Twitter Hand Skill

Twitter/X Management Expert Knowledge

Twitter API v2 Reference

Authentication

Twitter API v2 uses OAuth 2.0 Bearer Token for app-level access and OAuth 1.0a for user-level actions.

Bearer Token (read-only access + tweet creation):

Authorization: Bearer $TWITTER_BEARER_TOKEN

Environment variable: TWITTER_BEARER_TOKEN

Authentication modes:

• Bearer Token only (default): Sufficient for posting tweets, reading timelines, and searching. All core functionality works with just the Bearer Token.
• Bearer Token + OAuth 1.0a (optional): Required for user-context operations such as liking tweets, retweeting, following/unfollowing, and accessing DMs. Set TWITTER_API_KEY, TWITTER_API_SECRET, TWITTER_ACCESS_TOKEN, and TWITTER_ACCESS_TOKEN_SECRET to enable these features.

Note: The Like endpoint (POST /2/users/:id/likes) and Retweet endpoint (POST /2/users/:id/retweets) require OAuth 1.0a User Context authentication. If only Bearer Token is configured, these operations will be skipped with a warning.

Core Endpoints

Get authenticated user info:

curl -s -H "Authorization: Bearer $TWITTER_BEARER_TOKEN" \
  "https://api.twitter.com/2/users/me"

Response: {"data": {"id": "123", "name": "User", "username": "user"}}

Post a tweet:

curl -s -X POST "https://api.twitter.com/2/tweets" \
  -H "Authorization: Bearer $TWITTER_BEARER_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"text": "Hello world!"}'

Response: {"data": {"id": "tweet_id", "text": "Hello world!"}}

Post a reply:

curl -s -X POST "https://api.twitter.com/2/tweets" \
  -H "Authorization: Bearer $TWITTER_BEARER_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"text": "Great point!", "reply": {"in_reply_to_tweet_id": "PARENT_TWEET_ID"}}'

Post a thread (chain of replies to yourself):

1. Post first tweet → get tweet_id
2. Post second tweet with reply.in_reply_to_tweet_id = first tweet_id
3. Repeat for each tweet in thread

Delete a tweet:

curl -s -X DELETE "https://api.twitter.com/2/tweets/TWEET_ID" \
  -H "Authorization: Bearer $TWITTER_BEARER_TOKEN"

Like a tweet:

curl -s -X POST "https://api.twitter.com/2/users/USER_ID/likes" \
  -H "Authorization: Bearer $TWITTER_BEARER_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"tweet_id": "TARGET_TWEET_ID"}'

Get mentions:

curl -s -H "Authorization: Bearer $TWITTER_BEARER_TOKEN" \
  "https://api.twitter.com/2/users/USER_ID/mentions?max_results=10&tweet.fields=public_metrics,created_at,author_id"

Search recent tweets:

curl -s -H "Authorization: Bearer $TWITTER_BEARER_TOKEN" \
  "https://api.twitter.com/2/tweets/search/recent?query=QUERY&max_results=10&tweet.fields=public_metrics"

Get tweet metrics:

curl -s -H "Authorization: Bearer $TWITTER_BEARER_TOKEN" \
  "https://api.twitter.com/2/tweets?ids=ID1,ID2,ID3&tweet.fields=public_metrics"

Response includes: retweet_count, reply_count, like_count, quote_count, bookmark_count, impression_count

Rate Limits

Always check response headers:

• x-rate-limit-limit: Total requests allowed
• x-rate-limit-remaining: Requests remaining
• x-rate-limit-reset: Unix timestamp when limit resets

Algorithm Optimization

The following signals are known to affect distribution as of 2024-2025. Treat as heuristics -- validate against your own account's data.

Signals That Boost Distribution

Signals That Suppress Distribution

Algorithm-Aware Posting Strategy

1. Test before committing: Post a single tweet on a topic. If engagement is above-average in 1 hour, follow up with a thread within 24 hours.
2. Reply to yourself: Add a reply with a link or context. This creates a conversation thread that boosts the original.
3. Engagement window: Reply to comments on your tweets within the first hour. Reply chains are rewarded.
4. Content recycling: A tweet that performed well 3+ months ago can be reposted with fresh wording.

Media Upload Handling

Twitter API v2 Media Upload

Twitter API v2 has no media upload endpoint. Use the v1.1 endpoint, which requires OAuth 1.0a.

