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Webflow Code Component:Troubleshoot Deploy

Organization
webflow
webflow-code-component:troubleshoot-deploy

Debug deployment failures for Webflow Code Components. Analyzes error messages, identifies root causes, and provides specific fixes for common issues.

Overview

Publisherwebflow
Repositorywebflow-skills
Skill namewebflow-code-component:troubleshoot-deploy
Stars
122
Forks
18
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Webflow Code Component:Troubleshoot Deploy 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.
  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/webflow/webflow-skills.git /tmp/webflow-skills
mkdir -p .claude/skills
cp -r /tmp/webflow-skills/plugins/webflow-skills/skills/troubleshoot-deploy .claude/skills/webflow-webflow-code-component-troubleshoot-deploy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Webflow Code Component:Troubleshoot Deploy 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 Webflow Code Component:Troubleshoot Deploy 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 Webflow Code Component:Troubleshoot Deploy 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.

Troubleshoot Deploy

Debug and fix deployment issues for Webflow Code Components.

When to Use This Skill

Use when:

  • webflow library share failed with an error
  • Components deployed but aren't working correctly
  • User shares an error message from deployment
  • Bundle or compilation errors occurred

Do NOT use when:

  • Deployment hasn't been attempted yet (use deploy-guide instead)
  • Validating before deployment (use pre-deploy-check instead)
  • General code quality issues (use component-audit instead)

Instructions

Phase 1: Gather Information

  1. Get error details:

    • Ask for exact error message
    • Request output from npx webflow library share
    • Check if npx webflow library log has additional info
  2. Understand context:

    • First deploy or update?
    • Recent changes made?
    • Working previously?

Phase 2: Diagnose

  1. Identify error category:

    • Authentication errors
    • Build/compilation errors
    • Bundle size errors
    • Network/upload errors
    • Configuration errors
  2. Analyze root cause:

    • Parse error message
    • Check common causes
    • Identify specific issue

Phase 3: Provide Solution

  1. Give specific fix:

    • Step-by-step resolution
    • Code examples if needed
    • Verification steps
  2. Prevent recurrence:

    • Explain why it happened
    • Suggest preventive measures

Common Error Reference

For detailed solutions to each error, see references/ERROR_CATALOG.md.

Quick Reference

ErrorCategoryQuick Fix
"Authentication failed"AuthRegenerate API token in Workspace Settings
"Insufficient permissions"AuthCheck workspace role and token
"Module not found"Buildnpm install --save-dev @webflow/react
"TypeScript errors"BuildRun npx tsc --noEmit to find error
"Unexpected token"BuildCheck file extension is .tsx
"Bundle size exceeds limit"BundleTree-shake imports, lazy load heavy components
"Component not rendering"RuntimeCheck SSR issues, browser console
"Styles not appearing"RuntimeImport CSS in .webflow.tsx file
"webflow.json not found"ConfigCreate webflow.json in project root
"No components found"ConfigCheck glob pattern and file extension
"Invalid JSON in webflow.json"ConfigFix JSON syntax (trailing commas, comments)
"429 Too Many Requests"NetworkWait 60 seconds and retry
"Request timed out"NetworkCheck connectivity, proxy, Webflow status
"JavaScript heap out of memory"MemoryNODE_OPTIONS="--max-old-space-size=4096"
"Circular dependency"BuildExtract shared code, break import cycles

Most Common Fixes

Authentication:

bash
# Regenerate token, then:
export WEBFLOW_WORKSPACE_API_TOKEN=your-new-token
npx webflow library share

Missing Dependencies:

bash
npm install --save-dev @webflow/webflow-cli @webflow/data-types @webflow/react

SSR Issues:

typescript
// Wrap browser APIs in useEffect or disable SSR:
declareComponent(Component, { options: { ssr: false } });

Missing Styles:

typescript
// In .webflow.tsx, import styles:
import "./Component.module.css";

Debugging Commands

bash
# Check recent deploy logs
npx webflow library log

# Verbose deploy output (shows detailed errors)
npx webflow library share --verbose

# Type check without deploying
npx tsc --noEmit

Validation

The issue is resolved when all of the following are true:

Success CriteriaHow to Verify
Deploy completes without errorsnpx webflow library share exits cleanly
Components appear in DesignerOpen Add panel in Designer and find your library
Import logs confirm successnpx webflow library log shows successful import

Guidelines

Error Analysis Process

  1. Read the full error message - Often contains the solution
  2. Check the error category - Auth, build, bundle, or runtime
  3. Look for file paths - Points to exact location
  4. Check line numbers - For code errors
  5. Search error message - May be a known issue

When to Escalate

If none of the solutions work, gather this data before escalating:

  1. Deploy logs: npx webflow library log
  2. Verbose output: npx webflow library share --verbose
  3. Node.js version: node -v
  4. Package versions: npm list @webflow/webflow-cli @webflow/data-types @webflow/react
  5. Configuration: Contents of webflow.json
  6. Error message: Full error output (not just the summary line)

Then:

  • Check Webflow status page for outages
  • Search the Webflow Community Forum for your error message
  • Contact Webflow Support with the collected data above

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 Webflow Code Component:Troubleshoot Deploy AI skill do?

Debug deployment failures for Webflow Code Components. Analyzes error messages, identifies root causes, and provides specific fixes for common issues.

Why use Webflow Code Component:Troubleshoot Deploy on TypingMind?

Because you install it once and use it with any model. Webflow Code Component:Troubleshoot Deploy 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 Webflow Code Component:Troubleshoot Deploy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/webflow/webflow-skills/tree/main/plugins/webflow-skills/skills/troubleshoot-deploy. 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 Webflow Code Component:Troubleshoot Deploy?

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 Webflow Code Component:Troubleshoot Deploy?

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

Is the Webflow Code Component:Troubleshoot Deploy AI skill free?

Yes. It is published on GitHub by webflow 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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