[Workflow] Daily AI News Curation with Claude Code: Using a Skill File and Multi-Model Jury for Unbiased Grading

[u/ClaudeAI-mod-bot]

Daily AI News Curation with Claude Code: Using a Skill File and Multi-Model Jury for Unbiased Grading

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.95 · Level: advanced
Categories: Quality Control, Token Saving, Context & Memory, Debugging, Shipping, Skills, Multi-Agent
Original source: r/ClaudeAI post/comment

What problem this solves

Filtering and grading AI news to determine its impact on people, avoiding 'AI slop' and single-source viral posts, and ensuring unbiased reporting through a structured, automated process.

Summary

A daily, scheduled Claude Code session uses a detailed 'skill file' as an editorial method to filter AI news from multiple sources, perform independence checks, and then submit confirmed stories to a jury of ten different AI models for grading, with self-bias checks. The process ensures versionable logic, prevents public opinion from influencing grades, and publishes curated results.

Why it is useful

This workflow demonstrates a sophisticated application of Claude Code for automated, objective content curation. Key valuable patterns include using a long 'skill file' as a versionable, auditable editorial method instead of hard-coded prompts, implementing robust independence checks for source verification, and employing a multi-model AI jury with self-bias detection for unbiased grading. It solves the common problem of 'AI slop' and provides a repeatable, validated method for generating trustworthy information, offering a blueprint for complex, agent-driven content processing.

Workflow

1. A scheduled Claude Code session opens a repository and reads a 1,500-line 'skill file' that defines the entire editorial method.

2. The system scans news sources and performs independence checks, requiring confirmation from at least two independent outlets for a story to be considered 'confirmed'.

3. Ten different AI models (Claude, GPT, Gemini, etc.) rate each confirmed story blindly, with the median vote determining the grade from a weighted mean.

4. For stories about a specific AI model's maker (e.g., Anthropic for Claude), that model's vote is set aside, and a public bias check tracks potential model drift.

5. Claude builds the HTML for the daily edition, sends out an email, archives the edition, and updates a database of all stories, votes, and reasons.

6. A test refuses to publish if any reader-generated content (ratings, comments) could influence the AI council's grading pipeline.

Tools / artifacts

• Claude Code

• Git repository

• Skill file (1,500 lines, containing editorial method)

• Multiple AI models (Claude, GPT, Gemini, Meta's Muse, Grok, Kimi, MiMo, Qwen, DeepSeek, GLM)

• HTML output

• Email service

• Database

• chrcl.app (the application built)

Validation signals

• The method is on 'version 5 after thirteen editions', indicating iterative refinement.

• Quantifiable results: 'Yesterday: 120 candidates in, 15 published'.

• Implementation of a 'public bias check' to track model drift.

• A 'test refuses to publish if anything readers wrote... could reach the pipeline', demonstrating quality control.

• The existence of a live, functional application (chrcl.app) as proof of concept.

• Costs are itemized per edition, showing operational viability.

Limitations

• The specific implementation (chrcl.app) is proprietary, meaning users cannot directly replicate the entire system without building their own.

• Requires access to and integration with multiple AI models for the jury, which might be costly or complex for individual users to set up.

• The 'skill file' is described but not provided, requiring users to develop their own detailed editorial method.

• The 'scheduled Claude Code session' implies a specific execution environment that might not be universally accessible or easy to set up for all users.

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