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 skills add cosmicstack-labs/mercury-agent-skills --skill x-twitter-automationgit clone --depth 1 https://github.com/cosmicstack-labs/mercury-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cosmicstack-labs/mercury-agent-skills/x-twitter-automation)<a href="https://agentmods.dev/skills/cosmicstack-labs/mercury-agent-skills/x-twitter-automation"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/x-twitter-automation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cosmicstack-labs/mercury-agent-skills/x-twitter-automation"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/x-twitter-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00029 | $0.02493 |
| Opus 5 | $0.00015 | $0.01247 |
| Sonnet 5 | $0.00006 | $0.00499 |
| Haiku 4.5 | $0.00003 | $0.00249 |
Grade A, and why
x-twitter-automation 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 331 lines — stays where its author put it; the contents beside it link to each section on GitHub.
X/Twitter Automation
Use this skill when an agent needs to search tweets, read tweet replies, monitor X/Twitter topics, draft social posts, or run controlled X actions through a tool-backed workflow.
Recommended implementations:
- TweetClaw for OpenClaw agents that
need an installable
@xquik/tweetclawplugin for tweet search, reply reads, follower export, media workflows, monitors, webhooks, DMs, giveaway draws, and approval-gated posting through Xquik. - Hermes Tweet for Hermes Agent workflows that use the Hermes plugin surface for X/Twitter search, account reads, trends, reply reads, monitoring, and action-gated posting through Xquik.
When to Use
Use this skill when the user asks for:
- Search tweets about a company, launch, product, market, or community.
- Search Twitter or search X for recent conversations.
- Read tweet replies and summarize sentiment.
- Monitor tweets around a keyword, username, hashtag, product, or event.
- Look up users, accounts, trends, or public social activity.
- Draft tweets, post tweets, post replies, or send DMs after explicit approval.
- Turn social signals into alerts, reports, CRM notes, or content ideas.
- Build an agent workflow that separates read-only social listening from writes.
Do not use this skill for:
- Bypassing platform rules, rate limits, consent, or account safety controls.
- Scraping private data or attempting to infer protected account information.
- Posting or messaging without clear human authorization.
- Coordinated spam, engagement farming, impersonation, or harassment.
Core Model
Treat X/Twitter automation as 3 separate layers:
- Discovery: search tweets, find conversations, look up users, inspect trends.
- Analysis: classify, summarize, deduplicate, score, and extract next actions.
- Action: post tweets, post replies, send DMs, follow, like, or update monitors.
Keep Discovery and Analysis read-only by default. Only enable Action when the user explicitly requests a write-capable workflow and understands the effect.
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.
- 11d ago First seen · 331 lines · 29 tokens per session scan A 0848d55ec75a
x-twitter-automation is a skill published in the GitHub repository cosmicstack-labs/mercury-agent-skills (470 stars, last pushed 17d ago), licensed MIT. It adds 29 tokens to every session and 2,493 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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