Simple upload (images < 5MB, GIFs < 15MB):

curl -X POST "https://upload.twitter.com/1.1/media/upload.json" \
  -H "Authorization: OAuth oauth_consumer_key=...,oauth_token=...,oauth_signature=..." \
  -F "media=@/path/to/image.png"

Response: {"media_id": 123456789, "media_id_string": "123456789"}

Attach media to a tweet:

curl -s -X POST "https://api.twitter.com/2/tweets" \
  -H "Authorization: Bearer $TWITTER_BEARER_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"text": "Check this out", "media": {"media_ids": ["123456789"]}}'

Alt text for accessibility (set after upload, before tweeting):

curl -X POST "https://upload.twitter.com/1.1/media/metadata/create.json" \
  -H "Authorization: OAuth ..." \
  -H "Content-Type: application/json" \
  -d '{"media_id": "123456789", "alt_text": {"text": "Description of the image"}}'

When to Use Media

• Data/stats tweets: Chart or highlighted number as image -- 2-3x more impressions
• Thread hooks: Image in tweet 1 increases click-through
• Code snippets: Screenshot with syntax highlighting beats plain text
• Before/after: Visual comparisons are highly shareable

When to Skip Media

• OAuth 1.0a credentials not configured (Bearer Token alone cannot upload)
• Image does not add information beyond the text
• Attaching media would delay posting past the optimal window

Content Strategy Framework

Content Pillars

Define 3-5 core topics ("pillars") that all content revolves around:

Example for a tech founder:
  Pillar 1: AI & Machine Learning (40% of content)
  Pillar 2: Startup Building (30% of content)
  Pillar 3: Engineering Culture (20% of content)
  Pillar 4: Personal Growth (10% of content)

Content Mix (7 types)

Optimal Posting Times (UTC-based, adjust to audience timezone)

Tweet Writing Best Practices

The Hook (first line is everything)

Hooks that work:

• Contrarian: "Most people think X. They're wrong."
• Number: "I analyzed 500 [things]. Here's what I found:"
• Question: "Why do 90% of [things] fail?"
• Story: "In 2019, I almost [dramatic thing]."
• How-to: "How to [desirable outcome] without [common pain]:"
• List: "5 [things] I wish I knew before [milestone]:"
• Confession: "I used to believe [common thing]. Then I learned..."

Writing Rules

1. One idea per tweet — don't try to cover everything
2. Front-load value — the hook must deliver or promise value
3. Use line breaks — no wall of text, 1-2 sentences per line
4. 280 character limit — every word must earn its place
5. Active voice — "We shipped X" not "X was shipped by us"
6. Specific > vague — "3x faster" not "much faster"
7. End with a call to action — "Agree? RT" or "What would you add?"

Thread Structure

Tweet 1 (HOOK): Compelling opening that makes people click "Show this thread"
  - Must stand alone as a great tweet
  - End with "A thread:" or "Here's what I found:"

Tweet 2-N (BODY): One key point per tweet
  - Number them: "1/" or use emoji bullets
  - Each tweet should add value independently
  - Include specific examples, data, or stories

Tweet N+1 (CLOSING): Summary + call to action
  - Restate the key takeaway
  - Ask for engagement: "Which resonated most?"
  - Self-reference: "If this was useful, follow @handle for more"

Hashtag Strategy

• 0-2 hashtags per tweet (more looks spammy)
• Use hashtags for discovery, not decoration
• Mix broad (#AI) and specific (#LangChain)
• Never use hashtags in threads (except maybe tweet 1)
• Research trending hashtags in your niche before using them

Engagement Playbook

Replying to Mentions

Rules:

1. Respond within 2 hours during engagement_hours
2. Add value — don't just say "thanks!" — expand on their point
3. Ask a follow-up question — drives conversation
4. Be genuine — match their energy and tone
5. Never argue — if someone is hostile, ignore or block

Reply templates:

• Agreement: "Great point! I'd also add [related insight]"
• Question: "Interesting question. The short answer is [X], but [nuance]"
• Disagreement: "I see it differently — [respectful counterpoint]. What's your experience?"
• Gratitude: "Appreciate you sharing this! [Specific thing you liked about their tweet]"

When NOT to Engage

• Trolls or obviously bad-faith arguments
• Political flame wars (unless that's your content pillar)
• Personal attacks (block immediately)
• Spam or bot accounts
• Tweets that could create legal liability

Auto-Like Strategy

Like tweets from:

1. People who regularly engage with your content (reciprocity)
2. Influencers in your niche (visibility)
3. Thoughtful content related to your pillars (curation signal)
4. Replies to your tweets (encourages more replies)

Do NOT auto-like:

• Controversial or political content
• Content you haven't actually read
• Spam or low-quality threads
• Competitor criticism (looks petty)

Advanced Engagement Patterns

Quote Tweet vs Reply vs Retweet

Choosing the right interaction type determines whether you gain visibility or waste it.

