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 YangsonHung/awesome-agent-skills --skill tweetclaw-twitter-automationgit clone --depth 1 https://github.com/YangsonHung/awesome-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/yangsonhung/awesome-agent-skills/tweetclaw-twitter-automation)<a href="https://agentmods.dev/skills/yangsonhung/awesome-agent-skills/tweetclaw-twitter-automation"><img src="https://agentmods.dev/badge/skills/yangsonhung/awesome-agent-skills/tweetclaw-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/yangsonhung/awesome-agent-skills/tweetclaw-twitter-automation"><img src="https://agentmods.dev/badge/skills/yangsonhung/awesome-agent-skills/tweetclaw-twitter-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00055 | $0.00837 |
| Opus 5.5 | $0.00022 | $0.00335 |
| Sonnet 5.5 | $0.00011 | $0.00167 |
| Haiku 4.5 | $0.00006 | $0.00084 |
Grade A, and why
tweetclaw-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 1mo 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TweetClaw Twitter Automation
Use TweetClaw when a user asks an agent to work with X/Twitter through Xquik or the TweetClaw OpenClaw plugin.
When to Use
Use this skill when the user asks for:
- Scraping tweets, threads, replies, quotes, mentions, likes, or media
- Searching tweets or tweet replies
- Looking up users, followers, following, or verified followers
- Posting tweets, replying to tweets, or uploading media after approval
- Reading or sending direct messages after account authorization
- Creating monitors, webhooks, or giveaway draws
- Checking which TweetClaw or Xquik endpoint can perform a task
Do not use
Do not use this skill for:
- Spam, harassment, deceptive engagement, impersonation, or platform evasion
- Bulk unsolicited DMs, mass follows, mass likes, mass retweets, or engagement farming
- X ads, analytics dashboards, or scheduling features that TweetClaw does not provide
- Writing actions when only read-only access is configured
- Accessing private or account-scoped data without clear user authorization
- Asking users to paste API keys, signing keys, cookies, or tokens into chat
Instructions
- Restate the user job as one workflow: read, extraction, write, media, DM, monitor, webhook, draw, or discovery.
- Check current docs before install, configuration, limits, or API details matter:
- For OpenClaw installs, prefer the explicit npm selector:
openclaw plugins install npm:@xquik/tweetclaw
- After install or update, verify the runtime before live work:
openclaw plugins inspect tweetclaw --runtime --json
openclaw skills info tweetclaw
- Keep credentials in the Xquik dashboard, OpenClaw plugin config, or environment-backed secret storage. Never print or echo credential values.
- Use TweetClaw discovery or Xquik docs to choose the smallest endpoint and request limit that satisfies the user.
- Before any visible, state-changing, private, paid, recurring, extraction, monitor, webhook, draw, or account-scoped action, summarize the target, account, action, limits, data handling, and usage impact if available. Wait for explicit confirmation.
- For posting or replying, show the final text and media list before sending. Do not add links, mentions, hashtags, or claims the user did not request.
- For monitors and webhooks, state the target, event types, delivery behavior, and how the user can stop the resource.
- Return concise results with IDs, URLs, counts, and any partial failures.
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.
- 1mo ago First seen · 75 lines · 55 tokens per session scan A 3f696ecec152
tweetclaw-twitter-automation is a skill published in the GitHub repository YangsonHung/awesome-agent-skills (18 stars, last pushed 2mo ago), licensed MIT. It adds 55 tokens to every session and 837 once invoked, about $0.0002 per session on Opus 5.5. 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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