Error Handling logo

Error Handling

Organization
DevelopersGlobal
error-handling

Graceful degradation and meaningful error messages. Errors are first-class citizens, not afterthoughts. Every error path is designed, not discovered.

Overview

PublisherDevelopersGlobal
Repositoryai-agent-skills
Skill nameerror-handling
Stars
212
Forks
33
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 DevelopersGlobal 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/DevelopersGlobal/ai-agent-skills/tree/main/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/DevelopersGlobal/ai-agent-skills.git /tmp/ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/ai-agent-skills/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.

Overview

Error handling is not defensive programming — it's a user experience. When things go wrong (and they will), the system should degrade gracefully, give users actionable information, and leave enough telemetry to diagnose and fix the problem.

When to Use

  • When writing any code that can fail (I/O, network, parsing, user input)
  • When reviewing error handling in existing code
  • Before any service goes to production

Process

Step 1: Design Error Paths Explicitly

  1. For every operation, list: what can fail? What does failure look like?
  2. Classify failures:
    • Transient: Retry likely to succeed (network blip, temporary unavailability)
    • Client error: Bad input from the caller (4xx) — don't retry
    • System error: Internal failure (5xx) — alert, investigate
  3. Design the failure path for each class before writing the happy path.

Verify: Error classes defined for every external operation.

Step 2: Meaningful Error Messages

  1. Every error message answers: what went wrong? How can the caller fix it?
    • ✅ "Invalid email format. Expected: user@domain.com"
    • ❌ "Validation error"
  2. User-facing errors: friendly language, no stack traces.
  3. Developer-facing errors (logs): full context, request ID, stack trace.
  4. Never expose internal system details (DB schema, file paths) in user-facing errors.

Verify: Each error message would help a user or developer understand and fix the problem.

Step 3: Retry with Backoff

  1. Transient errors: retry with exponential backoff + jitter.
  2. Maximum retries: 3 (not infinite).
  3. After max retries: fail with a clear error, log the final failure.
  4. Non-transient errors (validation, auth): never retry.

Verify: Retry logic has a maximum. Non-transient errors don't retry.

Step 4: Graceful Degradation

  1. Identify non-critical dependencies. If they fail, degrade — don't crash.
  2. Example: recommendation engine fails → show default content, not 500.
  3. Circuit breaker pattern for failing dependencies: fail fast after threshold, recover automatically.

Verify: Every non-critical dependency has a defined degraded state.

Step 5: Structured Error Responses (APIs)

  1. API errors return consistent structure:
    json
    {
      "error": {
        "code": "INVALID_EMAIL",
        "message": "The email address format is invalid.",
        "requestId": "req_abc123"
      }
    }
  2. HTTP status codes used correctly: 400 (client error), 404 (not found), 429 (rate limited), 500 (server error).

Common Rationalizations (and Rebuttals)

ExcuseRebuttal
"I'll add error handling later"Later means in production, under pressure, while users are impacted.
"This can't fail"Everything can fail. Network calls, disk writes, parsing — all can fail.
"The error message doesn't matter"It matters when a developer is debugging at 2am.

Verification

  • Error classes defined for every external operation
  • Error messages answer: what went wrong? How to fix?
  • Transient errors retry with max limit
  • Non-critical dependencies have graceful degraded states
  • API errors return consistent structured format
  • No internal system details in user-facing errors

References

Frequently asked questions

What does the Error Handling AI skill do?

Graceful degradation and meaningful error messages. Errors are first-class citizens, not afterthoughts. Every error path is designed, not discovered.

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/DevelopersGlobal/ai-agent-skills/tree/main/skills/error-handling. TypingMind reads its SKILL.md 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?

Yes. It is published on GitHub by DevelopersGlobal 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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