Web LLM Chat Skill#

Interact with web-based LLMs through the Chrome Relay extension. This skill enables automated conversations with AI models, supporting both simple queries and multi-turn research workflows.

Currently supported: Qwen AI (chat.qwen.ai) — more models coming soon.

Why This Skill?#

The Problem#

• Web search APIs are expensive: Services like Brave Search API and Tavily require API keys and paid subscriptions, creating ongoing costs.
• Limited research capabilities: Traditional search APIs return raw results, lacking the reasoning and synthesis capabilities of modern LLMs.
• Quality vs. cost tradeoff: Getting high-quality, well-reasoned research often requires expensive API calls or manual effort.

The Opportunity#

Modern web-based LLMs (like Qwen) offer:

• Powerful built-in search: Native web search with real-time information retrieval
• RAG capabilities: Automatic retrieval-augmented generation for grounded responses
• Deep research features: Multi-source synthesis and citation
• Commercial-grade quality: As products backed by major companies, they're continuously improved

The Solution#

This skill leverages OpenClaw's Chrome Relay to:

• Access web LLMs for free: Use the web interface without API costs
• Automate research workflows: Let agents conduct multi-turn investigations
• Get higher quality results: Benefit from commercial LLM capabilities at lower cost
• Enable comparison: Cross-reference with other AI responses

Bottom line: Use OpenClaw to orchestrate powerful web-based LLMs at a fraction of the API cost, with better research quality than raw search APIs.

Features#

• Send messages to web-based LLMs and receive responses
• Multiple output formats: plain text, Markdown (preserves code blocks, tables, lists), or raw HTML
• Send-ready detection: waits until the page is ready for the next question
• Smart extraction: uses anchor-based extraction to get only the latest response
• Research mode: agent-orchestrated multi-turn conversations

Supported Models#

ModelStatusNotesQwen AI (chat.qwen.ai)✅ SupportedFull support for search, RAG, and multi-turn conversationsMore models🚧 Coming soonOpen an issue to request support for other web-based LLMs

Prerequisites#

• Chrome Relay extension attached to a Qwen Chat tab ( chat.qwen.ai/*)
• Gateway running on 127.0.0.1:18789 (default)
• Node.js with ws package installed

Installation#

Install the ws package using your preferred package manager:

bash

# npm
npm install ws

# yarn
yarn add ws

# pnpm
pnpm add ws

Quick Start#

Check Connection Status#

bash

node scripts/qwen_chat.js status

Send a Message#

bash

# Plain text (default)
node scripts/qwen_chat.js send "What is machine learning?"

# With custom wait time (for long responses)
node scripts/qwen_chat.js send "Explain RAG in detail" --wait 120

# Get response in Markdown format (preserves formatting)
node scripts/qwen_chat.js send "Write a Python function" --format markdown

# Get raw HTML
node scripts/qwen_chat.js send "Create a table" --format html

Read Current Page Content#

bash

node scripts/qwen_chat.js read

Command Reference#

status#

Check if Chrome Relay is connected and Qwen tab is active.

bash

node scripts/qwen_chat.js status

Output:

text

Extension: ✅ Connected
Qwen tab: ✅ Qwen Chat
  URL: https://chat.qwen.ai/c/...

send#

Send a message to Qwen and receive the response.

bash

node scripts/qwen_chat.js send "your message" [options]

Options:

OptionDescriptionDefault--wait NMaximum wait time in seconds45--format text|markdown|htmlOutput formattext--debug-extractShow extraction debugging infooff

Output Formats:

• text — Plain text output
• markdown — Preserves code blocks, tables, lists, headers, and formatting
• html — Raw HTML from the page

read#

Read the current page content (useful for debugging or reviewing conversation history).

bash

node scripts/qwen_chat.js read

research#

Run multi-round research on a topic (fixed stages, consider using agent-orchestrated mode instead).

bash

node scripts/qwen_chat.js research "AI safety" --rounds 10 --wait 120

How It Works#

Response Extraction#

The script uses a robust extraction strategy:

