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Edge Case Sweep

Community
adamentwistle
edge-case-sweep

Enumerate edge cases against a fixed checklist — empty, absent, boundaries, huge, weird text, duplicates, repeated/concurrent calls, stale state, time, failing dependencies — before declaring code complete. Use after the happy path of any new or changed function, handler, parser, validator, CLI, or data transformation works, and when reviewing one, before reporting done. Not for test structure or mocking decisions (see test-design).

Overview

Publisheradamentwistle
Repositoryfable-skills
Skill nameedge-case-sweep
Stars
98
Forks
11
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 adamentwistle on GitHub. Read the source before you install it.

Installation

Install the Edge Case Sweep 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/adamentwistle/fable-skills/tree/main/edge-case-sweep
  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/adamentwistle/fable-skills.git /tmp/fable-skills
mkdir -p .claude/skills
cp -r /tmp/fable-skills/edge-case-sweep .claude/skills/edge-case-sweep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Edge Case Sweep 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 Edge Case Sweep 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 Edge Case Sweep 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-case-sweep

Weak sweeping looks like: happy path passes, one if (!input) return added, done. Strong sweeping walks a fixed list against every new or changed code path, decides each applicable case, and proves the decision with a run.

Procedure

  1. List the changed entry points (functions, handlers, commands) and the inputs each receives.
  2. Walk the checklist below against each. For every case that can reach the code, record one of: handle (a code path exists), reject loudly (specific error, non-zero exit, 4xx — and say what the caller sees), or note (deliberately unhandled, listed in the report). Silent wrong behavior is the one unacceptable outcome.
  3. For every handle/reject decision, exercise it: run a test or a one-off call with that input and observe the outcome. "Handled" without an observed run is a claim, not a result.
  4. Do not add guards for inputs the code cannot receive; state the invariant that excludes them instead. Defensive noise hides the real checks.

Checklist

Inputs

  • empty: "", [], {}, zero rows, zero-byte file
  • absent: null / undefined / missing key / missing flag — distinct from empty, and the distinction must survive the whole path, not just the entry check
  • boundaries: 0, -1, exactly-at-limit, limit+1, first and last element, single element
  • huge: 10^6 items, multi-GB file, pathologically long string — does it stream or load everything?
  • weird text: unicode, emoji, RTL, newlines in fields, quotes, leading/trailing whitespace, case variants, path separators
  • duplicates and unsorted order wherever uniqueness or order is assumed

State & time

  • called twice (idempotency), called concurrently, retried after partial success — is any output left half-written?
  • stale state: file changed since read, record deleted between fetch and update
  • timezone, DST, end-of-month/year, leap day, clock skew

Environment

  • dependency down / slow / returning errors or malformed data: what does the caller see, and does it time out?
  • permissions denied, disk full, network cut mid-operation

Outputs

  • the reject path returns the documented type (exception class, status code, exit code), never a success-shaped value with bad contents
  • partial results are impossible or clearly marked as partial

Report

List the cases you exercised and the cases you deliberately left unhandled. A stated limitation is fine; a hidden one is a bug report waiting.

Frequently asked questions

What does the Edge Case Sweep AI skill do?

Enumerate edge cases against a fixed checklist — empty, absent, boundaries, huge, weird text, duplicates, repeated/concurrent calls, stale state, time, failing dependencies — before declaring code complete. Use after the happy path of any new or changed function, handler, parser, validator, CLI, or data transformation works, and when reviewing one, before reporting done. Not for test structure or mocking decisions (see test-design).

Why use Edge Case Sweep on TypingMind?

Because you install it once and use it with any model. Edge Case Sweep 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 Edge Case Sweep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/adamentwistle/fable-skills/tree/main/edge-case-sweep. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Edge Case Sweep?

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 Edge Case Sweep?

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

Is the Edge Case Sweep AI skill free?

Yes. It is published on GitHub by adamentwistle 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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