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Deliver Edge Cases

Organization
product-on-purpose
deliver-edge-cases

Documents edge cases, error states, boundary conditions, race conditions, and recovery paths for a feature - the systematic catalog of what can go wrong and the failure modes to design for. Use during specification to map the failure surface and ensure comprehensive coverage, or during QA planning to identify boundary and limit scenarios to test. Distinct from deliver-acceptance-criteria, which writes story-level Given/When/Then checks; this skill produces the whole-feature edge-case catalog.

Overview

Publisherproduct-on-purpose
Repositorypm-skills
Skill namedeliver-edge-cases
Stars
717
Forks
92
Bundled files
5
LicenseApache-2.0
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by product-on-purpose on GitHub. Read the source before you install it.

Installation

Install the Deliver Edge Cases 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/product-on-purpose/pm-skills/tree/main/skills/deliver-edge-cases
  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/product-on-purpose/pm-skills.git /tmp/pm-skills
mkdir -p .claude/skills
cp -r /tmp/pm-skills/skills/deliver-edge-cases .claude/skills/deliver-edge-cases
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deliver Edge Cases 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 Deliver Edge Cases 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 Deliver Edge Cases 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.

Edge Cases

An edge cases document systematically catalogs the unusual, boundary, and error scenarios for a feature. While happy-path flows are typically well-specified, edge cases often get discovered in production - causing bugs, poor user experience, and support burden. Documenting edge cases upfront ensures engineering handles them intentionally and QA knows what to test.

When to Use

  • When you need to enumerate failure modes, race conditions, timeouts, and boundary or limit scenarios - everything that can go wrong - and define a recovery path for each
  • During feature specification before engineering begins
  • When preparing QA test plans
  • After discovering production bugs to prevent similar issues
  • When reviewing PRDs or user stories for completeness
  • Before launch to ensure error states have been designed

When NOT to Use

  • You need story-scoped Given/When/Then checks for handoff -> use deliver-acceptance-criteria; this skill catalogs the whole feature's failure surface
  • The feature is not specified enough to enumerate inputs, states, and limits -> use deliver-prd first
  • A production incident already happened and you want the learning banked -> use iterate-lessons-log, then update this catalog with the new case
  • You need readiness coordination for a launch, not failure analysis -> use deliver-launch-checklist

Instructions

When asked to document edge cases, follow these steps:

  1. Define the Feature Scope Clearly describe what feature or flow you're analyzing. Edge cases are specific to context - the same input might be valid in one feature and invalid in another.

  2. Walk Through Input Validation Consider every user input: What if it's empty? Too long? Wrong format? Contains special characters? What are the minimum and maximum valid values?

  3. Explore Boundary Conditions Find the edges of acceptable ranges. If a field accepts 1-100, test 0, 1, 100, and 101. Consider pagination boundaries, timeout thresholds, and rate limits.

  4. Map Error States Identify what can go wrong: network failures, permission denied, resource not found, concurrent modifications, expired sessions. Document both the scenario and expected behavior.

  5. Consider Concurrency Issues What if two users act simultaneously? What if the user double-clicks? What if data changes between load and save? Race conditions often cause subtle bugs.

  6. Define Recovery Paths For each error, specify how users recover. What message do they see? Can they retry? Is data preserved? Good error handling turns frustration into confidence.

  7. Prioritize by Likelihood and Impact Not all edge cases need the same attention. High-likelihood + high-impact cases need robust handling; rare + low-impact cases might just need graceful failure.

Output Format

Use the template in references/TEMPLATE.md to structure the output. A complete edge-case catalog fills every template section: Feature Overview; Edge Case Categories; Error Messages; Recovery Paths; and Test Scenarios.

Quality Checklist

Before finalizing, verify:

  • All user inputs have validation edge cases documented
  • Boundary conditions are explicitly listed
  • Network/system failure scenarios are covered
  • Each error state has a defined user-facing message
  • Recovery paths are specified (not just error detection)
  • Edge cases are prioritized by likelihood and impact

Examples

See references/EXAMPLE.md for a completed example.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deliver Edge Cases AI skill do?

Documents edge cases, error states, boundary conditions, race conditions, and recovery paths for a feature - the systematic catalog of what can go wrong and the failure modes to design for. Use during specification to map the failure surface and ensure comprehensive coverage, or during QA planning to identify boundary and limit scenarios to test. Distinct from deliver-acceptance-criteria, which writes story-level Given/When/Then checks; this skill produces the whole-feature edge-case catalog.

Why use Deliver Edge Cases on TypingMind?

Because you install it once and use it with any model. Deliver Edge Cases 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 Deliver Edge Cases in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/product-on-purpose/pm-skills/tree/main/skills/deliver-edge-cases. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deliver Edge Cases?

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 Deliver Edge Cases?

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

Is the Deliver Edge Cases AI skill free?

Yes. It is published on GitHub by product-on-purpose under the Apache-2.0 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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