# Find recent podcasts
ls "Content/Master Content Database/Podcasts/" | head -20

# Find recent blog posts
ls "Content/Master Content Database/Blog Posts/" | head -20

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]

# 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

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

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)

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