1. Send-ready detection: Waits until the page is ready for the next question (input field editable, send button enabled)
2. Anchor-based extraction: Uses the user's message as an anchor to find and extract only the latest response
3. Content stabilization: Waits for content to stabilize before extraction

Why Not Use Thinking Indicators?#

• Thinking indicators can get stuck visually while the response is complete
• Send-ready detection is more reliable: if you can send the next question, the previous response is done
• Works regardless of UI changes to thinking indicators

Why Not Use Delta by Body Length?#

• Qwen page may reflow and change bodyLen unpredictably
• Anchor-based extraction is more robust to page reflows
• Only extracts the actual response content, not noise

Research Mode (Agent-Orchestrated)#

For multi-turn research, use agent-orchestrated mode instead of the fixed research command. This allows the agent to dynamically control the conversation based on Qwen's responses.

Workflow#

text

1. Determine research topic
2. Ask first question (open-ended, let Qwen expand)
3. Read Qwen's response
4. Analyze the response:
   - Which point deserves deeper exploration?
   - Which claim needs cross-validation?
   - Any contradictions or gaps?
5. Ask follow-up question based on analysis
6. Repeat steps 3-5 for 5-10 rounds
7. Final round: Ask Qwen to summarize, agent also compiles its own summary

Example Per-Round Operation#

bash

# Agent sends question and waits for response
node scripts/qwen_chat.js send "What are the key challenges in RLHF?" --wait 120

# Agent can read full page if needed
node scripts/qwen_chat.js read

Follow-up Strategy#

Good follow-ups come from Qwen's response:

Response PatternFollow-up DirectionMentions data/statistics"What's the original source? Sample size?"Gives opinion without evidence"Any research supporting this claim?"Mentions controversy"What are the counter-arguments?"Uses "possibly/maybe""Under what conditions does this hold?"Lists multiple factors"Which one is most critical? Why?"Mentions case study"Has this case been challenged by other researchers?"Goes off-topic"Back to the core question, specifically..."

Best Practices#

• Don't pre-plan all questions: Generate questions dynamically based on responses
• Allow tangents: If Qwen mentions something unexpected but interesting, pursue it
• Challenge occasionally: Don't always agree with Qwen; present counter-arguments
• Maintain continuity: Briefly reference previous points when asking follow-ups
• Control rounds: 5-10 rounds is optimal; too few lacks depth, too many has diminishing returns
• Handle timeouts honestly: If the script times out, report it to the user rather than making up content
• Adjust wait time: Use --wait 180 for search-heavy questions, --wait 60 for simple ones

Debugging#

Enable Extraction Debugging#

bash

node scripts/qwen_chat.js send "test message" --wait 90 --debug-extract

This shows:

• Baseline and latest body length
• Number of leaf elements detected
• Extraction path used
• Raw and final content lengths

Common Issues#

IssueSolutionExtension disconnectedCheck Chrome extension badge shows ONNo Qwen tab foundOpen chat.qwen.ai and attach extensionResponse not capturedIncrease --wait time, use --debug-extract to diagnoseMarkdown formatting brokenCode blocks use Monaco Editor; extraction handles this automatically

Configuration#

Auth Token#

The script auto-derives the relay token from the OpenClaw config. Config priority:

1. E:\.openclaw\.openclaw\openclaw.json (Windows)
2. ~/.openclaw/.openclaw/openclaw.json (Unix)

Gateway Ports#

• Gateway: 18789
• Relay: 18792 (Gateway + 3)

Limitations#

• Requires Qwen to be logged in the browser
• One tab at a time (controls the first attached Qwen tab)
• No streaming — waits for full response before returning
• research command uses fixed stages — use agent-orchestrated mode instead

File Structure#

text

qwen-chat/
├── SKILL.md                    # This file
├── scripts/
│   ├── qwen_chat.js           # Main script
│   ├── _diagnose_selectors.js # Diagnostic tools
│   └── _analyze_format.js     # Format analysis
└── references/
    └── chrome-relay.md        # Chrome Relay setup guide

See Also#

• Chrome Relay Setup — detailed relay configuration

License#

See LICENSE file for details.