Article PRD Generator

Generate a comprehensive article specification for: $ARGUMENTS

Argument Parsing

Parse the arguments to extract content type, custom word count, and topic:

Supported formats:

• •/prd Topic Here — defaults to long-form, preset word count
• •/prd news: Topic Here — uses news preset
• •/prd tutorial: Topic Here — uses tutorial preset
• •/prd 4000 words: Topic Here — long-form at custom 4000 words
• •/prd news 2000 words: Topic Here — news format at custom 2000 words
• •/prd 1500 words email: Topic Here — email format at custom 1500 words

Parsing rules:

1. •Look for a known content type keyword: long-form, research, news, opinion, listicle, email, tutorial, case-study, reddit
2. •Look for a word count pattern: N words or Nk words (e.g., 4000 words, 4k words)
3. •Everything after the colon (:) is the topic. If no colon, everything is the topic.
4. •If no content type specified, default to long-form
5. •If no word count specified, use the preset's default

Load Presets

Read templates/presets.json to get the preset for the detected content type.

If a custom word count was specified, scale the preset targets proportionally:

• •total_words = custom word count
• •minimum_words = custom word count * 0.8
• •sections = scale proportionally from preset (minimum 2)
• •external_links = scale proportionally (minimum 3)
• •real_world_examples = scale proportionally (minimum 2)
• •min_words_per_section = total_words / section_count
• •sources_per_section = scale proportionally (minimum 3)

Scaling formula: scaled_value = round(preset_value * (custom_words / preset_total_words))

Example: If the news preset has external_links: 8 at total_words: 1500, and you specify 3000 words, then external_links = round(8 * 3000/1500) = 16.

Target Output

Targets come from the preset (possibly scaled by custom word count):

• •Word target from preset targets.total_words
• •Section count from preset targets.sections_min to targets.sections_max
• •Examples and links from preset targets
• •Narrative flow — story arc from preset narrative_arc
• •Prose ratio from preset editorial.prose_ratio_minimum

Article Type Detection

Before scaffolding, determine the article type. This shapes what "evidence" looks like:

Set article_type in article.json. This tells downstream skills what kinds of evidence to prioritize.

Note: article_type (topic/evidence) is independent from content_type (format/length). A "technical tutorial" uses technical evidence in tutorial format. A "business news" article uses business evidence in news format.

Editorial Philosophy

The PRD sets the tone. If the scaffolding encourages bullet-heavy writing, the article will be bullet-heavy. Build narrative thinking into the structure from the start.

Headers that flow: Avoid double-barreled academic titles like "Finding Prospects: Strategies for Success". Use direct, conversational headers: "Where Good Prospects Hide" or "Building Your First 100 Contacts".

Critical thinking prompts: Each section scaffold should include a "why it matters" question, not just "what to cover".

Your Task

Step 1: Topic Research

Quick WebSearch to understand:

• •Current state of the topic (2025-2026)
• •Key players, people, companies, and examples
• •Common pain points and solutions
• •What makes this topic interesting NOW

Step 2: Define the Narrative Arc

Use the narrative arc from the content type preset. Different formats have different arcs:

Long-form: HOOK -> CONTEXT -> FOUNDATION -> DEEP DIVES -> PRACTICAL -> INSPIRATION -> ACTION News: HOOK -> CONTEXT -> DETAILS -> IMPACT -> NEXT-STEPS Email: HOOK -> VALUE -> CTA Tutorial: HOOK -> PREREQUISITES -> FOUNDATION -> WALKTHROUGH -> ADVANCED -> WRAP-UP Case Study: HOOK -> BACKGROUND -> CHALLENGE -> APPROACH -> RESULTS -> LESSONS Reddit: HOOK -> CONTEXT -> SUBSTANCE -> HONEST-TAKE

Map sections to the arc from templates/presets.json for the chosen content type.

Step 3: Create Section Scaffolding

For EACH section, define:

{
  "id": "section-slug",
  "title": "Short Punchy Title",
  "priority": 1,
  "status": "pending",
  "research_complete": false,
  "word_target": 0,
  "word_minimum": 0,
  "narrative_role": "from preset narrative_arc",
  "required_elements": {
    "examples_minimum": 2,
    "external_links_minimum": 3,
    "evidence_types": ["case study", "data point", "expert quote"]
  },
  "scaffold": {
    "opening_hook": "One compelling sentence that pulls reader in",
    "central_argument": "The main insight this section delivers",
    "key_questions": [
      "Why does this matter?",
      "What's the trade-off or nuance?",
      "When would you NOT do this?"
    ],
    "example_types": ["company case study", "named person", "research finding"],
    "closing_bridge": "Natural transition to next section"
  },
  "research_questions": [
    "Specific question 1?",
    "Specific question 2?",
    "What are the best real-world examples?"
  ]
}

Word targets per section: Distribute total_words across sections. Each section's word_target should be approximately total_words / section_count, but weight deep-dive sections heavier and intro/conclusion lighter.

