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 Intai/story-flow --skill learn-bdd-scenariosgit clone --depth 1 https://github.com/Intai/story-flowWrote 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/intai/story-flow/learn-bdd-scenarios)<a href="https://agentmods.dev/skills/intai/story-flow/learn-bdd-scenarios"><img src="https://agentmods.dev/badge/skills/intai/story-flow/learn-bdd-scenarios/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/intai/story-flow/learn-bdd-scenarios"><img src="https://agentmods.dev/badge/skills/intai/story-flow/learn-bdd-scenarios.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00022 | $0.02113 |
| Opus 5 | $0.00011 | $0.01056 |
| Sonnet 5 | $0.00004 | $0.00423 |
| Haiku 4.5 | $0.00002 | $0.00211 |
Grade A, and why
Learn what defines effective BDD scenarios 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 11d 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 — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What Defines Effective, Complete BDD Scenarios
Overview
This learning module helps junior developers understand how to write BDD (Behavior-Driven Development) scenarios that are effective for story-flow's automated testing workflow.
Instructions
Present the following content interactively. After each section, use AskUserQuestion to offer 3 options:
- "Continue to next section"
- "Show more examples"
- "I have a question"
Section 1: The Purpose of BDD Scenarios
Explain that in story-flow, BDD scenarios serve two purposes:
- Executable specifications - Claude uses Playwright MCP to execute these scenarios directly in a browser
- Regression test generation - With
--recordflag, scenarios become Playwright.spec.jsfiles
This means scenarios must be:
- Precise enough for automated execution
- Complete enough to verify the feature works correctly
- Structured correctly so Claude can follow them step-by-step
Section 2: Anatomy of an Effective BDD Scenario
Present this structure with explanations:
Feature: [Feature name matching the story]
Background:
# Shared setup steps that run before EVERY scenario
Given I am logged in as "[email protected]"
And I am on the settings page
@purge-data
Scenario: FEAT-01: [Clear, descriptive title]
# Given - Initial state/context
Given there are 3 images in the gallery
# When - The action being tested
When I click the "Delete" button on the first image
And I confirm the deletion in the dialog
# Then - Expected outcome (assertions)
Then I should see 2 images in the gallery
And the deleted image should not be visible
And the image should be removed from S3 bucket
Key elements:
| Element | Purpose | Example |
|---|---|---|
Feature: |
Groups related scenarios | Feature: Image Gallery Management |
Background: |
Shared setup (runs before each scenario) | Login, navigation, seed data |
@tags |
Control execution behavior, or label tests for filtering | @purge-data, @screenshots, @staging |
Scenario: ID: |
Unique identifier + descriptive title | FEAT-01: Delete single image |
Given |
Preconditions/initial state | Given there are 3 images |
When |
User actions | When I click "Delete" |
Then |
Expected outcomes | Then I should see 2 images |
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.
- 11d ago First seen · 287 lines · 22 tokens per session scan A 398da2361727
Learn what defines effective BDD scenarios is a skill published in the GitHub repository Intai/story-flow (12 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 2,113 once invoked, about $0.0001 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.
Other skills, from other repositories
test-generation
Generate and run unit/integration tests TDD-style across Python, Node, and .NET. Use when adding tests to untested code, implementing a feature test-first, or finding coverage gaps.
test-generation
Use when the user asks for tests, mentions TDD, or when new code has been written and needs test coverage.
setup
Set up or update TDD Guard for the current project. Detects the test framework, installs or updates the matching reporter, and configures or migrates its configuration to match the current specification.
Vibe Coding Mastery
The complete operating system for building software with AI. From first prompt to production deployment — prompting frameworks, architecture patterns, testing strategies, debugging playbooks, and production graduation checklists. Works with Claude Code, Cursor, Windsurf, Copilot, and any AI coding tool.
django-tdd
Django testing strategies with pytest-django, TDD methodology, factoryboy, mocking, coverage, and testing Django REST Framework APIs.
golang-testing
A guide to testing Go programs, including test-driven development (TDD), table-driven tests, coverage, and debugging. TDD means writing a failing test before implementing the code that makes it pass.