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 librefang/librefang-registry --skill xquik-social-datagit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/xquik-social-data)<a href="https://agentmods.dev/skills/librefang/librefang-registry/xquik-social-data"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/xquik-social-data.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.00989 |
| Opus 5 | $0.00018 | $0.00495 |
| Sonnet 5 | $0.00007 | $0.00198 |
| Haiku 4.5 | $0.00004 | $0.00099 |
Grade A, and why
xquik-social-data 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 4d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Xquik Social Data
Use this skill when a user needs to collect, normalize, monitor, or automate X and Twitter data with Xquik. Xquik provides a public REST API, generated SDKs, an HTTP MCP server, webhooks, and an installable agent skill for common social data workflows.
Prefer REST for product code, scripts, backend jobs, and dashboards. Prefer MCP when an agent should inspect endpoint metadata, choose calls, or operate inside an IDE or chat tool.
When to Use
- Search tweets, inspect tweet details, or collect account timelines
- Fetch user profile data, followers, following, mentions, media, or engagement data
- Run bulk extraction jobs for replies, quotes, posts, media, lists, communities, or people search
- Set up monitors and webhook delivery for new social events
- Use Xquik from an AI agent through the public MCP server or installable skill
- Draft, schedule, or confirm write actions only when the user explicitly asks
Required Inputs
XQUIK_API_KEYfor API, SDK, or MCP calls- The target endpoint, task type, username, tweet URL, tweet ID, query, or extraction type
- The desired output shape, such as JSON, CSV, summary, dashboard table, or webhook payload
- User confirmation before private reads, write actions, monitor creation, webhook delivery, or bulk jobs
Workflow
-
Read the public Xquik docs before selecting endpoints. Start with the API reference for REST routes, the OpenAPI schema for request fields, and the MCP guide for agent setup.
-
Use the installable skill when the agent supports Skills:
npx [email protected] add Xquik-dev/x-twitter-scraper -
For JavaScript or TypeScript helpers, pin the validated package version:
npm install [email protected] -
Keep credentials in environment variables or the host secret store. Never paste API keys into prompts, logs, source files, PRs, or issue text.
-
Choose the narrowest endpoint or extraction type that satisfies the task. Do not fetch extra pages, private data, or write-capable resources without user approval.
-
Preserve pagination metadata such as
next_cursorandhas_more. For long jobs, estimate first, start the job, then poll the documented job endpoint until it finishes or fails. -
Normalize outputs before analysis. Keep raw IDs, source URL, collected-at time, query parameters, and pagination state so results can be audited later.
-
For monitors and webhooks, confirm the target account or keyword, event types, destination URL, and ongoing behavior before creating resources.
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.
- 4d ago First seen · 87 lines · 37 tokens per session scan A 39edf3a2b95f
xquik-social-data is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 14d ago), licensed MIT. It adds 37 tokens to every session and 989 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
xquik-social-data
Prepare and normalize Xquik X data for MCP clients. Build request plans for tweet search, tweet lookup, and trends, or normalize supplied Xquik JSON into compact records. Trigger when the user asks for Xquik, X data, Twitter data, tweet search, trends, or social data ingest.
hermes-tweet
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hermes-tweet
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xquik
Xquik X data automation API - Use REST or MCP for tweet search, user lookup, follower exports, media downloads, monitors, webhooks, giveaway draws, and confirmation-gated X actions.
twitter-agent
Build and run a Twitter/X agent with a distinct personality and automated workflows.