Twitter Hand Skill logo

Twitter Hand Skill

OrganizationPopular
RightNow-AI
twitter-hand-skill

Expert knowledge for AI Twitter/X management — API v2 reference, content strategy, engagement playbook, safety, and performance tracking

Overview

PublisherRightNow-AI
Repositoryopenfang
Skill nametwitter-hand-skill
Stars
18.2K
Forks
2.3K
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by RightNow-AI on GitHub. Read the source before you install it.

Installation

Install the Twitter Hand Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/RightNow-AI/openfang.git /tmp/openfang
mkdir -p .claude/skills
cp -r /tmp/openfang/crates/openfang-hands/bundled/twitter .claude/skills/twitter-hand-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Twitter Hand Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Twitter Hand Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Twitter Hand Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Core Endpoints

Get authenticated user info:

bash
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:

bash
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:

bash
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:

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

Like a tweet:

bash
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:

bash
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:

bash
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:

bash
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

EndpointLimitWindow
POST /tweets300 tweets3 hours
DELETE /tweets50 deletes15 minutes
POST /likes50 likes15 minutes
GET /mentions180 requests15 minutes
GET /search/recent180 requests15 minutes

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

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)

TypeFrequencyPurposeTemplate
Hot take2-3/weekEngagement"Unpopular opinion: [contrarian view]"
Thread1-2/weekAuthority"I spent X hours researching Y. Here's what I found:"
Tip/How-to2-3/weekValue"How to [solve problem] in [N] steps:"
Question1-2/weekEngagement"[Interesting question]? I'll go first:"
Curated share1-2/weekCuration"This [article/tool/repo] is a game changer for [audience]:"
Story1/weekConnection"3 years ago I [relatable experience]. Here's what happened:"
Data/Stat1/weekAuthority"[Surprising statistic]. Here's why it matters:"

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

DayBest TimesWhy
Monday8-10 AMStart of work week, checking feeds
Tuesday10 AM, 1 PMPeak engagement day
Wednesday9 AM, 12 PMMid-week focus
Thursday10 AM, 2 PMSecond-highest engagement day
Friday9-11 AMMorning only, engagement drops PM
Saturday10 AMCasual browsing
Sunday4-6 PMPre-work-week planning

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)

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

Performance Metrics

Key Metrics

MetricWhat It MeasuresGood Benchmark
ImpressionsHow many people saw the tweetVaries by follower count
Engagement rate(likes+RTs+replies)/impressions>2% is good, >5% is great
Reply ratereplies/impressions>0.5% is good
Retweet rateRTs/impressions>1% is good
Profile visitsPeople checking your profile after tweetTrack trend
Follower growthNet new followers per periodTrack trend

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

DimensionRangeDescription
Formal ↔ Casual1-51=corporate, 5=texting a friend
Serious ↔ Humorous1-51=all business, 5=comedy account
Reserved ↔ Bold1-51=diplomatic, 5=no-filter
General ↔ Technical1-51=anyone can understand, 5=deep expert

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

json
[
  {
    "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:

markdown
# 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Twitter Hand Skill AI skill do?

Expert knowledge for AI Twitter/X management — API v2 reference, content strategy, engagement playbook, safety, and performance tracking

Why use Twitter Hand Skill on TypingMind?

Because you install it once and use it with any model. Twitter Hand Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Twitter Hand Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RightNow-AI/openfang/tree/main/crates/openfang-hands/bundled/twitter. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Twitter Hand Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Twitter Hand Skill?

As many as you like. As long as a model supports skills, you can use Twitter Hand Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Twitter Hand Skill AI skill free?

Yes. It is published on GitHub by RightNow-AI under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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