Skills-curated x-research

install

source · Clone the upstream repo

git clone https://github.com/trailofbits/skills-curated

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/trailofbits/skills-curated "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/x-research/skills/x-research" ~/.claude/skills/trailofbits-skills-curated-x-research && rm -rf "$T"

manifest: plugins/x-research/skills/x-research/SKILL.md

source content

X Research

Agentic research over X/Twitter. Decompose research questions into targeted searches, iteratively refine, follow threads, deep-dive linked content, and synthesize sourced briefings.

For X API details (endpoints, operators, response format): read

{baseDir}/skills/x-research/references/x-api.md

Prerequisites

X API Bearer Token -- set
```
X_BEARER_TOKEN
```
(or
```
XAI_API_KEY
```
) env var
Python 3.11+ and uv (
```
pip install uv
```
or https://docs.astral.sh/uv/)

CLI Tool

All commands use

uv run

for automatic dependency management:

Search

uv run {baseDir}/skills/x-research/scripts/x_search.py 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)
```
--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, 1hr cache
```
--from-user <username>
```
-- shorthand for
```
from:username
```
in query
```
--quality
```
-- filter low-engagement tweets (min 10 likes, post-hoc)
```
--no-replies
```
-- exclude replies
```
--save
```
-- save results to
```
~/x-research-output/
```
```
--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:

uv run {baseDir}/skills/x-research/scripts/x_search.py search "claude code" --sort likes --limit 10
uv run {baseDir}/skills/x-research/scripts/x_search.py search "from:anthropic" --sort recent
uv run {baseDir}/skills/x-research/scripts/x_search.py search "(cursor OR windsurf) AI editor" --pages 2 --save
uv run {baseDir}/skills/x-research/scripts/x_search.py search "AI agents" --quick
uv run {baseDir}/skills/x-research/scripts/x_search.py search "AI agents" --quality --quick

Profile

uv run {baseDir}/skills/x-research/scripts/x_search.py profile <username> [--count N] [--replies] [--json]

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

Thread

uv run {baseDir}/skills/x-research/scripts/x_search.py thread <tweet_id> [--pages N]

Fetches full conversation thread by root tweet ID.

Single Tweet

uv run {baseDir}/skills/x-research/scripts/x_search.py tweet <tweet_id> [--json]

Watchlist

uv run {baseDir}/skills/x-research/scripts/x_search.py watchlist                        # Show all
uv run {baseDir}/skills/x-research/scripts/x_search.py watchlist add <user> [note]      # Add account
uv run {baseDir}/skills/x-research/scripts/x_search.py watchlist remove <user>           # Remove
uv run {baseDir}/skills/x-research/scripts/x_search.py watchlist check                   # Check recent

Watchlist stored in

{baseDir}/skills/x-research/data/watchlist.json

Cache

uv run {baseDir}/skills/x-research/scripts/x_search.py cache clear

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
Spam filter: Add
```
-airdrop -giveaway -whitelist
```
for crypto-adjacent topics

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
```
WebFetch
```
?

3. Follow Threads

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

uv run {baseDir}/skills/x-research/scripts/x_search.py thread <tweet_id>

4. Deep-Dive Linked Content

When tweets link to GitHub repos, blog posts, or docs, fetch with

WebFetch

. 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.

Refinement Heuristics

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

When to Use

Researching what developers/experts/community thinks about a topic
Getting real-time perspectives on breaking news or product launches
Finding technical discussions about libraries, frameworks, or APIs
Monitoring what key accounts are posting about
Gathering sourced evidence for competitive analysis or market research
Quick pulse check on a topic before deeper investigation

When NOT to Use

Posting tweets, replying, or managing an X account (read-only tool)
Historical research beyond 7 days (uses recent search endpoint only)
Searching non-X platforms (use web search tools instead)
Tasks where web search provides better results (X is best for real-time opinions, discussions, and breaking news -- not reference docs)

Cost Awareness

X API uses pay-per-use pricing ($0.005/post read, $0.01/user lookup). Quick mode keeps costs under ~$0.50/search. Always check the cost display after each search. Cache prevents duplicate charges. See

references/x-api.md

for full pricing.

File Structure

skills/x-research/
  SKILL.md           (this file)
  scripts/
    x_search.py      (CLI entry point, run with uv)
    x_api.py         (X API wrapper)
    x_cache.py       (file-based cache, 15min TTL)
    x_format.py      (terminal + markdown formatters)
  data/
    watchlist.json   (accounts to monitor)
    cache/           (auto-managed)
  references/
    x-api.md         (X API endpoint reference)