X Posting Workflow

End-to-end workflow for creating, approving, and scheduling X/Twitter posts from OpenEd content.

When to Use

• Batch processing X posts from recent content

• Promoting podcast episodes or blog posts

• Scheduling a week of social content

• Any X posting that involves guest tagging

Prerequisites

• GETLATE_API_KEY in .env

• Connected Twitter account in getlate.dev (@OpenEdHQ)

The Workflow

Step 1: Find Content

Search the Master Content Database for recent/relevant content:

Find recent podcasts

ls "Content/Master Content Database/Podcasts/" | head -20

Find recent blog posts

ls "Content/Master Content Database/Blog Posts/" | head -20

Read source files to extract:

• Key insights and quotes

• Guest names

• Actual URLs (from frontmatter url: field)

CRITICAL: Never hallucinate URLs. Always pull from the source file's frontmatter.

Step 2: Find Guest X Handles

Before writing any post featuring a guest:

• Web search: "[Guest Name] [Company] Twitter X account"

• Verify it's the correct person (check bio)

• Note both personal handle AND company handle if applicable

Common OpenEd guest handles:

• See Studio/Social_Scheduling.md for previously found handles

• Add new handles to the reference table when found

Step 3: Framework Fitting via Sub-Agents

CRITICAL: Spawn a sub-agent for EACH piece of content.

This is token-heavy but ensures quality. Each sub-agent does deep framework fitting rather than surface-level template matching.

Sub-Agent Prompt Template:

You are writing X/Twitter posts for OpenEd. Your job is DEEP FRAMEWORK FITTING - matching this concept to proven templates that amplify it.

Source Content

[Paste the full content or key excerpts]

Guest Info

• Name: [Guest Name]
• X Handle: @[handle]
• Company: [Company] (@[company_handle] if applicable)

URL (use exactly)

[URL from frontmatter]

Your Task

Phase 1: Concept Extraction

Extract from this content:

1. Core insight (one sentence)
2. Emotional hook (what feeling does this evoke?)
3. Most quotable moment
4. Surprising fact or statistic
5. Contrarian angle (what does this challenge?)

Phase 2: Template Review

Read the FULL post-structures.md file. Do not skim.

For this concept, evaluate AT LEAST these template categories:

• Commentary (quote + translation + explanation)
• Contrast Evaluation (X overrated, Y underrated)
• Paradox Hook (present contradiction)
• Comparison with percentages (numbers + question)
• Personal Story (setup → conflict → resolution)
• One Sentence Comparison
• Behavior Dichotomy
• Binary Framing Hook
• Counterintuitive Statement Hook

For each potentially fitting template:

1. Name the template
2. Explain WHY it might work for this concept
3. Rate fit: Strong / Moderate / Weak
4. Draft a quick test version

Phase 3: Select and Write

Choose 2-3 BEST fitting templates. Write full post variations using the EXACT template structure.

Phase 4: Voice Validation

For each draft, check against AI-tells:

HARD BLOCKS (reject and rewrite if present):

• Correlative constructions ("X isn't just Y - it's Z")
• Split correlatives ("It wasn't about X. It was about Y.")
• Staccato fragments ("No X. No Y. Just Z.")
• Setup phrases ("Here's the thing:", "The best part?")
• Triple adjective patterns
• Thesaurus words (utilize, leverage, comprehensive, crucial)

VOICE CHECK:

• Does this sound like a person talking or performing "good writing"?
• Is this something you'd actually text to a friend?
• Does it follow the template structure or drift into generic copy?

Output Format

For each post option:

1. Template used: [Name]
2. Why this template: [1-2 sentences]
3. Post:

[The actual post text]

[link]

4. Voice validation: [Pass/Fail with notes]

Launch sub-agents in parallel - one per content piece. They will return structured options ready for the scheduling file.

Step 4: Compile Scheduling File

Collect sub-agent outputs into Studio/Social_Scheduling.md :

X Post Scheduling Queue

Created: [date] Status: Awaiting approval

1. [Content Title] - @[guest_handle]

URL: [actual URL from source]

Option A - [Template Name]:

[Post content]

[link]

Template reasoning: [Why this template fits]

• Approve Option A

Option B - [Template Name]:

[Post content]

[link]

Template reasoning: [Why this template fits]

• Approve Option B

User marks approvals with [X] and adds notes in {NOTES: ...} .

Step 5: Voice Validation (Final Check)

Before posting/scheduling, run final validation:

Hard Blocks:

• No correlative constructions ("X isn't just Y - it's Z")

• No split correlatives ("X wasn't about Y. It was about Z.")

• No staccato fragments ("No X. No Y. Just Z.")

• No setup phrases ("Here's the thing:", "The best part?")

Soft Checks:

• Sounds like a person, not a copywriter

• Uses actual template structure (can identify which one)

• Guest tagged correctly

• URL is real (from source file)

Step 6: Post and Schedule via getlate.dev

Post immediately:

python3 agents/post_tweet.py '[tweet content]'

Schedule for future:

import os import requests from pathlib import Path from dotenv import load_dotenv

load_dotenv(Path(file).parent.parent / ".env")

api_key = os.getenv("GETLATE_API_KEY") base_url = "https://getlate.dev/api/v1" headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}

Get account ID

response = requests.get(f"{base_url}/accounts", headers=headers) accounts = response.json().get("accounts", []) twitter_account = next((a for a in accounts if a.get("platform") == "twitter"), None) account_id = twitter_account.get("_id")

Schedule post (9am ET = 14:00 UTC in winter)

post_data = { "platforms": [{"platform": "twitter", "accountId": account_id}], "content": "Your tweet content here", "scheduledFor": "2026-01-15T14:00:00Z" # ISO 8601 format }

response = requests.post(f"{base_url}/posts", headers=headers, json=post_data)

Time conversion:

• 9am ET (winter) = 14:00 UTC

• 9am ET (summer/DST) = 13:00 UTC

Quick Reference

getlate.dev API

Endpoint Method Purpose

/accounts

GET List connected accounts

/posts

POST Create/schedule post

Post payload:

{ "platforms": [{"platform": "twitter", "accountId": "..."}], "content": "Tweet text", "publishNow": true, // OR "scheduledFor": "ISO-8601" // for scheduling }

Best Posting Times (ET)

• 8-9am - Morning commute

• 12-1pm - Lunch break

• 7-8pm - Evening wind-down

Character Limits

• Optimal: 70-100 characters

• Max: 280 characters

• With link: ~250 characters of text

Sub-Agent Required Reading

Each sub-agent MUST read these files before writing posts:

File Purpose Tokens

.claude/skills/text-content/references/templates/post-structures.md

100+ template structures ~4000

.claude/skills/ai-tells/SKILL.md

Voice constraints ~200

.claude/skills/ai-tells/references/pirate-wires-examples.md

Voice examples ~1500

Source content file The actual content to promote varies

Why this matters: The quality difference between skimming templates and deeply reading them is the difference between generic AI copy and posts that actually follow proven structures.

Related Skills

• text-content

• Template library and framework fitting

• ai-tells

• Voice validation

• ghostwriter

• Deeper voice guidance

Files

• agents/post_tweet.py

• Single tweet posting

• agents/test_tweet.py

• API testing

• Studio/Social_Scheduling.md

• Current queue

Last updated: 2026-01-14 (v2 - added sub-agent framework fitting)