Analyze Cross-Platform Content Performance with a New Agent Skill

Where this fits

You're a content strategist who just posted the same video to YouTube, TikTok, and Instagram. Engagement differs wildly — one platform rewards longer storytelling, another wants quick cuts. Manually pulling each platform's metrics, normalizing them, and spotting patterns eats a Friday afternoon. The content-performance skill automates that comparative analysis, giving your agent the tools to fetch post data, comments, and engagement stats from four major platforms so you can see what actually works.

Why agents benefit

• Unified data retrieval – One skill exposes a consistent interface to fetch video info, comments, and post details across YouTube, TikTok, Instagram, and Twitter, removing the need to learn four different APIs.
• Cross-platform comparison – Agents can pull the same metric (e.g., engagement rate or comment sentiment) for a piece of content published on multiple platforms and compute normalized comparisons.
• Format and cadence analysis – By correlating posting frequency and content type (short video vs. thread vs. carousel) with engagement, the agent can surface actionable patterns.
• Audience response insights – Comment retrieval enables sentiment or topic extraction, helping you understand why a piece resonated, not just the raw numbers.
• Reusable workflow – The skill's structured steps (describe tool → call tool → aggregate) turn a messy research task into a repeatable, auditable pipeline that fits into larger agent workflows.

Practical scenarios

Identifying the best format for a product demo

Your agent collects engagement rates for a 30-second demo clip published as a TikTok, a YouTube Short, and an Instagram Reel. The skill's tools return views, likes, comments, and shares. The agent then compares ratios and flags that TikTok has 3x the comment-to-view rate, suggesting the platform's audience engages more deeply with that format.

Optimizing posting cadence for a creator account

You feed the agent a month of post timestamps and engagement data across Twitter and Instagram. The skill's comparison logic correlates posting time and frequency with engagement dips, revealing that Instagram posts on Tuesdays outperform Thursdays, while Twitter sees no pattern. The agent can then suggest a scheduling change.

Auditing a competitor's cross-platform strategy

Using video info and comment tools, your agent pulls a competitor's top five posts per platform, extracts comment themes, and measures engagement per follower. It identifies that the competitor's YouTube long-form gets high watch time but low interaction, while their Twitter threads drive shares. You get a strategy matrix without manual spreadsheet work.

Add it to your agent workflow

Install the skill via npx:

npx skills add sandbaseai/sandbase-skills --skill content-performance

Then in your agent, you'll first read the API map and call sandbase_describe_tool before using a tool. A minimal flow:

# After loading the skill, set up a tool descriptor
tool_info = await sandbase_describe_tool("youtube_web_v2_video_info")
# Use the exact schema from tool_info to call:
result = await sandbase_call_tool(tool_name="youtube_web_v2_video_info", args={"video_id": "abc123"})

Repeat for each platform's tool, then write your comparison logic.

Compare before adopting

Before replacing your current analytics stack, compare the skill against:

• Quality signals – Does it return raw API data or processed metrics? Check the API map for rate limits and data freshness.
• Maintenance freshness – Social platforms change APIs often; verify the skill's last update and issue tracker.
• Alternatives – Dedicated analytics APIs (e.g., official YouTube Data API) may offer more depth but require separate auth for each platform.
• Workflow fit – If you already have a data pipeline, this skill might be redundant. For quick comparative research, it shines.

Why it is worth tracking

With 59 stars and an active sandbaseai author, this skill signals a practical approach to a common pain point. The fact it wraps multiple platform endpoints behind a uniform interface makes it a valuable building block for content intelligence agents. Even if you don't adopt it today, monitoring its evolution (especially how it handles API changes) will inform your own tool design. For a full list of capabilities and updates, visit the skill listing on OpenAgentSkill.