Use a Quote Tweet when:

• You have a distinct take or added context (not just "this!")
• The original tweet has high impressions and you want to draft off its reach
• You are crediting someone while adding your own insight for your audience
• The original author has a similar or larger following (exposes you to their audience)

Use a Reply when:

• You want to build a direct relationship with the author
• Your comment only makes sense in context of the original
• The original author has a much larger following (replies show on their thread, giving you visibility without looking self-promotional)
• You are answering a question or adding a correction

Use a plain Retweet when:

• The original says everything perfectly and you have nothing to add
• You want to signal-boost a community member, customer, or partner
• The content is time-sensitive (breaking news, event announcements)

Avoid:

• Quote tweeting with only emojis or "this" -- adds no value, looks lazy
• Quote tweeting someone with fewer followers just to dunk -- punching down
• Retweeting more than 3-4 times per day -- dilutes your original content ratio

Thread Repurposing

A thread that performed well contains 5-7 standalone content pieces. Extract them over the following week to maximize ROI.

Process:

1. Day 0 (original): Post the full thread
2. Day 2: Pull the single most quotable tweet from the thread. Post it standalone with slightly different wording. No link back to the thread
3. Day 4: Turn a data point or example from the thread into a graphic or screenshot tweet
4. Day 6: Post the thread's core thesis as a hot take (one tweet, punchy)
5. Day 8+: If engagement stayed strong, post a "Part 2" thread that goes deeper on whichever tweet in the original got the most replies

Rules:

• Change the wording each time -- copy-pasting feels like spam to followers who saw the original
• Space extractions at least 48 hours apart
• Stop if any extraction underperforms significantly -- the topic is tapped out
• Never repurpose a thread that got low engagement; the content did not resonate

Trending Topic Participation

When to participate:

• The trend directly intersects one of your content pillars
• You have a genuine, informed perspective (not a generic reaction)
• The trend is still rising (check the "Trending" tab; if it has been trending for >12 hours, you are late)
• The tone of the trend matches your brand voice

When to avoid:

• Tragedy, disaster, or crisis events -- opportunistic posting destroys trust
• Highly polarized political or social debates outside your expertise
• Trends driven by outrage mobs -- associating your brand is high-risk, low-reward
• You would need to force-fit your product or message into the trend

Execution:

• Lead with your actual insight, not the hashtag. The hashtag goes at the end or is omitted entirely if the topic keyword is in your text
• Be early or be different. If 50 people have already made the same joke, skip it
• Tie back to your pillar: "Trend X is exactly why [your pillar topic] matters more than ever"

Crisis & Negative Comment Management

Classifying Negative Interactions

Not all negative replies require the same response. Classify before acting:

Response Templates

• Constructive criticism: "Fair point — [acknowledgment]. We actually [explanation]. Appreciate you raising this."
• Frustrated user: "Sorry you hit that. Can you share [detail]? Happy to help sort it out."
• Factual correction: "To clarify — [correct info with source]. Happy to discuss further."

Rules During a Crisis

1. Stop all scheduled posts immediately — auto-posting during a crisis looks tone-deaf
2. Do not delete the original tweet unless it contains genuinely harmful misinformation
3. Acknowledge the situation in a single, clear tweet if it involves your product/brand
4. Do not be defensive — own mistakes directly
5. Wait before responding — draft a response and review it after 1 hour
6. Resume normal posting only after the situation has cooled down (24-48 hours minimum)

Content Calendar Template

WEEK OF [DATE]

Monday:
  - 8 AM: [Tip/How-to] about [Pillar 1]
  - 12 PM: [Curated share] related to [Pillar 2]

Tuesday:
  - 10 AM: [Thread] deep dive on [Pillar 1]
  - 2 PM: [Hot take] about [trending topic]

Wednesday:
  - 9 AM: [Question] to audience about [Pillar 3]
  - 1 PM: [Data/Stat] about [Pillar 2]

Thursday:
  - 10 AM: [Story] about [personal experience in Pillar 3]
  - 3 PM: [Tip/How-to] about [Pillar 1]

Friday:
  - 9 AM: [Hot take] about [week's trending topic]
  - 11 AM: [Curated share] — best thing I read this week

Worked Examples

Example 1: Product Launch Twitter Campaign (1-Week Plan)

Context: A dev tools startup is launching "FastDB," an open-source embedded database. The account has 2,400 followers, mostly backend engineers.

