Borrowing it
Nothing to install: this file belongs to ali-master/audit. 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/ali-master/audit/main/.agents/skills/promptfoo-evals/SKILL.mdgit clone --depth 1 https://github.com/ali-master/auditWrote 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/ali-master/audit/promptfoo-evals)<a href="https://agentmods.dev/skills/ali-master/audit/promptfoo-evals"><img src="https://agentmods.dev/badge/skills/ali-master/audit/promptfoo-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/ali-master/audit/promptfoo-evals"><img src="https://agentmods.dev/badge/skills/ali-master/audit/promptfoo-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 | $0.00079 | $0.01671 |
| Opus 5.5 | $0.00032 | $0.00668 |
| Sonnet 5.5 | $0.00016 | $0.00334 |
| Haiku 4.5 | $0.00008 | $0.00167 |
Grade A, and why
promptfoo-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 1mo 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 promptfoo-evals — 25 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Promptfoo Evals
You produce maintainable promptfoo eval suites: clear test cases, deterministic assertions where possible, model-graded only when needed.
See references/cheatsheet.md for the full assertion and provider reference.
For deep questions about promptfoo features, consult https://www.promptfoo.dev/llms-full.txt
Inputs (infer from repo context if not provided)
- What is being evaluated (prompt, agent, endpoint, RAG pipeline)?
- What are the inputs and outputs (text, JSON, multi-turn chat, tool calls)?
- What does "good" look like (acceptance criteria, failure modes)?
If context is insufficient, scaffold with TODO markers and starter tests.
Workflow
1. Find or create the eval suite
Search for existing configs: promptfooconfig.yaml, promptfooconfig.yml,
or any promptfoo/evals folder. Extend existing suites when possible.
For new suites, use this layout (unless the repo uses another convention):
evals/<suite-name>/
promptfooconfig.yaml
prompts/
tests/
Always add # yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
at the top of config files.
2. Write prompts
- Put prompts in
prompts/*.txt(plain) orprompts/*.json(chat format) - Reference via
file://prompts/main.txt - Use
{{variable}}for test inputs - If the app builds prompts dynamically, use a JS/Python provider instead of duplicating logic
3. Choose providers
Pick the simplest option that matches the real system:
| Scenario | Provider pattern |
|---|---|
| Compare models | openai:chat:gpt-4.1-mini, anthropic:messages:claude-sonnet-4-6 |
| Test an HTTP API | id: https with config.url, config.body, and transformResponse |
| Test local code | file://provider.py or file://provider.js |
| Echo/passthrough | echo (returns prompt as-is, useful for testing assertions) |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 223 lines · 79 tokens per session scan A 9732e06e2b70
promptfoo-evals is a skill published in the GitHub repository ali-master/audit (25 stars, last pushed 4mo ago), licensed MIT. It adds 79 tokens to every session and 1,671 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to promptfoo-evals, differing in 25 lines, and is treated as a copy.
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