
promptfoo-evaluation
Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".
- Installs
- 1.6K
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/daymade/claude-code-skills --skill promptfoo-evaluation- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads promptfoo-evaluation automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Promptfoo Evaluation
Overview
This skill provides guidance for configuring and running LLM evaluations using Promptfoo, an open-source CLI tool for testing and comparing LLM outputs.
Quick Start
# Initialize a new evaluation project
npx promptfoo@latest init
# Run evaluation
npx promptfoo@latest eval
# View results in browser
npx promptfoo@latest view
Configuration Structure
A typical Promptfoo project structure:
project/
├── promptfooconfig.yaml # Main configuration
├── prompts/
│ ├── system.md # System prompt
│ └── chat.json # Chat format prompt
├── tests/
│ └── cases.yaml # Test cases
└── scripts/
└── metrics.py # Custom Python assertions
Core Configuration (promptfooconfig.yaml)
# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
description: "My LLM Evaluation"
# Prompts to test
prompts:
- file://prompts/system.md
- file://prompts/chat.json
# Models to compare
providers:
- id: anthropic:messages:claude-sonnet-4-6
label: Claude-Sonnet-4.6
- id: openai:gpt-4.1
label: GPT-4.1
# Test cases
tests: file://tests/cases.yaml
# Concurrency control (MUST be under commandLineOptions, NOT top-level)
commandLineOptions:
maxConcurrency: 2
# Default assertions for all tests
defaultTest:
assert:
- type: python
value: file://scripts/metrics.py:custom_assert
- type: llm-rubric
value: |
Evaluate the response quality on a 0-1 scale.
threshold: 0.7
# Output path
outputPath: results/eval-results.json
Prompt Formats
Text Prompt (system.md)
You are a helpful assistant.
Task: {{task}}
Context: {{context}}
Chat Format (chat.json)
[
{"role": "system", "content": "{{system_prompt}}"},
{"role": "user", "content": "{{user_input}}"}
]
Few-Shot Pattern
Embed examples directly in prompt or use chat format with assistant messages:
[
{"role": "system", "content": "{{system_prompt}}"},
{"role": "user", "content": "Example input: {{example_input}}"},
{"role": "assistant", "content": "{{example_output}}"},
{"role": "user", "content": "Now process: {{actual_input}}"}
]
Test Cases (tests/cases.yaml)
- description: "Test case 1"
vars:
system_prompt: file://prompts/system.md
user_input: "Hello world"
# Load content from files
context: file://data/context.txt
assert:
- type: contains
value: "expected text"
- type: python
value: file://scripts/metrics.py:custom_check
threshold: 0.8
Python Custom Assertions
Create a Python file for custom assertions (e.g., scripts/metrics.py):
def get_assert(output: str, context: dict) -> dict:
"""Default assertion function."""
vars_dict = context.get('vars', {})
# Access test variables
expected = vars_dict.get('expected', '')
# Return result
return {
"pass": expected in output,
"score": 0.8,
"reason": "Contains expected content",
"named_scores": {"relevance": 0.9}
}
def custom_check(output: str, context: dict) -> dict:
"""Custom named assertion."""
word_count = len(output.split())
passed = 100 <= word_count <= 500
return {
"pass": passed,
"score": min(1.0, word_count / 300),
"reason": f"Word count: {word_count}"
}
Key points:
- Default function name is
get_assert - Specify function with
file://path.py:function_name - Return
bool,float(score), ordictwith pass/score/reason - Access variables via
context['vars']
LLM-as-Judge (llm-rubric)
assert:
- type: llm-rubric
value: |
Evaluate the response based on:
1. Accuracy of information
2. Clarity of explanation
3. Completeness
Score 0.0-1.0 where 0.7+ is passing.
threshold: 0.7
provider: openai:gpt-4.1 # Optional: override grader model
When using a relay/proxy API, each llm-rubric assertion needs its own provider config with apiBaseUrl. Otherwise the grader falls back to the default Anthropic/OpenAI endpoint and gets 401 errors:
assert:
- type: llm-rubric
value: |
Evaluate quality on a 0-1 scale.
