Error Handling logo

Error Handling

OrganizationPopular
EpicenterHQ
error-handling

Use when a task mentions error handling, Wellcrafted Results, result types, defineErrors, trySync, tryAsync, unwrap, thrown exceptions, or deciding whether to propagate, recover from, or present a failure. For recording diagnostics, use logging; for cache lifecycle, use query-layer.

Overview

PublisherEpicenterHQ
Repositoryepicenter
Skill nameerror-handling
Stars
4.8K
Forks
386
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Error Handling 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/EpicenterHQ/epicenter/tree/main/.agents/skills/error-handling
  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/EpicenterHQ/epicenter.git /tmp/epicenter
mkdir -p .claude/skills
cp -r /tmp/epicenter/.agents/skills/error-handling .claude/skills/error-handling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Error Handling 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 Error Handling 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 Error Handling 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.

Error Handling

Epicenter uses Wellcrafted Results as its internal contract for ordinary failures. This skill owns the full path: define a tagged failure, adapt a throwing dependency, compose or recover from Results, then consume the outcome at the boundary that serves a person or another API. Use logging for diagnostics, query-layer for TanStack observation and cache behavior, and hono for HTTP response mechanics.

Source Of Truth

Ground every Wellcrafted behavior claim in the official wellcrafted-dev/wellcrafted source and tests. When maintaining this guidance, confirm that Epicenter's installed version matches the source being read. If it does not, report the version drift; dependency freshness is handled outside this skill. Treat other skills, examples, generated documentation, and DeepWiki as leads, not authority.

Read the scoped references only when needed:

Carry the outcome until a caller settles it

Fallible application work returns Result<T, E> while its caller can recover, add context, or choose how to present the outcome. Compose operations by handling or forwarding each possible Err. Preserve a lower error unless the current operation owns a meaningful new failure. An intentional fallback can return Ok, and a partial success may need success data that records what degraded, so the final caller can respond accurately.

The interaction owner consumes the final outcome into UI state, a toast, an HTTP response, or another host contract. That owner is determined by responsibility and lifetime, not by whether the code lives in an onclick attribute or an operations directory. A terminal action can return void or Promise<void> after settling the outcome. Infallible work stays plain, and unexpected bugs must not become routine Err values.

Do not treat every consumption boundary as an unwrap. Branch on the Result when the caller presents or recovers from an error. Use unwrap when the receiving API already communicates failure by throwing.

Choose The Contract First

Required contractPattern
Callee already returns Result<T, E>Inspect or forward its Err; do not wrap the call in trySync or tryAsync
Callee throws but caller needs Result<T, E>Adapt the throwing operation with trySync or tryAsync
Failure has a valid fallbackReturn Ok(fallback) from catch
Failure must propagate as dataReturn a typed defineErrors factory result from catch
Only known external failures should become ErrMap known exceptions and rethrow unknown ones
Surrounding API is exception-basedKeep its throwing contract
Cleanup must run on success, failure, cancellation, or early returnUse finally

Use trySync for a synchronous operation and tryAsync for an operation returning a Promise.

ts
const { data: response, error } = await tryAsync({
	try: () => fetch(url),
	catch: (cause) => RequestError.TransportFailed({ cause }),
});
if (error !== null) return Err(error);
return Ok(response);

defineErrors factories already return Err(...). Pass the raw cause into the factory and let the factory compose its message with extractErrorMessage. Do not use raw Err(cause) at a catch boundary: thrown values may be null or undefined, and an untyped cause loses the domain failure.

Cross a deliberate throwing boundary

Keep a Result as data while the caller can still recover, report, retry, or propagate. Use unwrap only where the surrounding API already throws as its failure channel:

ts
import { unwrap } from 'wellcrafted/result';

const document = unwrap(await openDocument(id));

unwrap returns Ok.data and throws Err.error. The throw is the boundary's contract, not a sign the failure was unexpected. Guard on error !== null instead when this function must recover, add context, or clean up.

A conceptual sketch is the one exception: unwrap may stand in for Result branches that do not change the behavior being explained. Say that the sketch throws on failure, and show the branch whenever a failure changes that behavior, such as keeping an unsaved draft after a save conflict.

Consume Every Possible Err Branch

  • If a value can be Result<T, E>, inspect or deliberately forward its error branch.
  • After destructuring, error is the raw E. Return Err(error), not error.
  • If you retained the whole Result, return it unchanged: if (result.error !== null) return result.
  • error !== null is the reliable discriminator. Never construct Err(null) or Err(undefined).
  • Data-only destructuring is correct when the catch branch always returns Ok<T> and the inferred type collapses to Ok<T>.
  • Error-only destructuring is correct when success data is irrelevant. The rule is to handle every possible Err, not to destructure fields you do not use.

Prefer an immediate guard so the success path stays linear.

When a caller translates every variant of a tagged error union, use an exhaustive switch (error.name) with default: error satisfies never. A guard for one variant followed by a shared path is different. See error variants for both shapes.

Own The Promise

tryAsync returns a Promise. Choose its owner explicitly:

  • await when this function inspects the Result.
  • return tryAsync(...) when the caller owns the Promise<Result<...>>.
  • Do not use bare void tryAsync(...): ordinary failures fulfill with Err, so a Promise rejection handler cannot observe them. A best-effort operation still needs an async owner that awaits the Result and explicitly logs or ignores its error branch.
  • In UI fire-and-forget code, attach presentation before discarding a Promise that fulfills with a Result: void save().then((result) => toastOnError(result, 'Save failed')). If the Promise can reject, adapt or catch that rejection first.

Keep Native Try-Catch When It Expresses The Contract

Traditional try-catch is appropriate when:

  • a finally block owns cleanup;
  • a generator must yield a failure rather than return a Result;
  • a framework boundary converts an exception directly into its required response shape;
  • code catches one known exception and rethrows everything else;
  • the surrounding public API intentionally throws.

Do not turn unknown programming errors into a generic domain failure. Mapping every throw to Err can hide bugs behind a misleading retryable error.

Final Check

  1. The function's throw-versus-Result contract is explicit.
  2. The wrapper covers one coherent failure meaning.
  3. Caught values become typed errors, intentional fallbacks, or selective rethrows.
  4. Every possible Err branch is handled, forwarded, logged, presented, or explicitly ignored by a named best-effort owner.
  5. Unknown bugs still reach the appropriate crash or framework error boundary.

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 Error Handling AI skill do?

Use when a task mentions error handling, Wellcrafted Results, result types, defineErrors, trySync, tryAsync, unwrap, thrown exceptions, or deciding whether to propagate, recover from, or present a failure. For recording diagnostics, use logging; for cache lifecycle, use query-layer.

Why use Error Handling on TypingMind?

Because you install it once and use it with any model. Error Handling 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 Error Handling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EpicenterHQ/epicenter/tree/main/.agents/skills/error-handling. 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 Error Handling?

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 Error Handling?

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

Is the Error Handling AI skill free?

It is published on GitHub by EpicenterHQ. Check the repository for licensing terms. 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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