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/improve-agentnpx skills add agno-agi/agentos-render --skill improve-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/improve-agent)<a href="https://agentmods.dev/skills/agno-agi/agentos-render/improve-agent"><img src="https://agentmods.dev/badge/skills/agno-agi/agentos-render/improve-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 | $0.00087 | $0.01811 |
| Opus 5 | $0.00044 | $0.00905 |
| Sonnet 5 | $0.00017 | $0.00362 |
| Haiku 4.5 | $0.00009 | $0.00181 |
Grade A, and why
improve-agent scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `curl -sSf http://localhost:8000/health` returns 200; the container is bound to this checkout (`docker inspect agentos-api --format '{{range .Mounts}}{{.Source}}{{"\n"}}{{end}}' | grep -F "$(pwd)"` prints a line). This is a copy
100% identical to improve-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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve an Agent
Coding-agent workflow: run as
/improve-agentor by describing the task.
Derive probes from the agent's INSTRUCTIONS and its recorded usage, run them against the live container, judge, edit the file, re-probe. No user-supplied test cases. One pass takes 15–30 minutes; re-run if behavior still drifts. To change an agent instead, use extend-agent.
0. Preconditions
curl -sSf http://localhost:8000/healthreturns 200; the container is bound to this checkout (docker inspect agentos-api --format '{{range .Mounts}}{{.Source}}{{"\n"}}{{end}}' | grep -F "$(pwd)"prints a line).- Ask for the target slug and confirm it is code:
curl -s http://localhost:8000/agents | jq -r '.[] | "\(.id)\tis_component=\(.is_component)"'.is_component=truehas no file — route edits through Platform Builder (edit_*+publish_component) and never create a file under that id (it shadows the component). - Suggest a branch:
git checkout -b improve/<slug>-$(date +%Y%m%d).
1. Read the intent
Open the file (agents/<slug>.py; teams/lead.py for agno). Capture purpose, tools, and every explicit rule in INSTRUCTIONS. Restate the purpose in 1–2 sentences; fold in any failure modes the user volunteers.
2. Derive probes
Mine usage first (needs the venv: source .venv/bin/activate):
from db import get_postgres_db
db = get_postgres_db()
sessions, _ = db.get_sessions(component_id="<slug>", limit=20, deserialize=False)
asks = [run["input"]["input_content"] for s in sessions for run in (s.get("runs") or []) if run.get("input")]
evals, _ = db.get_eval_runs(agent_id="<slug>", limit=20, deserialize=False) # team_id= for a team
Look for recurring shapes, visible fumbles, and out-of-scope asks. A recorded answer is a scenario, never the oracle — expected behavior comes from INSTRUCTIONS. Reword private content before it becomes a probe. No sessions is fine.
Then derive from INSTRUCTIONS: 2–3 probes per rule plus 1–2 adversarial, usually 8–12 total, across golden path, edge cases (should refuse or ask, not fabricate), tool selection, and adversarial (injection, malformed input). Write a one-line expected behavior per probe. Wanting a behavior the instructions don't promise is a Step 5 edit, not a probe failure.
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 · 90 lines · 87 tokens per session scan A 0696dd0b6faf
improve-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 87 tokens to every session and 1,811 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to improve-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…
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.
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…
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…
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.