Pre-launch (3 days before):

• Seed curiosity without revealing the product name
• Engage heavily in database-related threads to increase profile visits before launch

Day 1 (Monday) -- Teaser:

We've been heads-down for 8 months building something
we think embedded databases have been missing.

Shipping it open-source this Thursday.

More soon.

Purpose: Create anticipation. No hashtags, no links. Let curiosity drive profile visits.

Day 2 (Tuesday) -- Problem framing:

SQLite is incredible for what it does.

But if you need concurrent writes, ACID transactions,
AND sub-millisecond reads in the same embedded DB...
your options get thin fast.

We've been living in that gap. Fix incoming Thursday.

Purpose: Define the problem space. People who feel this pain will follow for the reveal.

Day 3 (Wednesday) -- Social proof / build-up:

Shipped our embedded DB to 12 beta testers last month.

Results so far:
- 4.2x faster concurrent writes vs SQLite WAL mode
- Zero-config replication
- Single static binary, 3.8 MB

One more day.

Purpose: Concrete numbers build credibility. "One more day" maintains tension.

Day 4 (Thursday) -- Launch day thread (6-tweet thread):

1/6 [HOOK]: Introducing FastDB -- embedded DB for concurrent-write-heavy
     workloads. Open source. Single binary. Here's why we built it:
2/6 [PROBLEM]: SQLite = single-writer. Fine for reads, hits a wall on
     write-heavy apps (event logging, IoT, realtime sync). FastDB uses
     MVCC -- writers never block readers, readers never block writers.
3/6 [PROOF]: Benchmarks (M2 Mac, 8 threads): concurrent writes 51K ops/s
     vs SQLite WAL 12K ops/s. Point reads on par at ~900K ops/s.
4/6 [ONBOARD]: Getting started: `cargo add fastdb` then 3 lines of code.
     Full SQLite-compatible query layer coming in v0.2.
5/6 [ROADMAP]: v0.1 ships ACID transactions, built-in replication, crash
     recovery, zero deps beyond libc. v0.2: SQL layer, S3 cold storage.
6/6 [CTA]: Star the repo: github.com/example/fastdb -- open issues, roast
     the benchmarks, tell us what's missing.

Key structural choices: tweet 1 is a standalone hook, tweet 3 has hard numbers, tweet 6 ends with a specific ask (not just "check it out").

Day 4 afternoon -- Post a standalone tweet answering the most common reply question publicly (drives docs traffic). Day 5 (Friday) -- Reply to every substantive comment. Templates for common reactions:

• "How is this different from X?" -> Concrete comparison, link to docs
• "Benchmarks look suspicious" -> Link the reproduction steps, invite them to run it
• "Will you support [feature]?" -> Link the tracking issue

Day 6-7 (Weekend) -- Repurpose: extract the benchmark tweet as a standalone with a chart image; post a "5 things I learned launching an open-source DB" reflection thread.

Example 2: Building Thought Leadership from Scratch (Month 1)

Context: An individual ML engineer with 180 followers wants to become a recognized voice in applied machine learning. No existing audience. No viral content history.

Core principle for month 1: Do not broadcast. Contribute. Your first 500 followers come from being consistently useful in other people's threads, not from your own tweets.

Week 1 -- Comment-first growth:

• Post 0 original tweets
• Find 10 accounts in your niche with 5K-50K followers who post regularly
• Reply to 5-8 of their tweets per day with substantive comments (not "great post!")
• Goal: Get 3-5 of those authors to like or reply to your comments by end of week

What a good reply looks like:

Original tweet: "Fine-tuning LLMs is overrated. Most use cases
are better served by good prompting + RAG."

Bad reply: "Agreed!"

Good reply: "Mostly agree, but there's a middle ground --
LoRA fine-tuning on 500 domain-specific examples
consistently beats RAG for structured extraction tasks.

We saw 23% higher F1 on invoice parsing after a 2-hour
fine-tune vs our best RAG setup.

RAG still wins for open-domain QA though."

This reply adds data, shows experience, and invites further discussion. People reading the thread see your expertise and check your profile.

Week 2 -- First original content:

• Continue the reply strategy (5/day minimum)
• Post 2-3 original tweets. Keep them observational, not promotional:

Something I've noticed after fine-tuning 30+ models
this year:

The quality of your eval set matters 10x more than
the size of your training set.

50 carefully labeled examples with clear edge cases
beats 5000 noisy scraped examples every time.

• Post 1 "ask the audience" tweet to start conversations:

ML engineers: what's the most counterintuitive lesson
you've learned about deploying models to production?

