Skill Feedback Collector

Skill Feedback Collector

Pause the agent and wait for human feedback via a browser-based UI connected through WebSocket. Includes a task queue for batch execution of pre-loaded tasks.

When to Use

• After completing a task, to let the user verify the result
• When encountering uncertain parameters (colors, layouts, API choices, business logic)
• Before making destructive or irreversible changes that need user confirmation
• When the user prefers interactive confirmation over autonomous execution
• When batch-executing multiple pre-loaded tasks from the queue

Instructions

Follow these guidelines when this skill is active:

1. After completing a task, call ask_human_feedback with a summary and options for next steps. This lets the user decide what to do next rather than ending the session.

2. When unsure about implementation details, call ask_human_feedback with your question instead of making assumptions. Getting explicit input leads to better results.

3. The response from ask_human_feedback is your next instruction. Execute it, then call ask_human_feedback again when done. This creates a productive feedback loop.

4. If the task queue returns a task automatically (queue was non-empty), execute that task and call ask_human_feedback again when complete. The queue feeds tasks until empty.

5. Mode switching:

   • User says "free mode" / "自由模式" → call set_feedback_mode(enabled: false) to work without pausing
   • User says "feedback mode" / "确认模式" → call set_feedback_mode(enabled: true) to resume the confirmation loop

Best Practices

• Ask the user before ending a session — they may have more tasks
• Confirm before making destructive changes (force push, drop table, delete files)
• If feedback mode is disabled (bypass message returned), continue working autonomously

Tools

ask_human_feedback

Pauses execution and sends the reason to the browser UI. Returns the human's text response. If the task queue is non-empty, the next task is auto-dequeued and returned (with a short delay for UI visibility).

Parameters: reason (string) — summary of work done and what input you need.

Example reason format:

Completed: [specific work done]
Changes: [files modified, endpoints added, etc.]

What would you like me to do next?
1. [Option A]
2. [Option B]
3. Something else

set_feedback_mode

Toggle feedback confirmation on/off. When off, ask_human_feedback returns immediately without pausing.

Parameters: enabled (boolean)

Setup

npm install && npm run build

MCP configuration:

{
  "command": "node",
  "args": ["build/index.js"],
  "cwd": "/path/to/skill-feedback-collector"
}

Browser UI: http://<server-ip>:18061

Workflow

User message → Agent works → calls ask_human_feedback("Done. Next?")
                                    ↓
                    [Queue has tasks?] → YES → returns next task → Agent continues
                                    ↓ NO
                    [Waits for human input via browser UI]
                                    ↓
                    Human responds → Agent receives → works → calls ask_human_feedback again
                                    ↓
                    ... loop continues until user indicates they are done ...

Security

• Set FEEDBACK_TOKEN when deploying on shared or public networks to restrict access
• Use a firewall to limit which IPs can reach the HTTP/WebSocket port
• The server binds to 0.0.0.0 by default for convenience; restrict network access at the OS or firewall level if needed
• Conversation history (feedback-history.json) is stored locally in the skill directory; review and rotate if it contains sensitive information
• This skill does not make outbound network requests, download external resources, or execute shell commands

Tips

• The task queue lets users pre-load multiple tasks for sequential execution
• Users can add tasks to the queue while the agent is working
• HTTP long-polling fallback activates automatically when WebSocket is unavailable
• Browser notifications and sound alerts notify you when the agent has a question
• Conversation history is persisted locally (max 500 entries)