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X Research

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X/Twitter public sentiment research. Searches X for real-time perspectives, market sentiment, expert opinions, breaking news, and community discourse. Use when: user asks "what are people saying about", "X/Twitter sentiment", "check X for", "search twitter for", "what's CT saying about", or wants public opinion on a stock, sector, company, or market event.

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  • Added October 14, 2026
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A100/100

Scanned October 14, 2026

npx -y skills add raditrejp/dexter-kabu-jp --skill x-research --agent claude-code

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SKILL.md
---
name: x-research
description: >
  X/Twitter public sentiment research. Searches X for real-time perspectives,
  market sentiment, expert opinions, breaking news, and community discourse.
  Use when: user asks "what are people saying about", "X/Twitter sentiment",
  "check X for", "search twitter for", "what's CT saying about", or wants
  public opinion on a stock, sector, company, or market event.
---

# X Research Skill

Agentic research over X/Twitter using the `x_search` tool. Decompose the
research question into targeted searches, iterate to refine signal, and
synthesize into a sourced sentiment briefing.

## Research Loop

### 1. Decompose into Queries

Turn the research question into 3–5 targeted queries using X operators:

- **Core query**: Direct keywords or `$TICKER` cashtag
- **Expert voices**: `from:username` for known analysts or accounts
- **Bearish signal**: keywords like `(overvalued OR bubble OR risk OR concern)`
- **Bullish signal**: keywords like `(bullish OR upside OR catalyst OR beat)`
- **News/links**: add `has:links` to surface tweets with sources
- **Noise reduction**: `-is:reply` to focus on original posts; `-airdrop -giveaway` for crypto topics

### 2. Execute Searches

Use the `x_search` tool with `command: "search"`. For each query:

- Start with `sort: "likes"` and `limit: 15` to surface highest-signal tweets
- Add `min_likes: 5` or higher to filter noise for broad topics
- Use `since: "1d"` or `"7d"` depending on how time-sensitive the topic is
- If a query returns too much noise, narrow with more operators or raise `min_likes`
- If too few results, broaden with `OR` terms or remove restrictive operators

### 3. Check Key Accounts (Optional)

For well-known analysts, fund managers, or company executives, use
`command: "profile"` to see their recent posts directly.

### 4. Follow Threads (Optional)

When a high-engagement tweet appears to be a thread starter, use
`command: "thread"` with the tweet ID to get full context.

### 5. Synthesize

Group findings by theme (bullish, bearish, neutral, news/catalysts):

```
### [Theme]

[1–2 sentence summary of the theme]

- @username: "[key quote]" — [likes]♥ [Tweet](url)
- @username2: "[another perspective]" — [likes]♥ [Tweet](url)
```

End with an **Overall Sentiment** paragraph: predominant tone (bullish/bearish/
mixed/neutral), confidence level, and any notable divergence between retail and
institutional voices.

## Refinement Heuristics

| Problem | Fix |
|---|---|
| Too much noise | Raise `min_likes`, add `-is:reply`, narrow keywords |
| Too few results | Broaden with `OR`, remove restrictive operators |
| Crypto spam | Add `-airdrop -giveaway -whitelist` |
| Want expert takes only | Use `from:` or `min_likes: 50` |
| Want substance over hot takes | Add `has:links` |

## Output Format

Present a structured briefing:

1. **Query Summary**: what was searched and time window
2. **Sentiment Themes**: grouped findings with sourced quotes and tweet links
3. **Overall Sentiment**: tone, confidence, key voices
4. **Caveats**: X sentiment is not a reliable predictor; sample bias toward vocal minorities; last-7-days window only

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