Content Pipeline

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name: Content Pipeline version: 1.0.0 description: | 4-stage content pipeline orchestrator: Research -> Ideate -> Write -> Queue. Give it a topic, it researches existing discussions, generates hook angles, writes a draft, and queues it for review. Inspired by @shannholmberg's 4-Agent content system (Research -> Ideate -> Write -> Orchestrate). Designed for creators who build in public and want systematic content production. when_to_use: when creating original content that needs research, angle selection, and drafting from scratch trigger: /pipeline languages: all attribution: Inspired by @shannholmberg's 4-Agent content system. Pipeline architecture is original. allowed-tools: - Read - Write - Edit - Bash - Grep - Glob - Agent - AskUserQuestion

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Content Pipeline Orchestrator

> One command, from topic to review-ready draft. > Research -> Ideate -> Write -> Queue

When to use vs. not

Use pipeline (original content that needs research):

Writing from scratch on a topic you haven't deeply explored

Need to survey existing discussion, find data, pick an angle

Example: "write about the impact of MoE on local inference" / "year-end market review"

Don't use pipeline (already have material):

Quoting someone else's post -> just write directly

Replying/commenting -> just write directly

Polishing an existing draft -> just edit directly

These scenarios waste 4-5x tokens through the pipeline with zero benefit

File locations

Configure these paths for your project:

| File | Purpose | |------|---------| | ./content-queue.json | Idea lifecycle state | | ./research/ | Research results (by date + slug) |

Commands

Parse user input, match first hit:

| Input | Command | Action | |-------|---------|--------| | /pipeline | run | Full pipeline: research -> ideate -> write -> queue | | /pipeline url | url | Extract from URL -> ideate -> write -> queue | | /pipeline seed | seed | Add raw idea to queue as seed | | /pipeline status | status | Show queue grouped by status | | /pipeline review | review | Show a draft for review | | /pipeline approve | approve | Mark as approved | | /pipeline adapt | adapt | Generate platform variant | | /pipeline publish | publish | Mark as published + timestamp | | /pipeline clean | clean | Archive items published 30+ days ago |

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Queue data model

File: ./content-queue.json

{
  "ideas": [
    {
      "id": 1,
      "topic": "AI Agent end-to-end automation",
      "status": "drafted",
      "platform": "twitter",
      "created": "2026-03-03T15:00:00Z",
      "updated": "2026-03-03T15:05:00Z",
      "research_file": "research/20260303-ai-agent-automation.md",
      "hook_angle": "Builder perspective: Writing is easy, Research is the bottleneck",
      "draft": "This person built a full...",
      "variants": {},
      "source_url": null,
      "feedback": [],
      "published": null
    }
  ],
  "next_id": 2
}

Status flow: seed -> researched -> drafted -> approved -> published -> archived

Queue read/write rules

1. Read: Read ./content-queue.json 2. Write: Write back complete JSON (single-user, no concurrency issue) 3. ID assignment: Use next_id, increment after write 4. Timestamps: ISO 8601 with timezone

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Command details

/pipeline -- Full Pipeline

Input: topic (keywords or short phrase)

#### Stage 1: Research

1. Search for existing discussion on the topic using available search tools: - Twitter/X search for relevant posts and threads - Web search for articles and data - Any domain-specific sources you have access to 2. Compile findings into a research file:

   ./research/YYYYMMDD-{slug}.md
   

slug = topic keywords, lowercase with hyphens, max 30 chars

Research file format:

# Research: {topic}
Date: YYYY-MM-DD
Sources: [list search methods used]
Key findings
[Finding 1 + source attribution]
[Finding 2 + data/numbers]
[Finding 3 + opposing viewpoint]
Notable posts/articles
1. @user1 (N likes): "Core point summary"
2. @user2 (N likes): "Core point summary"
Data points
[Specific numbers, comparisons, statistics]
Opposing viewpoints
[Contrarian takes, if any]
Source links
[List of original URLs]

#### Stage 2: Ideate

1. Read the research file 2. Generate 3 hook angles based on the research:

Angle generation prompt (adapt for your LLM of choice):

You are a content strategist. Based on the following research, generate 3 hook angles for a post.
Research:
{research file content}
Requirements:
1. Each angle includes:
   - Hook type (contrast / counterintuitive / data-driven / story / question)
   - Core thesis (one sentence)
   - Key supporting points (2-3)
   - Estimated virality score (1-5)
2. Match the creator's voice and domain expertise
3. Avoid: AI cliches, marketing speak, listicle format
Output as JSON array:
[{"type": "contrast", "thesis": "...", "supports": ["...", "..."], "score": 4}, ...]

