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/eval-and-improvenpx skills add agno-agi/agentos-render --skill eval-and-improvegit 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/eval-and-improve)<a href="https://agentmods.dev/skills/agno-agi/agentos-render/eval-and-improve"><img src="https://agentmods.dev/badge/skills/agno-agi/agentos-render/eval-and-improve.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 5.1 | $0.00074 | $0.01491 |
| Opus 5 | $0.00037 | $0.00745 |
| Sonnet 5 | $0.00015 | $0.00298 |
| Haiku 4.5 | $0.00007 | $0.00149 |
Grade A, and why
eval-and-improve 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 5d 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.
This is a copy
100% identical to eval-and-improve — 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval and Improve
Coding-agent workflow: run as
/eval-and-improveor by describing the task.
Run the suite, diagnose every failure, fix what's in scope, stop when green. Cases live in evals/cases.py (agno.eval.Case), the setup/teardown sweeps in evals/hooks.py, the entrypoint in evals/__main__.py. Each case uses an LLM judge (criteria), a tool-call assertion (expected_tool_calls), and/or a deterministic scorer.
0. Preconditions
- Postgres on 5432 (
nc -z localhost 5432; elsedocker compose up -d agentos-db). - Venv active (
source .venv/bin/activate;./scripts/venv_setup.shif missing). No server needed — cases import the components directly. .envhasOPENAI_API_KEY(andPARALLEL_API_KEYif you use one — it pins Agno's expected web tool name at import). Worktrees don't inherit.env.
1. Run
python -m evals --tag smoke # fast template checks
python -m evals --tag release # broader pre-release checks
python -m evals --tag live # current web/source checks
python -m evals --name <case> # one case
python -m evals --tag smoke --list # what a selector picks, without running
python -m evals --json-output out.json # machine-readable (carries judge_reason)
python -m evals -v # stream the runs
Exit 0 = all passed. Be the only writer: teardowns sweep by snapshot diff, so a note or entity a teammate files during a case gets deleted. Coming from a scheduled failure? Find the case in eval history (db.get_eval_runs(), os.agno.com, or Platform Manager) and reproduce it with --name first; one that won't reproduce is usually environment. Stderr noise at the end of a run (Event loop is closed, httpx timeouts) is harmless.
2. Diagnose
| Symptom | Likely cause | Fix |
|---|---|---|
| Judge: right answer, missing X | Instructions don't push for X | agents/<slug>.py — tighten the rule |
| Judge: fabricated | Should have said it didn't know | Add a "say so plainly" rule |
| Reliability: missing tool | Routing rule weak, or the case too narrow | Strengthen the rule, or broaden expected_tool_calls |
Reliability: additional tool with allow_additional_tool_calls=False |
Agent fanned out | Tighten instructions or allow it |
Agno web tool name mismatch (parallel_search ↔ web_search) |
PARALLEL_API_KEY differs between .env and shell |
Sync and re-run |
| Flips PASS/FAIL across runs, no change | Judge variance | Re-run 2–3×; still flipping → make criteria more falsifiable |
| Fails in the suite, passes alone | Transient flake / 429 | Re-run alone, then the suite; persistent 429s → back off |
| Many fail at once | Broad regression (model, MCP down, tool removed) | Find the root cause; no prompt edits |
run paused awaiting user input |
Hit a HITL gate (Builder archive/delete) — never graded | Keep the case input on the ungated side |
cleanup: in the error |
A teardown couldn't delete what the case created | Hard-delete by id (eval_db.delete_component(id, hard_delete=True), ScheduleManager(eval_db).delete(id), eval_db.delete_learning(id), notes.delete(path)); don't touch the agent or case |
refusing to sweep … |
A guard tripped: rows predate the snapshot, or more rows than a case plausibly creates | Don't loosen the guard; inspect, delete the case's rows by hand, re-run |
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.
- 5d ago First seen · 79 lines · 74 tokens per session scan A 43455a1b1ede
eval-and-improve is a skill published in the GitHub repository agno-agi/agentos-render (3 stars, last pushed 5d ago), licensed Apache-2.0. It adds 74 tokens to every session and 1,491 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to eval-and-improve, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
deploy-platform
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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…
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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…
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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.
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…