Claude Code Skills Marketplace is a collection and marketplace of skills, plugins, agents, and instructions that extend Claude Code with specialized development workflows. It is for developers who want to install existing workflows or create, validate, and package their own Claude Code skills.
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 daymade/claude-code-skills --skill promptfoo-evaluationgit clone --depth 1 https://github.com/daymade/claude-code-skillsWrote 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/daymade/claude-code-skills/promptfoo-evaluation)<a href="https://agentmods.dev/skills/daymade/claude-code-skills/promptfoo-evaluation"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/promptfoo-evaluation/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/daymade/claude-code-skills/promptfoo-evaluation"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/promptfoo-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 9 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 16 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 19 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 22 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 242 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 245 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 248 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 251 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 254 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 328 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00086 | $0.03156 |
| Opus 5 | $0.00043 | $0.01578 |
| Sonnet 5 | $0.00017 | $0.00631 |
| Haiku 4.5 | $0.00009 | $0.00316 |
Grade A, and why
promptfoo-evaluation 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- promptfoo-evaluation — 94% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 448 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
What ships with it
2 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.
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.
- 5d ago First seen · 448 lines · 86 tokens per session scan A dfe42fda246f
promptfoo-evaluation is a skill published in the GitHub repository daymade/claude-code-skills (1,384 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 3,156 once invoked, about $0.0004 per session on Opus 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 2026-09-03.
Other skills, from other repositories
darwinian-evolver
Evolve prompts/regex/SQL/code with Imbue's evolution loop.
prompt-regression
Use when the user has changed a prompt (system prompt, RAG template, agent instruction, etc.) and wants to know whether the candidate is better or worse than the baseline. Also use when the user mentions prompt A/B testing, prompt comparison, prompt optimization validation, "did my prompt change help," or prompt…
llm-patterns
AI-first application patterns, LLM testing, prompt management.
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.
prompt-testing
Use when comparing two prompt variants, defining quality/efficiency/robustness metrics, or deciding whether to adopt a challenger prompt over a baseline.
recipe-eval-prompt
Compares original and optimized prompts through repeated blind paired execution in git worktrees. Use when evaluating prompt improvement effects or learning prompt engineering through concrete examples.