the JWT key step, verify the live platform on its public URL, then hand over the redeploy/logs/teardown instructions. Use this skill when the user asks to deploy, ship to…
improve-agent
Autonomous hardening loop for an existing agent — derive probes from the agent's INSTRUCTIONS and from its real usage recorded in the database, run them against the live container, judge responses, edit the agent file, and re-probe until it reliably does what its instructions say. No user input needed. Use to harden…
create-evals
Author eval coverage for an agent in this AgentOS — map what the agent promises, mine real sessions and eval history from Postgres for scenarios, propose capabilities worth testing, then write, run, and audit Case entries in evals/cases.py. Use when the user wants evals created, coverage added, or an agent's behavior…
eval-and-improve
Run the eval suite (python -m evals), diagnose every failure, fix what's in scope, and loop until all cases pass. Use when evals are failing — including overnight run-evals schedule failures — or when the user wants to run, diagnose, or repair the eval suite. To author new coverage, use create-evals instead.
review-and-improve
Repo-wide drift sweep for public-readiness — diff docs against code, confirm every agent is registered and reachable, every env var documented, every doc path exists, and scripts behave as advertised; auto-fix mechanical drift and flag the rest. Use before a public release or after a refactor.
setup-platform
Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.