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".
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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".
zh_description
用于promptfoo、评估，支持开发、调试、评审和交付。
version
1.0.0
author
seaworld008
source
in-house
source_url
tags
["development", "evaluation", "promptfoo"]
created_at
2026-03-04
updated_at
2026-03-20
quality
5
complexity
intermediate
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.jsondescription:"My LLM Evaluation"# Prompts to testprompts:-file://prompts/system.md-file://prompts/chat.json# Models to compareproviders:-id:anthropic:messages:claude-sonnet-4-6label:Claude-Sonnet-4.6-id:openai:gpt-4.1label:GPT-4.1# Test casestests:file://tests/cases.yaml# Concurrency control (MUST be under commandLineOptions, NOT top-level)commandLineOptions:maxConcurrency:2# Default assertions for all testsdefaultTest:
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}}
-description:"Test case 1"vars:system_prompt:file://prompts/system.mduser_input:"Hello world"# Load content from filescontext:file://data/context.txtassert:-type:containsvalue:"expected text"-type:pythonvalue:file://scripts/metrics.py:custom_checkthreshold:0.8
Python Custom Assertions
Create a Python file for custom assertions (e.g., scripts/metrics.py):
Specify function with file://path.py:function_name
Return bool, float (score), or dict with pass/score/reason
Access variables via context['vars']
LLM-as-Judge (llm-rubric)
assert:-type:llm-rubricvalue:|
Evaluate the response based on:
1. Accuracy of information
2. Clarity of explanation
3. Completeness
Score0.0-1.0where0.7+ispassing.threshold:0.7provider: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-rubricvalue:|
Evaluate quality on a 0-1 scale.
threshold:0.7provider:id:anthropic:messages:claude-sonnet-4-6config:apiBaseUrl:https://your-relay.example.com/api
Best practices:
Provide clear scoring criteria
Use threshold to set minimum passing score
Default grader uses available API keys (OpenAI → Anthropic → Google)
When using relay/proxy: every llm-rubric must have its own provider with apiBaseUrl — the main provider's apiBaseUrl is 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 variablevars:content:file://data/input.txt# Load prompt from fileprompts:-file://prompts/main.md# Load test cases from filetests:file://tests/cases.yaml# Load Python assertionassert:-type:pythonvalue: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-4-6label:Claude-Sonnet-4.6config:max_tokens:4096apiBaseUrl: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:
apiBaseUrl goes in providers[].config — Promptfoo appends /v1/messages automatically
maxConcurrency must be under commandLineOptions: — placing it at top level is silently ignored
When using relay with LLM-as-judge, set maxConcurrency: 1 to avoid concurrent request limits (generation + grading share the same pool)
Pass relay token as ANTHROPIC_API_KEY env 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.yamlproviders:-echo# Returns prompt as output, no API callstests:-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 examplesexample_input_1:file://data/examples/input1.txtexample_output_1:file://data/examples/output1.txtexample_input_2:file://data/examples/input2.txtexample_output_2:file://data/examples/output2.txt# Actual testactual_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-4-6config:max_tokens:8192# Increase for long outputsdefaultTest:assert:-type:pythonvalue:file://scripts/metrics.py:check_length
