x-research

rohunvora/x-research-skill · updated Apr 8, 2026

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$npx skills add https://github.com/rohunvora/x-research-skill --skill x-research
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summary

General-purpose agentic research over X/Twitter. Decompose any research question into targeted searches, iteratively refine, follow threads, deep-dive linked content, and synthesize into a sourced briefing.

skill.md

X Research

General-purpose agentic research over X/Twitter. Decompose any research question into targeted searches, iteratively refine, follow threads, deep-dive linked content, and synthesize into a sourced briefing.

For X API details (endpoints, operators, response format): read references/x-api.md.

CLI Tool

All commands run from this skill directory:

cd ~/clawd/skills/x-research
source ~/.config/env/global.env

Search

bun run x-search.ts search "<query>" [options]

Options:

  • --sort likes|impressions|retweets|recent — sort order (default: likes)
  • --since 1h|3h|12h|1d|7d — time filter (default: last 7 days). Also accepts minutes (30m) or ISO timestamps.
  • --min-likes N — filter by minimum likes
  • --min-impressions N — filter by minimum impressions
  • --pages N — pages to fetch, 1-5 (default: 1, 100 tweets/page)
  • --limit N — max results to display (default: 15)
  • --quick — quick mode: 1 page, max 10 results, auto noise filter (-is:retweet -is:reply), 1hr cache, cost summary
  • --from <username> — shorthand for from:username in query
  • --quality — filter low-engagement tweets (≥10 likes, post-hoc)
  • --no-replies — exclude replies
  • --save — save results to ~/clawd/drafts/x-research-{slug}-{date}.md
  • --json — raw JSON output
  • --markdown — markdown output for research docs

Auto-adds -is:retweet unless query already includes it. All searches display estimated API cost.

Examples:

bun run x-search.ts search "BNKR" --sort likes --limit 10
bun run x-search.ts search "from:frankdegods" --sort recent
bun run x-search.ts search "(opus 4.6 OR claude) trading" --pages 2 --save
bun run x-search.ts search "$BNKR (revenue OR fees)" --min-likes 5
bun run x-search.ts search "BNKR" --quick
bun run x-search.ts search "BNKR" --from voidcider --quick
bun run x-search.ts search "AI agents" --quality --quick

Profile

bun run x-search.ts profile <username> [--count N] [--replies] [--json]

Fetches recent tweets from a specific user (excludes replies by default).

Thread

bun run x-search.ts thread <tweet_id> [--pages N]

Fetches full conversation thread by root tweet ID.

Single Tweet

bun run x-search.ts tweet <tweet_id> [--json]

Watchlist

bun run x-search.ts watchlist                       # Show all
bun run x-search.ts watchlist add <user> [note]     # Add account
bun run x-search.ts watchlist remove <user>          # Remove account
bun run x-search.ts watchlist check                  # Check recent from all

Watchlist stored in data/watchlist.json. Use for heartbeat integration — check if key accounts posted anything important.

Cache

bun run x-search.ts cache clear    # Clear all cached results

15-minute TTL. Avoids re-fetching identical queries.

Research Loop (Agentic)

When doing deep research (not just a quick search), follow this loop:

1. Decompose the Question into Queries

Turn the research question into 3-5 keyword queries using X search operators:

  • Core query: Direct keywords for the topic
  • Expert voices: from: specific known experts
  • Pain points: Keywords like (broken OR bug OR issue OR migration)
  • Positive signal: Keywords like (shipped OR love OR fast OR benchmark)
  • Links: url:github.com or url: specific domains
  • Noise reduction: -is:retweet (auto-added), add -is:reply if needed
  • Crypto spam: Add -airdrop -giveaway -whitelist if crypto topics flooding

2. Search and Extract

Run each query via CLI. After each, assess:

  • Signal or noise? Adjust operators.
  • Key voices worth searching from: specifically?
  • Threads worth following via thread command?
  • Linked resources worth deep-diving with web_fetch?

3. Follow Threads

When a tweet has high engagement or is a thread starter:

bun run x-search.ts thread <tweet_id>

4. Deep-Dive Linked Content

When tweets link to GitHub repos, blog posts, or docs, fetch with web_fetch. Prioritize links that:

  • Multiple tweets reference
  • Come from high-engagement tweets
  • Point to technical resources directly relevant to the question

5. Synthesize

Group findings by theme, not by query:

### [Theme/Finding Title]

[1-2 sentence summary]

- @username: "[key quote]" (NL, NI) [Tweet](url)
- @username2: "[another perspective]" (NL, NI) [Tweet](url)

Resources shared:
- [Resource title](url) — [what it is]

6. Save

Use --save flag or save manually to ~/clawd/drafts/x-research-{topic-slug}-{YYYY-MM-DD}.md.

Refinement Heuristics

  • Too much noise? Add -is:reply, use --sort likes, narrow keywords
  • Too few results? Broaden with OR, remove restrictive operators
  • Crypto spam? Add -$ -airdrop -giveaway -whitelist
  • Expert takes only? Use from: or --min-likes 50
  • Substance over hot takes? Search with has:links

Heartbeat Integration

On heartbeat, can run watchlist check to see if key accounts posted anything notable. Flag to Frank only if genuinely interesting/actionable — don't report routine tweets.

File Structure

skills/x-research/
├── SKILL.md           (this file)
├── x-search.ts        (CLI entry point)
├── lib/
│   ├── api.ts         (X API wrapper: search, thread, profile, tweet)
│   ├── cache.ts       (file-based cache, 15min TTL)
│   └── format.ts      (Telegram + markdown formatters)
├── data/
│   ├── watchlist.json  (accounts to monitor)
│   └── cache/          (auto-managed)
└── references/
    └── x-api.md        (X API endpoint reference)
how to use x-research

How to use x-research on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add x-research
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/rohunvora/x-research-skill --skill x-research

The skills CLI fetches x-research from GitHub repository rohunvora/x-research-skill and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/x-research

Reload or restart Cursor to activate x-research. Access the skill through slash commands (e.g., /x-research) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.741 reviews
  • Chaitanya Patil· Dec 28, 2024

    x-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sophia Malhotra· Nov 27, 2024

    We added x-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Piyush G· Nov 19, 2024

    Keeps context tight: x-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Soo Khan· Oct 18, 2024

    Solid pick for teams standardizing on skills: x-research is focused, and the summary matches what you get after install.

  • Shikha Mishra· Oct 10, 2024

    x-research has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Rahul Santra· Sep 17, 2024

    Solid pick for teams standardizing on skills: x-research is focused, and the summary matches what you get after install.

  • Sophia Gupta· Sep 13, 2024

    Registry listing for x-research matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Charlotte Khanna· Sep 5, 2024

    I recommend x-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Charlotte Li· Sep 1, 2024

    Keeps context tight: x-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Charlotte Wang· Aug 24, 2024

    x-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

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