I'll start: the model is almost never the bottleneck.
Data pipelines are.

Week 3 -- First thread (5-tweet authority thread):

1/5 [HOOK]: I've deployed 12 ML models to production this year. The ones
     that worked all had one thing in common. It wasn't the architecture.
2/5 [THESIS]: Every success had a tight feedback loop -- predictions
     validated by a human within 24 hours, not "we'll evaluate next quarter."
3/5 [EVIDENCE]: Model A (invoice classifier): accountants flagged errors
     same-day, retrained weekly, 84% -> 97% in 6 weeks. Model B (churn
     predictor): sales ignored outputs, no feedback 3 months, drifted to
     coin-flip accuracy.
4/5 [FRAMEWORK]: The pattern: (1) deploy with human-in-the-loop review,
     (2) log every correction, (3) retrain on corrections every 1-2 weeks,
     (4) remove human review once accuracy stabilizes.
5/5 [CTA]: If you're skipping the feedback loop, you're building on sand.
     What's your experience?

Notice the structure: personal credibility in tweet 1, a clear thesis in tweet 2, contrasting real examples in tweet 3, an actionable takeaway in tweet 4, and a discussion prompt in tweet 5.

Week 4 -- Establish rhythm:

• Settle into a sustainable cadence: 1 thread/week, 1-2 standalone tweets/day, 5+ replies/day
• Review metrics from week 2-3 content to identify which topics resonated
• Double down on the topic that got the most replies (not likes -- replies indicate deeper engagement)

Month 1 milestones:

What to avoid in month 1:

• Posting 10 tweets/day hoping something sticks -- looks desperate, exhausts your ideas
• Buying followers or using engagement pods -- Twitter's algorithm detects and penalizes this
• Talking about yourself or your product -- earn attention through insight first
• Getting discouraged by low numbers -- 180 to 400 followers in a month is strong growth

Performance Metrics

Key Metrics

Engagement Rate Formula

engagement_rate = (likes + retweets + replies + quotes) / impressions * 100

Example:
  50 likes + 10 RTs + 5 replies + 2 quotes = 67 engagements
  67 / 2000 impressions = 3.35% engagement rate

Content Performance Analysis

Track which content types and topics perform best:

| Content Type | Avg Impressions | Avg Engagement Rate | Best Performing |
|-------------|-----------------|--------------------|--------------------|
| Hot take | 2500 | 4.2% | "Unpopular opinion: ..." |
| Thread | 5000 | 3.1% | "I analyzed 500 ..." |
| Tip | 1800 | 5.5% | "How to ... in 3 steps" |

Use this data to optimize future content mix.

Brand Voice Guide

Voice Dimensions

Consistency Rules

• Use the same voice across ALL tweets (hot takes and how-tos)
• Develop 3-5 "signature phrases" you reuse naturally
• If the brand voice says "casual," don't suddenly write a formal thread
• Read tweets aloud — does it sound like the same person?

Safety & Compliance

Content Guidelines

NEVER post:

• Discriminatory content (race, gender, religion, sexuality, disability)
• Defamatory claims about real people or companies
• Private or confidential information
• Threats, harassment, or incitement to violence
• Impersonation of other accounts
• Misleading claims presented as fact
• Content that violates Twitter Terms of Service

Approval Mode Queue Format

[
  {
    "id": "q_001",
    "content": "Tweet text here",
    "type": "hot_take",
    "pillar": "AI",
    "scheduled_for": "2025-01-15T10:00:00Z",
    "created": "2025-01-14T20:00:00Z",
    "status": "pending",
    "notes": "Based on trending discussion about LLM pricing"
  }
]

Preview file for human review:

# Tweet Queue Preview
Generated: YYYY-MM-DD

## Pending Tweets (N total)

### 1. [Hot Take] — Scheduled: Mon 10 AM
> Tweet text here

**Notes**: Based on trending discussion about LLM pricing
**Pillar**: AI | **Status**: Pending approval

---

### 2. [Thread] — Scheduled: Tue 10 AM
> Tweet 1/5: Hook text here
> Tweet 2/5: Point one
> ...

**Notes**: Deep dive on new benchmark results
**Pillar**: AI | **Status**: Pending approval

Risk Assessment

Before posting, evaluate each tweet:

• Could this be misinterpreted? → Rephrase for clarity
• Does this punch down? → Don't post
• Would you be comfortable seeing this attributed to the user in a news article? → If no, don't post
• Is this verifiably true? → If not sure, add hedging language or don't post