deepeval

deepeval is a skill for Claude Code, Codex from sammcj/agentic-coding. It costs 19 tokens per session (3,301 once invoked), scanned A, original, Apache-2.0.

A Python testing framework for checking the quality of applications that use large language models. It provides measures for areas such as retrieval-augmented generation, conversations, agents, relevance, safety, and custom criteria.

In plain words
What is it for?
Writing automated evaluations for chatbots, retrieval systems, and AI agents, running them with pytest, and checking outputs against chosen quality measures.
Why use it?
It helps developers test AI behaviour systematically instead of judging every response by hand.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Writing automated evaluations for chatbots, retrieval systems, and AI agents, running them with pytest, and checking outputs against chosen quality measures.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sammcj/agentic-coding/deepeval
Install

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.

Any agent
npx skills add sammcj/agentic-coding --skill deepeval
Clone the repo
git clone --depth 1 https://github.com/sammcj/agentic-coding

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for deepeval

README.md
[![agentmods](https://agentmods.dev/badge/skills/sammcj/agentic-coding/deepeval/github.svg)](https://agentmods.dev/skills/sammcj/agentic-coding/deepeval)
Your own site
<a href="https://agentmods.dev/skills/sammcj/agentic-coding/deepeval"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/deepeval/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.

agentmods 80×15 button for deepeval

Your own site · 80×15
<a href="https://agentmods.dev/skills/sammcj/agentic-coding/deepeval"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/deepeval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,301 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

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 →

  • high Privilege Escalation · line 49
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.03301
Opus 5.5 $0.00008 $0.01320
Sonnet 5.5 $0.00004 $0.00660
Haiku 4.5 $0.00002 $0.00330

Measured 1mo ago against content hash bf7104075474, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

deepeval 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.

Skills_disabled/deepeval/SKILL.md · 511 lines

How it starts

The opening of the file, as written. The whole thing — 511 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DeepEval

Overview

DeepEval is a pytest-based framework for testing LLM applications. It provides 50+ evaluation metrics covering RAG pipelines, conversational AI, agents, safety, and custom criteria. DeepEval integrates into development workflows through pytest, supports multiple LLM providers, and includes component-level tracing with the @observe decorator.

Repository: https://github.com/confident-ai/deepeval Documentation: https://deepeval.com

Installation

pip install -U deepeval

Requires Python 3.9+.

Quick Start

Basic pytest test

import pytest
from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric

def test_chatbot():
    metric = AnswerRelevancyMetric(threshold=0.7, model="athropic-claude-sonnet-4-5")
    test_case = LLMTestCase(
        input="What if these shoes don't fit?",
        actual_output="You have 30 days for full refund"
    )
    assert_test(test_case, [metric])

Run with: deepeval test run test_chatbot.py

Environment setup

DeepEval automatically loads .env.local then .env:

# .env
OPENAI_API_KEY="sk-..."

Core Workflows

RAG Evaluation

Evaluate both retrieval and generation phases:

from deepeval.metrics import (
    ContextualPrecisionMetric,
    ContextualRecallMetric,
    ContextualRelevancyMetric,
    AnswerRelevancyMetric,
    FaithfulnessMetric
)

# Retrieval metrics
contextual_precision = ContextualPrecisionMetric(threshold=0.7)
contextual_recall = ContextualRecallMetric(threshold=0.7)
contextual_relevancy = ContextualRelevancyMetric(threshold=0.7)

# Generation metrics
answer_relevancy = AnswerRelevancyMetric(threshold=0.7)
faithfulness = FaithfulnessMetric(threshold=0.8)

test_case = LLMTestCase(
    input="What are the side effects of aspirin?",
    actual_output="Common side effects include stomach upset and nausea.",
    expected_output="Aspirin side effects include gastrointestinal issues.",
    retrieval_context=[
        "Aspirin common side effects: stomach upset, nausea, vomiting.",
        "Serious aspirin side effects: gastrointestinal bleeding.",
    ]
)

evaluate(test_cases=[test_case], metrics=[
    contextual_precision, contextual_recall, contextual_relevancy,
    answer_relevancy, faithfulness
])

Read the full file on GitHub · 511 lines

Files

What ships with it

4 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.

Changes

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.

  1. 1mo ago First seen · 511 lines · 19 tokens per session scan A bf7104075474

Subscribe to this mod's changes

deepeval is a skill published in the GitHub repository sammcj/agentic-coding (165 stars, last pushed yesterday), licensed Apache-2.0. It adds 19 tokens to every session and 3,301 once invoked, about $0.0001 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 2026-09-03.