threshold: 0.7
provider:
id: anthropic:messages:claude-sonnet-4-6
config:
apiBaseUrl: https://your-relay.example.com/api
Best practices:
- Provide clear scoring criteria
- Use
thresholdto set minimum passing score - Default grader uses available API keys (OpenAI → Anthropic → Google)
- When using relay/proxy: every
llm-rubricmust have its ownproviderwithapiBaseUrl— the main provider'sapiBaseUrlis NOT inherited
Common Assertion Types
| Type | Usage | Example |
|---|---|---|
contains | Check substring | value: "hello" |
icontains | Case-insensitive | value: "HELLO" |
equals | Exact match | value: "42" |
regex | Pattern match | value: "\\d{4}" |
python | Custom logic | value: file://script.py |
llm-rubric | LLM grading | value: "Is professional" |
latency | Response time | threshold: 1000 |
File References
All file:// paths are resolved relative to promptfooconfig.yaml location (NOT the YAML file containing the reference). This is a common gotcha when tests: references a separate YAML file — the file:// paths inside that test file still resolve from the config root.
# Load file content as variable
vars:
content: file://data/input.txt
# Load prompt from file
prompts:
- file://prompts/main.md
# Load test cases from file
tests: file://tests/cases.yaml
# Load Python assertion
assert:
- type: python
value: file://scripts/check.py:validate
Running Evaluations
# Basic run
npx promptfoo@latest eval
# With specific config
npx promptfoo@latest eval --config path/to/config.yaml
# Output to file
npx promptfoo@latest eval --output results.json
# Filter tests
npx promptfoo@latest eval --filter-metadata category=math
# View results
npx promptfoo@latest view
Relay / Proxy API Configuration
When using an API relay or proxy instead of direct Anthropic/OpenAI endpoints:
providers:
- id: anthropic:messages:claude-sonnet-4-6
label: Claude-Sonnet-4.6
config:
max_tokens: 4096
apiBaseUrl: https://your-relay.example.com/api # Promptfoo appends /v1/messages
# CRITICAL: maxConcurrency MUST be under commandLineOptions (NOT top-level)
commandLineOptions:
maxConcurrency: 1 # Respect relay rate limits
Key rules:
apiBaseUrlgoes inproviders[].config— Promptfoo appends/v1/messagesautomaticallymaxConcurrencymust be undercommandLineOptions:— placing it at top level is silently ignored- When using relay with LLM-as-judge, set
maxConcurrency: 1to avoid concurrent request limits (generation + grading share the same pool) - Pass relay token as
ANTHROPIC_API_KEYenv var
Troubleshooting
Python not found:
export PROMPTFOO_PYTHON=python3
Large outputs truncated:
Outputs over 30000 characters are truncated. Use head_limit in assertions.
File not found errors:
All file:// paths resolve relative to promptfooconfig.yaml location.
maxConcurrency ignored (shows "up to N at a time"):
maxConcurrency must be under commandLineOptions:, not at the YAML top level. This is a common mistake.
LLM-as-judge returns 401 with relay API:
Each llm-rubric assertion must have its own provider with apiBaseUrl. The main provider config is not inherited by grader assertions.
HTML tags in model output inflating metrics:
Models may output <br>, <b>, etc. in structured content. Strip HTML in Python assertions before measuring:
import re
clean_text = re.sub(r'<[^>]+>', '', raw_text)
Echo Provider (Preview Mode)
Use the echo provider to preview rendered prompts without making API calls:
# promptfooconfig-preview.yaml
providers:
- echo # Returns prompt as output, no API calls
tests:
- vars:
input: "test content"
Use cases:
- Preview prompt rendering before expensive API calls
- Verify Few-shot examples are loaded correctly
- Debug variable substitution issues
- Validate prompt structure
# Run preview mode
npx promptfoo@latest eval --config promptfooconfig-preview.yaml
Cost: Free - no API tokens consumed.
Advanced Few-Shot Implementation
Multi-turn Conversation Pattern
For complex few-shot learning with full examples:
[
{"role": "system", "content": "{{system_prompt}}"},
// Few-shot Example 1
{"role": "user", "content": "Task: {{example_input_1}}"},
{"role": "assistant", "content": "{{example_output_1}}"},
// Few-shot Example 2 (optional)
{"role": "user", "content": "Task: {{example_input_2}}"},
{"role": "assistant", "content": "{{example_output_2}}"},
// Actual test
{"role": "user", "content": "Task: {{actual_input}}"}
]
Test case configuration:
tests:
- vars:
system_prompt: file://prompts/system.md
# Few-shot examples
example_input_1: file://data/examples/input1.txt
example_output_1: file://data/examples/output1.txt
example_input_2: file://data/examples/input2.txt
example_output_2: file://data/examples/output2.txt
# Actual test
actual_input: file://data/test1.txt
Best practices:
- Use 1-3 few-shot examples (more may dilute effectiveness)
- Ensure examples match the task format exactly
- Load examples from files for better maintainability
- Use echo provider first to verify structure
Long Text Handling
For Chinese/long-form content evaluations (10k+ characters):
Configuration:
providers:
- id: anthropic:messages:claude-sonnet-4-6
config:
max_tokens: 8192 # Increase for long outputs
defaultTest:
assert:
- type: python
value: file://scripts/metrics.py:check_length
Python assertion for text metrics:
import re
def strip_tags(text: str) -> str:
"""Remove HTML tags for pure text."""
