Agent-Skills.md

Agent Skills: X Search

Search X/Twitter for real-time developer discourse, product feedback, community sentiment, and expert opinions. Use when user says "x search", "search x for", "search twitter for", "what are people saying about", or needs recent X discourse for context (library releases, API changes, product launches, industry discussion). Also use when researching a library, framework, API, or product to supplement web search with real-time community signal — e.g. "research Bun", "what do devs think of Hono", "is Turso production-ready".

UncategorizedID: iamladi/cautious-computing-machine--sdlc-plugin/x-search
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Install this agent skill to your local

pnpm dlx add-skill https://github.com/iamladi/cautious-computing-machine--sdlc-plugin/tree/HEAD/skills/x-search

Skill Files

Browse the full folder contents for x-search.

Download Skill

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skills/x-search/SKILL.md

Skill Metadata

Name
x-search
Description
Search X/Twitter for real-time developer discourse, product feedback, community sentiment, and expert opinions. Use when user says "x search", "search x for", "search twitter for", "what are people saying about", or needs recent X discourse for context (library releases, API changes, product launches, industry discussion). Also use when researching a library, framework, API, or product to supplement web search with real-time community signal — e.g. "research Bun", "what do devs think of Hono", "is Turso production-ready".

X Search

Priorities

Signal quality > Source attribution > API cost efficiency

Goal

Surface real-time perspectives, developer discussions, product feedback, and expert opinions from X/Twitter. The value you provide is turning raw social discourse into a sourced, structured briefing — separating signal from noise and attributing every claim to its source with engagement context.

Platform Constraints

These are hard technical limits that shape your approach:

  • Auth: Requires X_BEARER_TOKEN env var (Basic tier, $200/mo from https://developer.x.com).
  • Time window: Basic tier covers last 7 days only — you cannot search older tweets, so don't promise historical analysis.
  • Rate limits: 450 requests per 15-minute window. The CLI adds 350ms delay between calls, but be aware during multi-page research that you're consuming a shared budget.
  • Filtering: min_likes/min_retweets search operators are unavailable on Basic tier. The CLI filters post-hoc from public_metrics instead — this means you still fetch the full result set even when filtering aggressively.
  • Volume: Max 100 tweets per request, max 5 pages (500 tweets per search). For most research questions this is sufficient; if not, refine queries rather than paginating blindly.

CLI Tool

Locate the CLI entry point:

Glob(pattern: "**/sdlc/**/utils/x-search/x-search.ts", path: "~/.claude/plugins")

Run via Bash tool with the resolved path. The CLI has built-in --help for each subcommand. Key subcommands:

  • search "<query>" [options] — Search tweets. Useful options: --sort (likes/impressions/retweets/recent), --since (1h/3h/12h/1d/7d), --min-likes N, --pages N (1-5), --limit N, --no-replies, --save. Auto-adds -is:retweet unless your query already includes it.
  • profile <username> [--count N] [--replies] — Recent tweets from a user (excludes replies by default).
  • thread <tweet_id> [--pages N] — Full conversation thread from a root tweet.
  • tweet <tweet_id> — Fetch a single tweet with full metadata.
  • watchlist [add|remove|check] — Manage tracked accounts (stored in data/watchlist.json alongside CLI).
  • cache clear — Clear cached results (15-minute TTL).

Output defaults to markdown. Use --json for raw data, --save to write to CWD as x-research-{slug}-{date}.md.

Research Approach

For a quick single search, just run the query and present results. For deeper research questions, use an iterative approach:

Decompose the question into targeted searches. Think about what angles will surface useful signal: the core topic, known expert voices (from: operator), pain points vs positive sentiment, and linked resources (has:links, url:domain). Use X search operators to reduce noise — the reference docs below cover the full operator set.

Iterate based on what you find. After each search, assess: Is this signal or noise? Are there expert voices worth searching directly? High-engagement threads worth following? Linked resources worth fetching? Adjust your queries based on what the data tells you rather than running a fixed set.

Follow threads and linked content. Threads often contain the most substantive takes because they allow nuance. When tweets link to GitHub repos, blog posts, or docs, use web_fetch to get the full context — especially for links that multiple tweets reference or that come from high-engagement sources.

Synthesize by theme, not by query. Group your findings around what you learned, not how you searched. Each theme should include a brief summary, attributed quotes with engagement metrics (likes, impressions), and links to referenced resources.

Improving Search Quality

Use your judgment to adapt these strategies:

  • Too much noise? Exclude replies (-is:reply), sort by likes, use more specific keywords.
  • Too few results? Broaden with OR operators, remove restrictive filters, try alternative terminology.
  • Crypto/spam flooding? Add exclusions like -$ -airdrop -giveaway -whitelist.
  • Want substance over hot takes? Filter for tweets with links (has:links) or minimum engagement (--min-likes).
  • Want expert perspectives? Use from: for known voices in the space, or sort by engagement to surface authoritative tweets.

Match your search depth to the question. A "what do people think about X?" question might need one or two searches. A "comprehensive landscape of Y" might need five searches across different angles with thread follows and link deep-dives.

References

For X API endpoint details, search operators, and response structure:

  • Glob(pattern: "**/sdlc/**/skills/x-search/references/x-api.md", path: "~/.claude/plugins") → Read result

Arguments

$ARGUMENTS