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 anysiteio/agent-skills --skill anysite-content-analyticsgit clone --depth 1 https://github.com/anysiteio/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/anysiteio/agent-skills/anysite-content-analytics)<a href="https://agentmods.dev/skills/anysiteio/agent-skills/anysite-content-analytics"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-content-analytics/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/anysiteio/agent-skills/anysite-content-analytics"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-content-analytics.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 | $0.00078 | $0.02703 |
| Opus 5 | $0.00039 | $0.01352 |
| Sonnet 5 | $0.00016 | $0.00541 |
| Haiku 4.5 | $0.00008 | $0.00270 |
Grade A, and why
anysite-content-analytics 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 10d 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 — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.
anysite Content Analytics
Measure and optimize content performance across social platforms using anysite MCP. Track engagement, identify top performers, and refine your content strategy.
Overview
- Track post performance across Instagram, YouTube, LinkedIn, Twitter/X
- Analyze engagement metrics (likes, comments, shares, views)
- Identify top content and viral patterns
- Benchmark against competitors for strategy insights
- Optimize posting strategy based on data
Coverage: 80% - Strong for Instagram, YouTube, LinkedIn, Twitter, Reddit
Supported Platforms
- ✅ Instagram: Posts, Reels, likes, comments, engagement rates
- ✅ YouTube: Videos, views, likes, comments, watch time indicators
- ✅ LinkedIn: Posts, articles, reactions, comments, shares
- ✅ Twitter/X: Tweets, retweets, likes, replies
- ✅ Reddit: Posts, upvotes, comments, awards
v2 Tool Interface
All data fetching uses the anysite MCP v2 universal meta-tools:
execute(source, category, endpoint, params)- Fetch data from any source. Returns first page +cache_key.get_page(cache_key, offset, limit)- Load more items from a previous execute() whennext_offsetis returned.query_cache(cache_key, conditions?, sort_by?, aggregate?, group_by?)- Filter, sort, and aggregate cached data without new API calls.export_data(cache_key, format)- Export full dataset as CSV, JSON, or JSONL. Returns a download URL.
Error Handling
v2 responses may include llm_hint fields with guidance on how to resolve errors. Common patterns:
- 412: Entity not found - verify the identifier (username, URN, URL).
- 422: Invalid parameter format - check URN prefix format or param types.
- Always check
llm_hintin error responses for specific resolution steps.
Quick Start
Step 1: Collect Content Data
Platform-specific:
- Instagram:
execute("instagram", "user", "user_posts", {"user": "username", "count": 50}) - LinkedIn:
execute("linkedin", "user", "user_posts", {"urn": "fsd_profile:ACoAAA...", "count": 50}) - Twitter:
execute("twitter", "user", "user_posts", {"user": "username", "count": 100}) - YouTube:
execute("youtube", "channel", "channel_videos", {"channel": "channel_id", "count": 30})
What ships with it
1 file 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.
- 10d ago First seen · 367 lines · 78 tokens per session scan A 1a0ab0b35b2c
anysite-content-analytics is a skill published in the GitHub repository anysiteio/agent-skills (19 stars, last pushed 25d ago), licensed MIT. It adds 78 tokens to every session and 2,703 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 2026-08-30.
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