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 zeenie-ai/OpenCompany --skill tikhub-skillgit clone --depth 1 https://github.com/zeenie-ai/OpenCompanyWrote 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/zeenie-ai/opencompany/tikhub-skill)<a href="https://agentmods.dev/skills/zeenie-ai/opencompany/tikhub-skill"><img src="https://agentmods.dev/badge/skills/zeenie-ai/opencompany/tikhub-skill/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/zeenie-ai/opencompany/tikhub-skill"><img src="https://agentmods.dev/badge/skills/zeenie-ai/opencompany/tikhub-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 330 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00074 | $0.05154 |
| Opus 5 | $0.00037 | $0.02577 |
| Sonnet 5 | $0.00015 | $0.01031 |
| Haiku 4.5 | $0.00007 | $0.00515 |
Grade A, and why
tikhub-skill 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 5d 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 — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TikHub Skill
TikHub (api.tikhub.io) is a pay-per-request REST API that exposes roughly
1,000 scraping endpoints across TikTok, Douyin, Instagram, YouTube,
Twitter/X, Xiaohongshu, Bilibili, Kuaishou, Weibo, Reddit, Threads,
LinkedIn, Zhihu, Lemon8 and others. Each endpoint is one HTTP call that
returns the platform's raw data; there are no long-running jobs, no
actors, no datasets to poll. The tikhub_action tool is a thin, flattened
wrapper over the official tikhub Python SDK: every SDK method is
addressable by its resource-dot-method id (for example
douyin_web.fetch_one_video), and the tool passes your params straight
through as the SDK's keyword arguments.
Tool: tikhub_action
Operations
| Operation | Purpose | Key fields |
|---|---|---|
list_endpoints |
Discover endpoints (no API call, free) - returns each endpoint's id, HTTP method, path, summary and typed params with required flags |
platform (default all), search (substring over id / summary / path), limit |
call |
Invoke one endpoint by id | endpoint (resource-dot-method id or a /api/v1/... path), params (JSON object of the SDK keyword args) |
fetch_url |
Parse any TikTok / Douyin share URL through hybrid_parsing.video_data |
url, minimal (trimmed payload) |
account |
Balance, free credit and today's usage for the stored key | - |
Response
call and fetch_url:
{
"operation": "call",
"endpoint": "douyin_web.fetch_one_video",
"path": "/api/v1/douyin/web/fetch_one_video",
"data": { "aweme_detail": { "aweme_id": "7372484719365098803", "desc": "...", "statistics": { "digg_count": 12345 } } },
"code": 200,
"router": "/api/v1/douyin/web/fetch_one_video",
"cost_usd": 0.001
}
data is the platform payload exactly as TikHub returns it - the shape
differs per endpoint and per platform. code / router are TikHub's own
envelope fields. cost_usd is what this call was billed.
list_endpoints:
{
"operation": "list_endpoints",
"platform": "twitter",
"count": 13,
"total": 13,
"endpoints": [
{
"endpoint": "twitter_web.fetch_search_timeline",
"resource": "twitter_web",
"method": "fetch_search_timeline",
"http_method": "GET",
"path": "/api/v1/twitter/web/fetch_search_timeline",
"summary": "Search",
"params": [
{ "name": "keyword", "type": "str", "required": true },
{ "name": "search_type", "type": "str | None", "required": false },
{ "name": "cursor", "type": "str | None", "required": false }
]
}
]
}
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.
- 5d ago Changed · +18 lines eed17f810925
- 6d ago First seen · 356 lines · 74 tokens per session scan A 033905f0c0a2
tikhub-skill is a skill published in the GitHub repository zeenie-ai/OpenCompany (883 stars, last pushed today), licensed MIT. It adds 74 tokens to every session and 5,154 once invoked, about $0.0004 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 2026-09-06.
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