lettuce-skill

lettuce-skill is a skill for Claude Code, Codex from LambdaTest/agent-skills. It costs 68 tokens per session (850 once invoked), scanned A, original, MIT.

A helper for writing Lettuce behavior-driven tests in Python. Behavior-driven development describes software behavior in plain-language feature files, then connects each step to Python code; Lettuce is an old, largely unmaintained framework.

In plain words
What is it for?
Use it to maintain or generate legacy Python tests with Given/When/Then scenarios, browser actions, and step definitions.
Why use it?
It gives existing Lettuce projects the expected feature-file and step-definition patterns, while warning that Behave is a better choice for new projects.

Skill for Claude CodeCodex

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

Good fit Use it to maintain or generate legacy Python tests with Given/When/Then scenarios, browser actions, and step definitions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lambdatest/agent-skills/lettuce-skill
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 LambdaTest/agent-skills --skill lettuce-skill
Clone the repo
git clone --depth 1 https://github.com/LambdaTest/agent-skills

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 lettuce-skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/lambdatest/agent-skills/lettuce-skill/github.svg)](https://agentmods.dev/skills/lambdatest/agent-skills/lettuce-skill)
Your own site
<a href="https://agentmods.dev/skills/lambdatest/agent-skills/lettuce-skill"><img src="https://agentmods.dev/badge/skills/lambdatest/agent-skills/lettuce-skill/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 lettuce-skill

Your own site · 80×15
<a href="https://agentmods.dev/skills/lambdatest/agent-skills/lettuce-skill"><img src="https://agentmods.dev/badge/skills/lambdatest/agent-skills/lettuce-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 850 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 pass 7 Sept 2026
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 $0.00068 $0.00850
Opus 5 $0.00034 $0.00425
Sonnet 5 $0.00014 $0.00170
Haiku 4.5 $0.00007 $0.00085

Measured 10d ago against content hash 2b0137c065f3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

lettuce-skill 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 10d 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.

lettuce-skill/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Lettuce BDD Skill (Legacy)

Note: Lettuce is largely unmaintained. For new Python BDD projects, use Behave instead.

Core Patterns

Feature File (features/login.feature)

Feature: User Login
  Scenario: Successful login
    Given I navigate to the login page
    When I enter "[email protected]" as email
    And I enter "password123" as password
    And I click login
    Then I should see the dashboard

  Scenario: Invalid login
    Given I navigate to the login page
    When I enter "[email protected]" as email
    And I enter "wrong" as password
    And I click login
    Then I should see "Invalid credentials"

Step Definitions (features/steps.py)

from lettuce import step, world
from selenium import webdriver
from selenium.webdriver.common.by import By

@step(r'I navigate to the login page')
def navigate_to_login(step):
    world.browser = webdriver.Chrome()
    world.browser.get(world.base_url + '/login')

@step(r'I enter "([^"]*)" as email')
def enter_email(step, email):
    el = world.browser.find_element(By.ID, 'email')
    el.clear()
    el.send_keys(email)

@step(r'I enter "([^"]*)" as password')
def enter_password(step, password):
    el = world.browser.find_element(By.ID, 'password')
    el.clear()
    el.send_keys(password)

@step(r'I click login')
def click_login(step):
    world.browser.find_element(By.CSS_SELECTOR, 'button[type="submit"]').click()

@step(r'I should see the dashboard')
def see_dashboard(step):
    assert '/dashboard' in world.browser.current_url

@step(r'I should see "([^"]*)"')
def see_text(step, text):
    assert text in world.browser.page_source

Terrain (Setup/Teardown — terrain.py)

from lettuce import before, after, world

@before.all
def setup():
    world.base_url = 'http://localhost:3000'

@before.each_scenario
def before_scenario(scenario):
    pass

@after.each_scenario
def cleanup(scenario):
    if hasattr(world, 'browser'):
        world.browser.quit()

@after.all
def teardown(total):
    print(f"Ran {total.scenarios_ran} scenarios")

Read the full file on GitHub · 137 lines

Files

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.

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. 10d ago First seen · 137 lines · 68 tokens per session scan A 2b0137c065f3

Subscribe to this mod's changes

lettuce-skill is a skill published in the GitHub repository LambdaTest/agent-skills (366 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 850 once invoked, about $0.0003 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 2026-08-30.

Related

Other skills, from other repositories

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

tika-eval-compare

Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".

apache/tika · 50 tokens

neuron-evaluation-engineer

Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…

neuron-core/neuron-ai · 77 tokens

jetson-validate-image

Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.

NVIDIA/skills · 50 tokens

atmos-validation

Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.

cloudposse/atmos · 31 tokens

skill-benchmark

Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.

HoangNguyen0403/agent-skills-standard · 16 tokens