3. Select the highest-scored angle 4. If multiple angles tie, present options for user to choose

#### Stage 3: Write

1. Write the draft using the selected hook angle + research data points 2. Content format routing: - Content <= 280 chars -> short post (tweet) - 280-2000 chars -> long post (thread) - > 2000 chars -> article 3. Apply your preferred writing style/voice (integrate with a style skill if you have one) 4. Verify all claims have source attribution from the research

#### Stage 4: Queue

1. Read content-queue.json 2. Create new entry: - status: "drafted" - platform: target platform - research_file: relative path - hook_angle: selected angle description - draft: written text 3. Write back content-queue.json 4. Output confirmation:

   Pipeline complete -- queued #
   Topic: 
   Hook: 
   Draft: ...
   Format: short / long / article
   Use /pipeline review  to see full content
   

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/pipeline url -- From URL input

1. Fetch the URL content using available tools 2. Extract core arguments and data points 3. Skip Stage 1 (use extracted content as research) 4. Continue to Stage 2 (ideate) -> Stage 3 (write) -> Stage 4 (queue) 5. Record source_url in the entry

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/pipeline seed -- Add raw seed

1. Create queue entry: - status: "seed" - topic: the idea text - draft: null (seeds have no draft yet) 2. Output: Seed added to queue #

Seeds are raw ideas waiting to be developed. Run /pipeline later to expand a seed through the full pipeline.

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/pipeline status -- Queue status

Read content-queue.json, output grouped by status:

Content Pipeline Status
Seed (N):
  #3 "Multi-agent orchestration" -- 3/3 15:00
Drafted (N):
  #1 "AI Agent automation" -- 3/3 15:05
  #2 "Market arbitrage math" -- 3/3 16:20
Approved (N):
  #5 "MCP practical experience" -- 3/2 20:00
Published (N):
  #4 "Three-layer scraping approach" -- 3/1
Total: N items | Pending: seed(N) + drafted(N)

Show only non-archived items. If over 20 items, show most recent 20 + total count.

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/pipeline review -- Review

1. Find the entry in queue 2. Display full info:

Review #
Topic: 
Status: 
Hook: 
Created: 
--- Draft ---
--- Variants ---
[list any platform variants]
--- Research ---
File: 
[first 5 key findings if research file exists]
Actions:
  /pipeline approve  -- approve for publishing
  /pipeline adapt   -- generate platform variant

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/pipeline approve -- Approve

1. Change status to "approved" 2. Update updated timestamp 3. Output: # approved -- ready to publish

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/pipeline adapt -- Multi-platform adaptation

Adapt the draft for a different platform:

1. Read the entry's draft 2. Rewrite for the target platform's conventions: - Different character limits - Different audience expectations - Different formatting norms 3. Store in variants. field 4. Output: variant generated -- /pipeline review to see

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/pipeline publish -- Publish marker

1. Change status to "published" 2. Record published timestamp 3. Output: # marked as published

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/pipeline clean -- Archive cleanup

1. Scan all published entries 2. Archive entries older than 30 days 3. Output: Archived N old entries

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Design principles

Research and Ideate stages are platform-agnostic -- only the Write stage adapts for platform

One research effort can produce content for multiple platforms ("one fish, many meals")

Drafts should be source-verified before entering the queue -- no unsourced claims

Seeds are cheap to capture, expensive to develop -- capture freely, develop selectively

The pipeline is a framework, not a straitjacket -- skip stages when you already have what you need