x-research

Q: How do I install x-research?

Run npx skills add https://github.com/bradautomates/head-of-content --skill x-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/x-research/ (personal) or .claude/skills/x-research/ (project).

by bradautomatesProductivity & Automation

Research high-performing X/Twitter content from tracked accounts using Apify's Tweet Scraper V2. Identifies outlier tweets, trending topics, and content patterns to inform content strategy. Use when asked to: - Find trending tweets or content in a niche - Research what's performing on X/Twitter - Identify high-performing tweet patterns - Analyze competitors' X content - Generate content ideas from X trends - Run X/Twitter research Triggers: "x research", "twitter research", "find trending tweets", "analyze x accounts", "what's working on twitter", "content research x", "tweet analysis"

Installs: 246
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/bradautomates/head-of-content --skill x-research

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads x-research automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

X/Twitter Research

Research high-performing tweets from tracked accounts, identify outliers, and optionally analyze video content for hooks and structure.

Prerequisites

APIFY_TOKEN environment variable or in .env
GEMINI_API_KEY environment variable or in .env (for video analysis)
apify-client and google-genai Python packages
Accounts configured in .claude/context/x-accounts.md

Verify setup:

python3 -c "
import os
try:
    from dotenv import load_dotenv
    load_dotenv()
except ImportError:
    pass
from apify_client import ApifyClient
assert os.environ.get('APIFY_TOKEN'), 'APIFY_TOKEN not set'
" && echo "Prerequisites OK"

Workflow

1. Create Run Folder

RUN_FOLDER="x-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"

2. Fetch Tweets

python3 .claude/skills/x-research/scripts/fetch_tweets.py \
  --days 30 \
  --max-items 100 \
  --output {RUN_FOLDER}/raw.json

Parameters:

--days: Days back to search (default: 30)
--max-items: Max tweets per account (default: 100)
--handles: Override accounts file with specific handles

API Limits: Minimum 50 tweets per query required. Wait a couple minutes between runs.

3. Identify Outliers

python3 .claude/skills/x-research/scripts/analyze_posts.py \
  --input {RUN_FOLDER}/raw.json \
  --output {RUN_FOLDER}/outliers.json \
  --threshold 2.0

Output JSON contains:

total_posts: Number of tweets analyzed
outlier_count: Number of outliers found
topics: Top hashtags, mentions, and keywords
content_patterns: Analysis of what formats perform well
accounts: List of accounts analyzed
outliers: Array of outlier tweets with engagement metrics

4. Analyze Videos with AI (Optional)

If outliers contain video content:

python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py \
  --input {RUN_FOLDER}/outliers.json \
  --output {RUN_FOLDER}/video-analysis.json \
  --platform x \
  --max-videos 5

Note: X/Twitter is primarily text-based. Video analysis is optional and only useful when outliers contain video posts.

5. Generate Report

Read {RUN_FOLDER}/outliers.json (and optionally {RUN_FOLDER}/video-analysis.json), then generate {RUN_FOLDER}/report.md.

Report Structure:

# X/Twitter Research Report

Generated: {date}

## Summary

- **Total tweets analyzed**: {total_posts}
- **Outlier tweets identified**: {outlier_count}
- **Outlier rate**: {percentage}%

## Top Performing Tweets (Outliers)

### 1. @{username} ({name})

> {tweet_text}

- **URL**: {url}
- **Date**: {created_at}
- **Engagement**: {likes} likes | {retweets} RTs | {replies} replies | {bookmarks} bookmarks
- **Engagement Score**: {score}
- **Engagement Rate**: {rate}%
- **Followers**: {followers}

[Repeat for top 15 outliers]

## Top Performing Hooks (if video analysis available)

### Hook 1: {technique} - @{username}
- **Opening**: "{opening_line}"
- **Why it works**: {attention_grab}
- **Replicable Formula**: {replicable_formula}
- [Watch Video]({url})

## Trending Topics

### Top Hashtags
[From outliers.json topics.hashtags]

### Top Keywords
[From outliers.json topics.keywords]

### Top Mentions
[From outliers.json topics.mentions]

## Content Patterns in Outliers

| Pattern | Count | Percentage |
|---------|-------|------------|
| Contains media | {count} | {pct}% |
| Contains external link | {count} | {pct}% |
| Thread format | {count} | {pct}% |
| Quote tweet | {count} | {pct}% |
| Asks a question | {count} | {pct}% |
| List/numbered format | {count} | {pct}% |
| Short (<100 chars) | {count} | {pct}% |
| Medium (100-200 chars) | {count} | {pct}% |
| Long (>200 chars) | {count} | {pct}% |

## Actionable Takeaways

[Synthesize patterns into 4-6 specific recommendations]

## Accounts Analyzed

[List accounts]

Focus on actionable insights. Content patterns and trending topics are key for X/Twitter research.

Quick Reference

Full pipeline:

RUN_FOLDER="x-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && \
python3 .claude/skills/x-research/scripts/fetch_tweets.py -o "$RUN_FOLDER/raw.json" && \
python3 .claude/skills/x-research/scripts/analyze_posts.py -i "$RUN_FOLDER/raw.json" -o "$RUN_FOLDER/outliers.json"

With video analysis (optional):

python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py -i "$RUN_FOLDER/outliers.json" -o "$RUN_FOLDER/video-analysis.json" -p x

Then read JSON files and generate the report.

Engagement Metrics

Engagement Score (weighted):

Bookmarks: 4x (highest signal - saved for reference)
Replies: 3x (active conversation)
Retweets: 2x (amplification)
Quotes: 2x (engagement with commentary)
Likes: 1x (passive approval)

Outlier Detection: Tweets with engagement rate > mean + (threshold x std_dev)

Engagement Rate: (score / followers) x 100

Output Location

All output goes to timestamped run folders:

x-research/
└── {YYYY-MM-DD_HHMMSS}/
    ├── raw.json            # Raw tweet data from Apify
    ├── outliers.json       # Outliers with metadata and topics
    ├── video-analysis.json # AI video analysis (optional)
    └── report.md           # Final report

FAQ

What does the x-research skill do?

How do I install x-research?

Run npx skills add https://github.com/bradautomates/head-of-content --skill x-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/x-research/ (personal) or .claude/skills/x-research/ (project).

Which AI agents can use x-research?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is x-research safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains x-research?

It is published by bradautomates in bradautomates/head-of-content. It has 246 recorded installs.

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x-research

Install

Install

Describe the task

Review the result

SKILL.md

X/Twitter Research

Prerequisites

Workflow

1. Create Run Folder

2. Fetch Tweets

3. Identify Outliers

4. Analyze Videos with AI (Optional)

5. Generate Report

Quick Reference

Engagement Metrics

Output Location

FAQ

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