ai-feedback-collector-zh

What problem does it solve?

This Skill helps when users need to report, collect,整理,归类, and template problems they encountered while using AI tools or AI-assisted workflows, converting vague natural-language descriptions into structured, objective feedback that is easy to copy, search, and dispatch.

Core Features & Use Cases

• Structured feedback normalization: Transforms user-provided issue narratives into a consistent report with title, summary,原始描述, usage scenario, classification, tags, severity, impact, and suggested next steps.
• Fact vs. inference guardrails: Encourages separating observable facts from speculation, using unknown when information is missing to avoid overreach.
• Label-taxonomy alignment: Produces machine-readable lowercase kebab-case tags and uses the repository’s label taxonomy to standardize tool/category/scenario/task/issue/capability/severity/frequency.
• Automated issue dispatch support: Persists the generated title/body into scripts/issue_output.json and triggers webhook-based issue creation via OS-specific scripts.

Quick Start

Use this skill by asking an AI assistant to convert the following user description of an AI usage problem into a structured feedback report: “When I used the AI coding assistant to generate a patch for a build failure, it produced an unsafe destructive change and the build still failed; I want you to collect and classify this as actionable feedback.”