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 skills add djscheuf/agentic-dev-ecosystem-template --skill promptfoogit clone --depth 1 https://github.com/djscheuf/agentic-dev-ecosystem-templateWrote 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/djscheuf/agentic-dev-ecosystem-template/promptfoo)<a href="https://agentmods.dev/skills/djscheuf/agentic-dev-ecosystem-template/promptfoo"><img src="https://agentmods.dev/badge/skills/djscheuf/agentic-dev-ecosystem-template/promptfoo/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/djscheuf/agentic-dev-ecosystem-template/promptfoo"><img src="https://agentmods.dev/badge/skills/djscheuf/agentic-dev-ecosystem-template/promptfoo.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 | $0.00039 | $0.00816 |
| Opus 5.5 | $0.00016 | $0.00326 |
| Sonnet 5.5 | $0.00008 | $0.00163 |
| Haiku 4.5 | $0.00004 | $0.00082 |
Grade A, and why
promptfoo 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.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Promptfoo Knowledge Skill
Use this skill when the task involves creating, editing, or debugging promptfoo configuration (promptfooconfig.yaml), providers, custom evaluation scripts, or CLI workflows.
When to Run
- Setting up a new promptfoo eval or project
- Configuring providers (built-in, local, or custom)
- Writing or debugging custom JavaScript/TypeScript/Python providers
- Writing or debugging custom assertions or transforms
- Optimizing prompts or comparing models
- Troubleshooting failed evals, provider errors, or OOM/timeouts
- Converting between CLI, YAML, and Node-package usage
What Promptfoo Is
promptfoo is an open-source CLI and library for evaluating and red-teaming LLM applications. It runs prompts against one or more providers (LLM APIs), compares outputs, and scores them with assertions and metrics. The core idea is test-driven prompt engineering: define expectations first, then iterate.
Mental Model
prompts × providers × tests = matrix of outputs
|
v
assertions & metrics
- Prompts: text, chat JSON, or dynamically generated templates
- Providers: LLM targets (OpenAI, Anthropic, local Ollama, custom JS/Python, HTTP, etc.)
- Tests: input variables + assertions that define pass/fail criteria
- Config: a single
promptfooconfig.yamlbinds them together
Phase Map
Read phases in order when learning the tool; jump directly to a phase when the task is focused.
| Phase | File | Covers |
|---|---|---|
| 0 | SKILL.md |
Index, when-to-use, mental model (you are here) |
| 1 | phases/01-overview.md |
Philosophy, workflow, key concepts |
| 2 | phases/02-quickstart-workflow.md |
Install, init, eval, view, .env |
| 3 | phases/03-config-reference.md |
promptfooconfig.yaml structure and properties |
| 4 | phases/04-prompts.md |
Text, file, chat, dynamic, Nunjucks |
| 5 | phases/05-providers.md |
Built-in providers, syntax, configuration, env vars |
| 6 | phases/06-assertions-metrics.md |
Deterministic, model-graded, custom, derived, named |
| 7 | phases/07-custom-providers.md |
JS/TS/Python provider interfaces and return contracts |
| 8 | phases/08-evaluation-scripts.md |
Custom asserts, transforms, Node package API |
| 9 | phases/09-cli-commands.md |
CLI commands, environment variables, CI behavior |
| 10 | phases/10-debugging-scenarios.md |
Troubleshooting, logs, OOM, stuck evals, custom-provider debugging |
| QR | quick-reference.md |
Compact cheat sheet |
What ships with it
11 files 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.
- phases/01-overview.md 3.0 KB
- phases/02-quickstart-workflow.md 2.3 KB
- phases/03-config-reference.md 5.3 KB
- phases/04-prompts.md 4.2 KB
- phases/05-providers.md 4.1 KB
- phases/06-assertions-metrics.md 5.0 KB
- phases/07-custom-providers.md 6.6 KB
- phases/08-evaluation-scripts.md 4.9 KB
- phases/09-cli-commands.md 6.5 KB
- phases/10-debugging-scenarios.md 4.8 KB
- quick-reference.md 4.6 KB
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 · 65 lines · 39 tokens per session scan A 50aed76e72e4
promptfoo is a skill published in the GitHub repository djscheuf/agentic-dev-ecosystem-template (12 stars, last pushed 20d ago), licensed MIT. It adds 39 tokens to every session and 816 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-13.
Other skills, from other repositories
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
edd
Eval-driven development loop for this repo (rag-loop, an aircraft-systems RAG app on LangGraph, OpenAI, Qdrant Cloud, and TruLens). Use when the user wants to run an experiment, inspect feedback metrics, or decide keep-or-reject on a config/prompt change.
writing-evals
Scaffolds evaluation suites for the Axiom AI SDK. Generates eval files, scorers, flag schemas, and config from natural-language descriptions. Use when creating evals, writing scorers, setting up flag schemas, or configuring axiom.config.ts.
migrate-etl
Stabilize an ETL code unit (SSIS, Informatica, or other platforms) by handing the converted artifacts off to etl-stabilization and recording the result on the registry entry.
ai-observability-promptfoo
Testing and evaluation framework for LLM prompts and applications -- promptfooconfig.yaml, assertions, model-graded evals, red teaming, CI/CD integration, custom providers, and comparative evaluation.
evo-simpo-loss
Implement the SimPO (Simple Preference Optimization) loss function for the SimPOTrainer class. Use when reproducing the SimPO paper's loss computation, injecting the simpoloss method into simpotrainer.py, running unit tests, and generating validated loss outputs.