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 sammcj/agentic-coding --skill deepevalgit clone --depth 1 https://github.com/sammcj/agentic-codingWrote 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/sammcj/agentic-coding/deepeval)<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.
<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>- NVIDIA SkillSpector warn
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.
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 | $0.00019 | $0.03301 |
| Opus 5.5 | $0.00008 | $0.01320 |
| Sonnet 5.5 | $0.00004 | $0.00660 |
| Haiku 4.5 | $0.00002 | $0.00330 |
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.
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
])
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.
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 · 511 lines · 19 tokens per session scan A bf7104075474
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.
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