Twitter Agent

Twitter/X social management agent. Extracts feed, scores posts for relevance, and creates draft responses for the user's approval.

Critical Rules

•DRAFTS ONLY - Never auto-post anything. All content must be approved by the user.
•TOP 0.01% ONLY - Only surface posts scoring > 0.9 (extremely selective)
•Follow Operating Manual - See references/operating-manual.md for voice, audience, and approval gates

Quick Start

bash

# Extract posts from timeline (requires Playwright MCP login)
python twitter.py extract --limit 20

# Score unscored posts (surfaces high-relevance only)
python twitter.py score-feed

# View high-relevance posts
python twitter.py high-relevance

# List pending drafts
python twitter.py drafts

# Create a draft reply (does NOT post)
python twitter.py draft-reply POST_ID "Response text"

Workflow

code

Timeline ─> Extract ─> Score ─> Present High-Relevance ─> User Reviews
                                        │
                            Create Drafts (if warranted)
                                        │
                              User Approves/Rejects
                                        │
                              User Posts Manually

Setup: Playwright MCP

Twitter extraction requires Playwright MCP with persistent login:

bash

# Install Playwright MCP with persistent browser session
claude mcp add playwright -- npx @playwright/mcp@latest --user-data-dir ~/.playwright-data/twitter

# First time: log in via the browser
# Subsequent runs: session persists

Target Audience (Score HIGH)

Per the user's Operating Manual, prioritize these audiences:

•AI for science researchers with serious engineering taste
•HPC, scientific computing, data infra engineers
•Lab automation and autonomous experimentation operators
•Hard tech founders and early employees (materials, energy, defense)
•Defense and energy program people who care about capability
•Deep tech investors with real full stack instincts
•Materials science graduate students and postdocs
•Industry R&D engineers (batteries, catalysts, semiconductors, aerospace)

Score LOW (Avoid)

•Generic AI hype accounts
•Engagement farmers
•People who only want vibes and hot takes
•Content about generic AI trends, not scientific computing

Engagement Rules

The user should ONLY engage when he can:

•Add a missing system detail or infrastructure layer
•Ask a pointed, specific question
•Clarify a technical distinction
•Provide HPC/simulation workflow insight
•Build a useful relationship with someone in target audience

Functions

Feed Extraction

python

from twitter import extract_feed, store_extracted_post

# Get extraction instructions (uses Playwright MCP)
result = extract_feed(limit=20)
# Follow the instructions with browser tools

# Store each extracted tweet
store_extracted_post(
    post_id="1234567890",
    author_name="Display Name",
    author_handle="username",
    content="Tweet content here",
    engagement={"likes": 50, "retweets": 10}
)

Relevance Scoring

python

from twitter import score_relevance, score_feed, get_high_relevance_posts

# Score a single post
result = score_relevance(post)
# Returns: {score, audience_match, engagement_fit, reasoning, suggested_action}

# Score all unscored posts and get high-relevance ones (>0.9)
high_relevance = score_feed(min_score=0.9)

# Get already-scored high-relevance posts
posts = get_high_relevance_posts(min_score=0.9)

Draft Creation (NEVER Auto-Posts)

python

from twitter import draft_reply, draft_quote, draft_dm, get_pending_drafts

# Create draft reply to a post
result = draft_reply(post_id=42, suggested_text="Great point about HPC...")
# Returns: {success, draft_id, message: "the user must review and post manually"}

# Create draft quote tweet
result = draft_quote(post_id=42, suggested_text="This connects to...")

# Create draft DM
result = draft_dm(handle="username", suggested_text="Quick question...")

# List all pending drafts for the user to review
drafts = get_pending_drafts()

Draft Management

python

from twitter import approve_draft, reject_draft

# the user approves a draft (still must manually post)
approve_draft(draft_id=1)

# the user rejects a draft
reject_draft(draft_id=1)

CLI Commands

bash

# Extract tweets from timeline
python twitter.py extract [--limit N]

# Store a tweet manually
python twitter.py store POST_ID --author "Name" --handle "username" --content "Text"

# Score all unscored posts
python twitter.py score-feed [--min-score 0.9] [--show-all]

# Score a single post
python twitter.py score-post DB_ID

# Show high-relevance posts
python twitter.py high-relevance [--min-score 0.9]

# List pending drafts
python twitter.py drafts

# Create drafts (DOES NOT POST)
python twitter.py draft-reply POST_ID "Response text"
python twitter.py draft-quote POST_ID "Quote text"
python twitter.py draft-dm HANDLE "DM text"

# Approve/reject drafts
python twitter.py approve DRAFT_ID
python twitter.py reject DRAFT_ID

When User Says...

User Says	Action
"Check my Twitter"	Extract feed, score, show high-relevance posts
"Any good engagement opportunities?"	`python twitter.py high-relevance`
"Draft a reply to that post"	`draft_reply(post_id, text)` + show draft
"Reply to @username about X"	Search posts, create `draft_reply`
"DM that person"	`draft_dm(handle, text)`
"Show pending drafts"	`python twitter.py drafts`
"Approve draft 5"	`approve_draft(5)` + remind the user to post manually

Approval Gates (From Operating Manual)

Can post without asking:

•Technical observations with no sensitive claims
•Neutral questions
•Replies that are not political and not personal

Must get user approval on:

•Any thread
•Anything that names a person as a claim or critique
•Anything that references a partner, lab, employer, or program
•Anything with a strong defense or policy stance
•Anything mentioning the user's product names, fundraising, hiring, or partnerships

Voice Guidelines (From Operating Manual)

•Direct, technical, calm
•Curious and skeptical
•High signal, low drama
•Precise about what is known vs assumed
•Short sentences
•No emoji unless it adds clarity (max one)
•No hashtags
•No em dashes

Ban list:

•programmable matter (unless milestone)
•revolutionize, disrupt, democratize, game changer, huge

Database Tables

social_feed

Stores all extracted posts:

Column	Description
id	Database ID
platform	Always "twitter"
post_id	Twitter's post ID
author_name	Display name
author_handle	@handle (no @)
content	Tweet text
engagement	JSON: {likes, retweets, replies}
relevance_score	0.0-1.0 from scoring
suggested_action	reply/quote/like/ignore
action_taken	What was done
captured_at	When extracted

twitter_drafts

Stores draft responses:

Column	Description
id	Draft ID
post_id	FK to social_feed (for replies/quotes)
draft_type	reply/quote/dm
target_handle	For DMs
draft_text	Draft content
status	pending/approved/rejected
created_at	When drafted
approved_at	When approved

Related Skills

•browser-automation: How to use Playwright MCP
•vault-operations: Captures for context
•relationship-intelligence: Track people engaged with
•pcp-operations: System overview

References

•references/operating-manual.md - the user's full X Operating Manual

Remember: This is a DRAFT-ONLY system. Never auto-post. the user must manually post all content.