Section word_minimum: Use the preset's quality_gates.min_words_per_section.

Title Guidelines:

• •NO colons separating concept from description
• •3-7 words, conversational tone
• •Should sound like something you'd say to a colleague

Good: "Where Good Prospects Hide" Bad: "Finding Prospects: Strategies and Techniques"

Good: "The Foundation Nobody Talks About" Bad: "Email Deliverability: Technical Setup and Best Practices"

Step 4: Identify Example Requirements

For each deep-dive section, specify:

• •Real-world examples to feature (actual companies, people, events)
• •Evidence appropriate to the article type (code for technical, data for analytical, case studies for business)
• •Comparisons (before/after, old vs new approach)

Scale the example requirements based on the content type. An email needs 2 examples total. A long-form article needs 10+.

Step 5: Create Article Directory

mkdir -p articles/[slug]/{research,output,drafts}

Step 6: Write article.json

{
  "topic": "Original topic",
  "slug": "url-slug",
  "article_type": "technical|business|marketing|opinion|tutorial|cultural",
  "content_type": "long-form|research|news|opinion|listicle|email|tutorial|case-study",
  "title": "Compelling Title That Promises Value",
  "subtitle": "More specific description of what reader learns",
  "summary": "3-4 sentence executive summary covering: the problem, the solution, what reader will learn, why it matters now",
  "status": "draft",
  "created": "YYYY-MM-DD",
  "custom_word_count": null,
  "targets": {
    "total_words": "from preset (or custom)",
    "minimum_words": "from preset (or custom * 0.8)",
    "sections": "from preset sections_min-sections_max range",
    "external_links": "from preset (scaled if custom words)",
    "real_world_examples": "from preset (scaled if custom words)"
  },
  "editorial_standards": {
    "prose_ratio_minimum": "from preset editorial.prose_ratio_minimum",
    "max_consecutive_bullets": "from preset editorial.max_consecutive_bullets",
    "header_style": "conversational, no colons"
  },
  "narrative_arc": {
    "hook": "Why this matters now",
    "tension": "The problem/challenge",
    "resolution": "How to solve it",
    "transformation": "What reader becomes capable of"
  },
  "sections": [],
  "quality_gates": {
    "min_words_per_section": "from preset quality_gates",
    "min_links_per_section": "from preset quality_gates",
    "min_examples_total": "from preset quality_gates",
    "narrative_flow_check": true,
    "prose_ratio_check": true
  },
  "research_config": {
    "sources_per_section": "from preset research.sources_per_section",
    "search_queries_min": "from preset research.search_queries_min",
    "include_video_research": "from preset research.include_video_research"
  },
  "sources": [],
  "metadata": {
    "target_audience": "Description of who this is for",
    "reading_time": "from preset reading_time_estimate",
    "depth": "from preset depth",
    "tags": []
  }
}

All values labeled "from preset" must be populated from templates/presets.json for the chosen content_type, scaled if a custom word count was given.

Step 7: Initialize progress.txt

# Article Progress Log
Topic: [topic]
Type: [article_type]
Format: [content_type]
Target: [total_words]+ words
Created: [date]

## Quality Gates
- [ ] All sections meet word minimums ([min_words_per_section]+)
- [ ] [external_links]+ external links
- [ ] [real_world_examples]+ real-world examples
- [ ] Narrative flow verified
- [ ] Prose ratio [prose_ratio_minimum]%+ (not bullet-heavy)
- [ ] Headers conversational (no colons)

## Iterations

Output Summary

After creating the PRD, report:

• •Article title and subtitle
• •Content type and article type detected
• •Custom word count (if specified) or preset default
• •Number of sections with word targets
• •Total target word count
• •Key examples to feature
• •Narrative arc summary
• •Next step: /research [first-section-id]

Quality Standard

This PRD sets the bar. Thin scaffolding produces thin sections. Invest time here to define:

• •Compelling hooks and natural bridges
• •Critical thinking questions (not just "what to cover")
• •Concrete example requirements appropriate to the topic
• •Headers that flow conversationally

A good PRD makes writing 10x easier.