x-tweet

name	x-tweet
description	Generate tweets that match the voice profile and resonate with your target audience. Tweets should feel like authentic observations from an experienced insider, not marketing content.

X Tweet Generator

Overview

Generate 3 tweets per day that sound like a clear-eyed insider sharing observations, not doing content marketing. Voice, audience, and topics are defined by your context profiles.

Before Writing

Step 1: Read Context

1. Read context profiles in /context/:

   • voice.json — How to sound
   • audience.json — Who you're writing for
   • Any business profile JSONs — Business context

2. Check engagement data in /knowledge/engagement/ for what's working

3. Check tweet history — Read /knowledge/content/tweet_history.json for previously written/posted tweets. Do NOT repeat the same topic or angle unless the user explicitly asks. If a topic was already covered, find a different angle or skip it entirely.

Step 2: Scrape Engagement (MANDATORY — run BEFORE research)

4. Scrape your profile — Run scrape_profile.py to get latest engagement data on recent tweets. Update engagement_log.json. Review what's working (likes, views, replies) and what's not. Use this to inform today's tweet strategy.

Step 3: Research (MANDATORY — use researcher-agent for all 3 in parallel)

5. Current tech news — Search for today's top tech headlines (AI, startups, VC, big tech). Get specific names, numbers, details.
6. Industry Twitter trends — Search for what's trending among your target audience, industry news, exits, funding, layoffs, controversies.
7. Topics you care about — Search for topics relevant to your voice profile and business profile (defined in context profiles).

Step 4: Write

8. Mix news-reactive and evergreen tweets. At least 1 tweet should react to a specific current event/news item. The others can be original pattern observations, but should be informed by what's happening now — not generic.

Tweet Types (Rotate Daily)

1. Pattern Observation (Primary — 40% of tweets)

Spot something in your industry and name the pattern.

• Start with a specific detail or observation
• Build to a broader insight
• Let the pattern speak for itself

2. Contrarian / Anti-Hype (25% of tweets)

Challenge conventional wisdom, call out spin, cut through noise.

• Mock discourse fatigue
• Question what everyone accepts
• Skeptical but not cynical

3. Founder/Insider Insight (20% of tweets)

Share practical wisdom from experience.

• Real operational insight, not motivational fluff
• Specific enough to be useful
• From experience, not theory

4. Cultural Commentary (15% of tweets)

Industry culture, work life, ecosystem observations.

• Natural language mixing if bilingual
• Pop culture timestamps
• Self-deprecating mundane contrasts

Voice Rules

ALWAYS

• Sound like someone who's been in the room
• Be a pattern spotter, not a preacher
• Be skeptical but not bitter
• Use simple language for sophisticated observations
• Short paragraphs, breathing room
• Trust the reader to connect the dots
• Follow voice.json for specific voice patterns
• When citing specific data/statistics, always attach the source with a link at the end of the tweet (e.g. "מקור: bloomberg.com/news/articles/...")

NEVER

• Management consultant speak
• Breathless hype ("game-changing", "revolutionary")
• Motivational speaker energy
• Corporate press release tone
• Humble-bragging
• Excessive emojis
• Thread announcements
• Generic advice

Signature Phrases

Use sparingly — pull from voice.json signature_concepts and colloquialisms.

Twitter-Specific Patterns

• Brevity: Cut connective tissue, not substance
• Single-line punch: One powerful observation, full stop
• Setup → Evidence: First tweet poses observation, thread continues with proof

Language

Defined by your voice.json. Supports:

• Primary language tweets
• Secondary language tweets for broader reach (1 in 3)
• Natural code-switching between languages

Length

• Ideal: 1-3 sentences (under 280 chars)
• Max: 4-5 sentences for thread starters
• Short and punchy beats long and complete

Daily Generation

Generate 3 tweets with variety across languages and tweet types.

Each tweet should be independent (not a thread) unless specifically requested.

Step 5: Humanize (MANDATORY — run AFTER writing)

9. Run humanizer — Apply the /humanizer skill to all tweets before presenting. Remove AI-isms, inject personality, ensure they sound like a real person wrote them.

Quality Checklist

Before delivering:

• Would this stop someone from scrolling?
• Does it sound like a person, not a brand?
• Is there a real insight, not just a take?
• Would your target audience share this?
• Is it under 280 characters?
• Does it match the voice profile?
• Has it been run through the humanizer?

Tweet History

After presenting tweets to the user, always update /knowledge/content/tweet_history.json with each tweet's date, topic, angle, status, and text preview. This prevents repeating the same topics/angles across sessions.

Engagement Learning

Check /knowledge/engagement/engagement_log.json for data on what works. Patterns to optimize for:

• Which tweet types get most engagement
• Which topics resonate
• Language performance differences
• Time-of-day patterns
• What language/phrases trigger shares