dify-playwright

name	dify-playwright
description	Inspect, patch, publish, run, and diagnose workflows on a user-configured Dify deployment through Playwright and Dify APIs. Use for authenticated console operations, workflow run inspection, batch execution, and vision-input debugging.

Dify Playwright

Use this skill when a task requires access to a Dify workflow draft, console-only execution logs, or repeatable workflow runs.

Choose The Correct API

Path	Authentication	Best for
Console API	Dify email/password through Playwright	Read/write drafts, publish, inspect node executions
Service API	Dify app API key	Run an already published workflow

Prefer the Service API when no draft or console-only data is needed.

Setup

cd /path/to/dify-playwright
npm install
npx playwright install chromium
cp .env.example .env
set -a; source .env; set +a

The console path requires DIFY_BASE_URL, DIFY_EMAIL, and DIFY_PASSWORD. App-specific operations also require DIFY_APP_ID. Never print or commit these values.

Console Workflow Procedure

1. Read the current draft with npm run dify -- get-draft output/draft.json.
2. Inspect node IDs, edges, node types, and the current workflow hash.
3. Patch a local copy while preserving unrelated graph data.
4. Save with npm run dify -- save-draft input/save-payload.json.
5. If Dify rejects a stale hash, fetch the draft again and reapply the patch.
6. Publish only after validating graph structure and required node references.
7. Run representative inputs and fetch node executions instead of trusting final text alone.

Published Runs

export DIFY_APP_API_KEY='<app-api-key>'
npm run dify -- run-formal input/run.json output/result.json

The command forces streaming mode and extracts the final workflow event.

Batch Runs And Logs

• Use scripts/run_workflow_batch.mjs for generic parallel draft or installed-app runs.
• Use scripts/fetch_batch_node_usage.mjs to collect LLM node model, token, latency, loop, and status fields.
• Keep input and output files out of Git when they contain private workflow data.

Vision Workflows

Read references/dify-vision-files.md. Verify actual LLM prompt file counts from node executions; plausible output text does not prove that an image reached the model.

Completion Checklist

• Confirm the chosen API path matches the task.
• Confirm the intended draft hash changed before reporting a saved edit.
• Confirm publish completed when the user requested a published change.
• Fetch node executions for representative runs.
• Check image file counts for vision workflows.
• Separate HTTP/file preparation time from LLM time when diagnosing latency.
• Do not expose credentials, cookies, private URLs, or workflow payloads in logs or commits.