return re.sub(r'<[^>]+>', '', text)
def check_length(output: str, context: dict) -> dict:
"""Check output length constraints."""
raw_input = context['vars'].get('raw_input', '')
input_len = len(strip_tags(raw_input))
output_len = len(strip_tags(output))
reduction_ratio = 1 - (output_len / input_len) if input_len > 0 else 0
return {
"pass": 0.7 <= reduction_ratio <= 0.9,
"score": reduction_ratio,
"reason": f"Reduction: {reduction_ratio:.1%} (target: 70-90%)",
"named_scores": {
"input_length": input_len,
"output_length": output_len,
"reduction_ratio": reduction_ratio
}
}
Real-World Example
Project: Chinese short-video content curation from long transcripts
Structure:
tiaogaoren/
├── promptfooconfig.yaml # Production config
├── promptfooconfig-preview.yaml # Preview config (echo provider)
├── prompts/
│ ├── tiaogaoren-prompt.json # Chat format with few-shot
│ └── v4/system-v4.md # System prompt
├── tests/cases.yaml # 3 test samples
├── scripts/metrics.py # Custom metrics (reduction ratio, etc.)
├── data/ # 5 samples (2 few-shot, 3 eval)
└── results/
See: ./tiaogaoren/ (example project root) for full implementation.
Resources
For detailed API reference and advanced patterns, see references/promptfoo_api.md.
FAQ
What does the promptfoo-evaluation skill do?
Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".
How do I install promptfoo-evaluation?
Run npx skills add https://github.com/daymade/claude-code-skills --skill promptfoo-evaluation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/promptfoo-evaluation/ (personal) or .claude/skills/promptfoo-evaluation/ (project).
Which AI agents can use promptfoo-evaluation?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is promptfoo-evaluation safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains promptfoo-evaluation?
It is published by daymade in daymade/claude-code-skills. It has 1,550 recorded installs.
More from daymade/claude-code-skills
twitter-reader
daymade/claude-code-skills
Fetches Twitter/X post and Article content — text, author, engagement metrics and embedded media — downloading images locally and generating complete Markdown with image references. Use when retrieving a tweet's text or an X Article with images, or pulling post metadata for a Markdown/PKM record. Preferred over Jina alone for X Articles with images.
i18n-expert
daymade/claude-code-skills
This skill should be used when setting up, auditing, or enforcing internationalization/localization in UI codebases (React/TS, i18next or similar, JSON locales), including installing/configuring the i18n framework, replacing hard-coded strings, ensuring en-US/zh-CN coverage, mapping error codes to localized messages, and validating key parity, pluralization, and formatting.
prompt-optimizer
daymade/claude-code-skills
Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt). Use when requirements lack triggers or measurable outcomes, or the user asks to "optimize my prompt" / "improve this requirement" / "make this more specific".
ppt-creator
daymade/claude-code-skills
Deprecated presentation entry retained as a compatibility notice. Use to check the retirement status of ppt-creator or identify a legacy installation. This package does not generate PPTX files.
qa-expert
daymade/claude-code-skills
Sets up QA testing processes: test strategies, Google Testing Standards test cases, P0-P4 bug tracking, quality metrics, progress reports, autonomous execution via master prompts, and OWASP security testing, with third-party QA handoff docs. Use when establishing QA infrastructure, writing test cases, executing test plans, or tracking bugs. Not for auditing an already-rendered UI (use frontend-visual-qa).
macos-cleaner
daymade/claude-code-skills
Diagnoses and safely reclaims macOS disk space: caches, logs, app remnants, large/duplicate files, Docker/OrbStack, Chromium code-sign clones, developer caches. Use when a Mac is low on storage, shows “Caching needs more space”, or before deleting anything to avoid data loss. Not for Windows/Linux or permission issues (use macos-permissions).
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available