Borrowing it
Nothing to install: this file belongs to agno-agi/agentos-modal. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/agno-agi/agentos-modal/main/.agents/skills/create-evals/SKILL.mdgit clone --depth 1 https://github.com/agno-agi/agentos-modalWrote 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-modal/create-evals)<a href="https://agentmods.dev/skills/agno-agi/agentos-modal/create-evals"><img src="https://agentmods.dev/badge/skills/agno-agi/agentos-modal/create-evals/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/agno-agi/agentos-modal/create-evals"><img src="https://agentmods.dev/badge/skills/agno-agi/agentos-modal/create-evals.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00088 | $0.01323 |
| Opus 5.5 | $0.00035 | $0.00529 |
| Sonnet 5.5 | $0.00018 | $0.00265 |
| Haiku 4.5 | $0.00009 | $0.00132 |
Grade A, and why
create-evals 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 3d 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 create-evals — 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Evals
Coding-agent workflow: run as
/create-evalsor by describing the task.
Turn what an agent promises into Case entries in evals/cases.py. The template's cases cover the reference components only; a user-built agent is invisible to the suite until this skill writes one. Failing suite → improve; harden an agent → improve.
Preconditions: configured PostgreSQL reachable, venv active (source .venv/bin/activate; ./scripts/venv_setup.sh if missing), .env populated. Even CLI --help/--list imports application knowledge storage and requires PostgreSQL; no API server is needed for native agent/team cases.
Be self-driving: the repo and the database answer most questions. Ask the user only what they alone know — which jobs matter and which failures would hurt. One pick per exchange, recommendation first.
1. Pick the agent
The user's choice, or the least-covered component — almost always one of theirs. Source components have a file under agents/ or teams/. Studio-built components have none: Agent.load("<id>", db=get_postgres_db(), registry=registry, published_only=True) (or Team.load) rehydrates the published config at import time; it returns None for an archived or unpublished component, and a Case with neither field set raises — guard the load or the whole suite fails at import. Workflows cannot be case targets.
Before cases that use MCP, inspect evals/__main__.py: it currently collects MCP tools
from OS Manager. Ensure the target's required MCP tools are connected in the eval loop;
API-side connections do not carry into a separate CLI process. Use the pinned framework
collection helpers in the existing entrypoint when additional target tools are needed.
2. Map what it promises
Read the file (or the published config: get_postgres_db().get_config(component_id="<id>")["config"]). Every "always", "never", "use X for Y" is a case. Note the tools for reliability assertions.
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.
- 3d ago Changed · -5 tokens per session fc2bb1d1decb
- 6d ago Changed · +9 lines · +4 tokens per session a1e3fb78d260
- 2mo ago First seen · 79 lines · 89 tokens per session scan A 026e02916d3e
create-evals is a skill published in the GitHub repository agno-agi/agentos-modal (3 stars, last pushed today), licensed Apache-2.0. It adds 88 tokens to every session and 1,323 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to create-evals, differing in 0 lines, and is treated as a copy.
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