x-research

name

x-research

description

General-purpose X/Twitter research agent. Searches X for real-time perspectives, dev discussions, product feedback, cultural takes, breaking news, and expert opinions. Works like a web research agent but uses X as the source. Use when: (1) user says "x research", "search x for", "search twitter for", "what are people saying about", "what's twitter saying", "check x for", "x search", "/x-research", (2) user is working on something where recent X discourse would provide useful context (new library releases, API changes, product launches, cultural events, industry drama), (3) user wants to find what devs/experts/community thinks about a topic. NOT for: posting tweets, account management, or historical archive searches beyond 7 days.

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)