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Vc Scenario

CommunityPopular
withkynam
vc-scenario

Generate comprehensive edge cases and test scenarios by decomposing features across 12 dimensions. Use before implementation or testing to catch issues early.

Overview

Publisherwithkynam
Repositoryvibecode-pro-max-kit
Skill namevc-scenario
Stars
1.1K
Forks
234
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by withkynam on GitHub. Read the source before you install it.

Installation

Install the Vc Scenario AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/withkynam/vibecode-pro-max-kit/tree/main/.claude/skills/vc-scenario
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/withkynam/vibecode-pro-max-kit.git /tmp/vibecode-pro-max-kit
mkdir -p .claude/skills
cp -r /tmp/vibecode-pro-max-kit/.claude/skills/vc-scenario .claude/skills/vc-scenario
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vc Scenario in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Vc Scenario on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Vc Scenario is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

vc-scenario — Edge Case & Scenario Explorer

Output style: Follow process/development-protocols/communication-standards.md — answer-first, plain language, no unexplained jargon, TL;DR on long responses.

Decompose any feature or code path across 12 dimensions to surface edge cases, risks, and test targets before implementation begins.

Mode Selection

Choose a mode before generating scenarios. Default is Simple unless a trigger condition applies.

Simple Mode (default)

Generates edge cases from the plan description, checklist item, or approach text provided in the prompt. No subagent spawned.

Use when:

  • The checklist item is self-contained and clearly described
  • Blast radius is narrow (1–2 files, single package)
  • No auth, billing, schema, or external API surface is touched
  • Speed matters and hypothetical coverage is sufficient

Deep Mode

Spawns a research subagent to read the actual source before generating scenarios. Scenarios reference real variable names, real function signatures, and real failure modes visible in the code rather than hypothetical ones.

Trigger conditions (any one):

  • Checklist item modifies an auth, billing, schema, or external API surface
  • Blast radius spans 3+ files or 2+ packages
  • The plan marks the item as HIGH_RISK
  • Caller explicitly requests deep mode

Deep mode subagent steps:

  1. Reads the actual source files being modified (from plan Touchpoints)
  2. Locates and reads existing test files for those files (via grep for import paths or describe blocks)
  3. Reads any Public Contracts affected (from plan's Public Contracts section)
  4. Returns: real function signatures, actual data shapes, existing test coverage gaps, real failure modes visible in the code

The orchestrator then generates scenarios using the research output.

Output quality difference

ModeExample scenario
Simple"What if the input is null?"
Deep"What if creditBalance.available is 0 but creditBalance.pending is positive — does deductCredits(amount) check available-only or available + pending?"

Simple mode surfaces generic edge cases quickly. Deep mode surfaces scenarios that are only discoverable by reading the actual implementation.


When to Use

  • Before implementing complex or stateful features
  • Before writing tests (generates test targets)
  • Risk assessment during planning or code review
  • API design review — surface contract edge cases early

When NOT to Use

  • Trivial single-line changes or cosmetic UI tweaks
  • Already well-tested, stable code with no recent modifications
  • Pure configuration changes with no logic paths

12 Decomposition Dimensions

Not all 12 apply to every feature. Identify relevant dimensions first, then generate scenarios only for those.

#DimensionWhat to Look For
1User Typesadmin, guest, banned, new user, power user, bot/scraper
2Input Extremesempty, null, max length, unicode, special chars, SQL/script injection
3Timingconcurrent access, race conditions, timeout, slow network, retry storms
4Scale0 items, 1 item, 1M items, pagination boundary, cursor wrap
5State Transitionsfirst use, mid-flow abort, resume after crash, partial completion
6Environmentmobile/low-end CPU, no JS, screen reader, proxy/VPN, different timezone/locale
7Error CascadesDB down, API timeout, disk full, OOM, network partition, partial write
8Authorizationexpired token, wrong role, shared/public link, CORS, CSRF, privilege escalation
9Data Integrityduplicate entries, orphan references, encoding mismatch, concurrent schema migration
10Integrationwebhook replay, API version mismatch, third-party outage, contract drift
11ComplianceGDPR deletion request, audit logging gap, data retention, accidental PII exposure
12Business Logicedge pricing (zero/negative), coupon stacking, refund after partial delivery, free tier limits

Workflow

Step 0 — Select mode using the Mode Selection rules above.

Simple mode:

  1. Parse feature description or checklist item from the prompt
  2. Filter dimensions — mark which of the 12 apply; skip irrelevant ones explicitly
  3. Generate 3–5 scenarios per relevant dimension
  4. Categorize severity — Critical / High / Medium / Low
  5. Output as structured table (see format below)
  6. Summarize total scenario count by severity

Deep mode:

  1. Spawn research subagent — pass Touchpoints, Public Contracts, and checklist item text
  2. Subagent returns real function signatures, data shapes, coverage gaps, visible failure modes
  3. Filter dimensions using research output to remove inapplicable ones
  4. Generate 3–5 scenarios per relevant dimension, referencing actual variable/function names
  5. Categorize severity — Critical / High / Medium / Low
  6. Output as structured table annotated with source evidence (file + line where relevant)
  7. Summarize total scenario count by severity

Severity Criteria

LevelMeaning
CriticalData loss, security breach, auth bypass, silent corruption
HighFeature broken for a subset of users, data inconsistency
MediumDegraded UX, recoverable error not surfaced to user
LowMinor visual glitch, non-blocking warning

Output Format

## Scenario Report: [target]

Dimensions analyzed: [list]
Dimensions skipped: [list + reason]

| # | Dimension | Scenario | Severity | Expected Behavior |
|---|-----------|----------|----------|-------------------|
| 1 | Input Extremes | Empty string for required name field | High | Return 400 with field error |
| 2 | Authorization | Expired JWT accessing protected route | Critical | Redirect to login, invalidate session |
| 3 | Timing | Two users submit same form simultaneously | High | Idempotency key or conflict error |

### Summary
- Critical: N
- High: N
- Medium: N
- Low: N
- Total: N scenarios across X dimensions

Integration with Other Skills

Next StepSkillHow
Generate test cases from scenariosvc-testPass scenario table as input context
Inform implementation plan risksgenerate-plan / plan-agentPaste Critical/High rows into risk assessment
Deep persona debate on top risksvc-predictFeed Critical scenarios as the change proposal

Example Invocations

# Simple mode (default — self-contained, narrow blast radius)
/vc-scenario src/api/payment.ts
/vc-scenario "User registration with OAuth providers"
/vc-scenario src/middleware/auth.ts

# Deep mode (auto-triggered: billing surface, 3+ files)
/vc-scenario "Deduct credits on model usage — touches CreditBalance, CreditTransaction, usage-sync.ts"

# Deep mode (explicit request)
/vc-scenario --deep "Add multi-tenancy to the database layer"

Frequently asked questions

What does the Vc Scenario AI skill do?

Generate comprehensive edge cases and test scenarios by decomposing features across 12 dimensions. Use before implementation or testing to catch issues early.

Why use Vc Scenario on TypingMind?

Because you install it once and use it with any model. Vc Scenario is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Vc Scenario in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/withkynam/vibecode-pro-max-kit/tree/main/.claude/skills/vc-scenario. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Vc Scenario?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Vc Scenario?

As many as you like. As long as a model supports skills, you can use Vc Scenario with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Vc Scenario AI skill free?

Yes. It is published on GitHub by withkynam under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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