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/extend-agentnpx skills add agno-agi/agentos-render --skill extend-agentgit clone --depth 1 https://github.com/agno-agi/agentos-renderWrote 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/agno-agi/agentos-render/extend-agent)<a href="https://agentmods.dev/skills/agno-agi/agentos-render/extend-agent"><img src="https://agentmods.dev/badge/skills/agno-agi/agentos-render/extend-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 | $0.00114 | $0.02128 |
| Opus 5 | $0.00057 | $0.01064 |
| Sonnet 5 | $0.00023 | $0.00426 |
| Haiku 4.5 | $0.00011 | $0.00213 |
Grade B, and why
extend-agent scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
- `curl -sSf http://localhost:8000/health` returns 200 (else ask for `docker compose up -d --build`). Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `curl -sSf http://localhost:8000/health` returns 200 (else ask for `docker compose up -d --build`). This is a copy
100% identical to extend-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extend an Agent
Coding-agent workflow: run as
/extend-agentor by describing the task.
The user names a change; you implement it, verify it against the live agent, and ask if there's more. Stop when they say done. The autonomous half is improve-agent — run it afterward.
The platform is on http://localhost:8000 (RUNTIME_ENV=dev); code edits hot-reload.
0. Preconditions
curl -sSf http://localhost:8000/healthreturns 200 (else ask fordocker compose up -d --build).- The container is bound to this checkout:
docker inspect agentos-api --format '{{range .Mounts}}{{.Source}}{{"\n"}}{{end}}' | grep -F "$(pwd)"prints a line. Empty →cdto the bound repo or restart compose from here. - Ask for the target slug. Suggest a branch:
git checkout -b extend/<slug>-$(date +%Y%m%d).
1. Read the agent
Confirm the slug is code, not a Studio-built component:
curl -s http://localhost:8000/agents | jq -r '.[] | "\(.id)\tis_component=\(.is_component)"'
is_component=true has no source file — route the change through Platform Builder (edit_agent / edit_team / edit_workflow, then publish_component). Never create agents/<slug>.py under that id: code wins on resolution and silently shadows the built component.
Open the file — agents/<slug>.py, or for the reference components platform-builder → agents/builder.py, platform-manager → agents/manager.py, platform-engineer → agents/engineer.py, agno → teams/lead.py. Capture purpose (docstring + INSTRUCTIONS), tools, pattern, and the existing levers (learning=, knowledge=, num_history_runs, model). Restate the purpose in 1–2 sentences.
2. Ask what to change
Structured choice when the harness has one, else plain text. Multi-select is fine — handle sequentially, then loop:
- Add a tool — MCP server, agno toolkit, or function tool.
- Add a capability — knowledge base, learning/memory, sub-agent/context provider, scheduled task.
- Grow the registry — declare a block in
app/registry.pyso everything the platform builds can carry it. Offer this unprompted when the ask is plural ("agents should be able to…"). - Refine instructions — clarify, narrow, change tone or format.
- Fix a bug — ask for the failing prompt, the observed behavior, what they want instead.
- Something else.
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.
- 4d ago First seen · 113 lines · 114 tokens per session scan B 149dbec12b67
extend-agent is a skill published in the GitHub repository agno-agi/agentos-render (3 stars, last pushed 4d ago), licensed Apache-2.0. It adds 114 tokens to every session and 2,128 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 extend-agent, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
extend-agent
User-driven loop to change an existing agent in this AgentOS — add a tool/MCP server/toolkit, add a capability (knowledge base, learning/memory, sub-agent, scheduled task), grow the safe Studio registry so components built at runtime gain a new capability, refine its instructions, or fix a specific known bug…
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…
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…
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.
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.
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…