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From Script to Deploy: A Complete AI News Generation Workflow

#ai #automation #productivity #webdev

Getting AI to produce a single article is easy. Getting it to produce consistent, publication-ready content every day through an automated system is a different problem entirely. Here is the prompting and workflow design that makes daily AI news generation reliable.

The Goal

jidonglab.com publishes daily AI industry news. The system needed to: find stories, write analysis, format for two platforms (English on the main site, Korean on DEV.to), and deploy — without manual intervention. "Set it and forget it" was the target.

Teaching Claude a Writing Skill

The biggest quality lever was not the prompt itself but the blog-writing skill definition. Rather than asking for "news," I asked for analysis with insight.

SKILL_CONTEXT="Use blog-writing skill to produce AI industry analysis that reads like it was written by a domain expert"
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Good prompt:

"Use blog-writing skill to write AI industry news analysis. Include technical background, industry impact, and forward outlook. The audience is AI developers and tech founders."

Bad prompt:

"Summarize this news"

The word "analysis" versus "summary" changes the output depth significantly.

Tone Differentiation by Platform

Same story, different audience. The English version targets global readers who want concise, data-driven updates. The Korean version targets developers who want deeper context.

if [[ "$lang" == "en" ]]; then
    TONE="concise, fact-focused for global tech audience"
    TARGET_WORDS="300-400"
else
    TONE="detailed analysis with context for Korean developers"
    TARGET_WORDS="400-600"
fi
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The reasoning: jidonglab.com readers want quick updates. DEV.to readers want understanding.

Deduplication Strategy

AI occasionally generates the same story under slightly different headlines. File-based dedup catches this:

Deleted: 2026-03-14-ai-legislation-chatbot-safety.md
Kept:    2026-03-14-ai-chatbot-safety-legislation.md
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The filename generation rule given to the AI:

"Filename format: YYYY-MM-DD-primary-keyword-secondary-keyword.md. Primary keyword is company or technology name. Secondary keyword is the core topic. Example: 2026-03-14-anthropic-partner-network-100m.md"

Script-level dedup before generation is cheaper than generating and discarding:

existing_files=$(ls src/content/ai-news/${DATE}-*.md 2>/dev/null || true)
if echo "$existing_files" | grep -q "$base_filename"; then
    echo "Similar news already exists: $base_filename"
    continue
fi
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GitHub Actions Optimization

The original DEV.to publish workflow was 53 lines of complex filtering logic. Only AI news needed automated cross-posting. The rewrite:

- name: Publish AI news only
  run: |
    find src/content/ai-news -name "*.md" -newer .last-publish | \
    while read file; do
      publish_to_devto "$file"
    done
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Blog posts get manual review before publishing. News gets published immediately. Different content types require different deployment strategies.

The paths trigger filter prevents unnecessary workflow runs:

on:
  push:
    paths:
      - 'src/content/ai-news/**'
  schedule:
    - cron: '0 9 * * *'
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Localization, Not Translation

The multilingual prompt pattern uses different audience contexts, not just different languages:

English version:

"Write an AI industry news analysis for global tech professionals. Focus on technical implications, market impact, competitive landscape. Style: Professional, data-driven, concise."

Korean version:

"Write AI industry news analysis for Korean developers and tech founders. Include technical background, impact on the Korean market, practical application ideas. Style: friendly but professional, insight-driven."

The key difference: it is not "translate to Korean." It is "write for Korean developers" — different audience context, different emphasis, different examples.

Metadata also reflects this:

# English
title: "Anthropic Partner Network Reaches 100M Users"

# Korean
title: "Anthropic Partner Network 1억 사용자 돌파 — 한국 AI 시장에 미치는 영향"
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The English title states the fact. The Korean title adds "impact on the Korean AI market" because that is what the audience cares about.

Claude Code vs Shell Scripts: When to Use Which

This project used shell scripts for the pipeline, not Claude Code's agent mode. The decision criteria:

Claude Code for: complex code generation, multi-file dependency analysis, code quality review.

Shell scripts for: repetitive content generation, API calls and filesystem operations, workflow automation.

AI news generation is simple logic that needs consistency and reliability. Shell scripts are the better fit:

for news_item in "${news_items[@]}"; do
    generate_content "$news_item" "$lang"
    validate_output "$output_file"
    if [[ $? -eq 0 ]]; then
        commit_and_push "$output_file"
    fi
done
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What Could Be Better

RSS feed monitoring instead of manual topic input. Parsing feeds from TechCrunch, The Verge, and Ars Technica would make topic selection autonomous.

MCP server for news source aggregation. Connecting NewsAPI, Reddit, and Hacker News through an MCP server would give Claude access to diverse sources without prompt engineering.

Quality scoring before publish. A second AI pass that rates each article on factual accuracy (0-10), reader usefulness (0-10), and grammar (0-10) — rejecting anything below threshold.

OpenAI batch API for parallel bilingual generation. Processing English and Korean simultaneously would halve total execution time.

Takeaways

  • Skill-based prompting produces more consistent content than ad-hoc "write news" prompts
  • Multilingual content needs localized prompting, not translation — different audiences need different framing
  • Deduplication and error handling in the automation layer are what make daily generation sustainable
  • Claude Code vs shell scripting is a complexity/repeatability tradeoff — pick based on the task

Building a fully automated content system is not about asking AI to write. It is systems engineering: designing the workflow, applying the right tool at each stage, and building guardrails that catch failures before they reach production.

Commit log

c4b0055 — feat: apply blog-writing skill to AI news script
358bf9c — fix: DEV.to workflow publish ai-news only + remove chatbot regulation news
e615288 — feat: AI news script — English (jidonglab) + Korean (DEV.to) dual generation
a00b3bf — feat: AI news 2026-03-14 (4 posts, en)
069ca0d — feat: AI news CLI generation script + 2026-03-14 4 news items
6788360 — feat: AI news auto-generation (2026-03-14)

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