Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/godmakereth/vibe-coding-tw/twscrapenpx skills add godmakereth/vibe-coding-tw --skill twscrapegit clone --depth 1 https://github.com/godmakereth/vibe-coding-twWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.02689 |
| Opus 5 | $0.00000 | $0.01345 |
| Sonnet 5 | $0.00000 | $0.00538 |
| Haiku 4.5 | $0.00000 | $0.00269 |
Grade A, and why
twscrape scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to twscrape — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 439 lines — stays where its author put it; the contents beside it link to each section on GitHub.
twscrape
Python library for scraping Twitter/X data using GraphQL API with account rotation and session management.
When to use this skill
Use this skill when:
- Working with Twitter/X data extraction and scraping
- Need to bypass Twitter API limitations with account rotation
- Building social media monitoring or analytics tools
- Extracting tweets, user profiles, followers, trends from Twitter/X
- Need async/parallel scraping operations for large-scale data collection
- Looking for alternatives to official Twitter API
Quick Reference
Installation
pip install twscrape
Basic Setup
import asyncio
from twscrape import API, gather
async def main():
api = API() # Uses accounts.db by default
# Add accounts (with cookies - more stable)
cookies = "abc=12; ct0=xyz"
await api.pool.add_account("user1", "pass1", "[email protected]", "mail_pass", cookies=cookies)
# Or add accounts (with login/password - less stable)
await api.pool.add_account("user2", "pass2", "[email protected]", "mail_pass2")
await api.pool.login_all()
asyncio.run(main())
Common Operations
# Search tweets
await gather(api.search("elon musk", limit=20))
# Get user info
await api.user_by_login("xdevelopers")
user = await api.user_by_id(2244994945)
# Get user tweets
await gather(api.user_tweets(user_id, limit=20))
await gather(api.user_tweets_and_replies(user_id, limit=20))
await gather(api.user_media(user_id, limit=20))
# Get followers/following
await gather(api.followers(user_id, limit=20))
await gather(api.following(user_id, limit=20))
# Tweet operations
await api.tweet_details(tweet_id)
await gather(api.retweeters(tweet_id, limit=20))
await gather(api.tweet_replies(tweet_id, limit=20))
# Trends
await gather(api.trends("news"))
Key Features
1. Multiple API Support
- Search API: Standard Twitter search functionality
- GraphQL API: Advanced queries and data extraction
- Automatic switching: Based on rate limits and availability
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 439 lines · 0 tokens per session scan A bc5c29144772
twscrape is a skill published in the GitHub repository godmakereth/vibe-coding-tw (47 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,689 tokens. A static security scan graded it A with 0 findings. It is 97% identical to twscrape, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…