content-performance-audit

name	content-performance-audit
description	Statistically audit what actually drives a creator's views. Use when a SocialGPT user asks "what drives my views", "why do some of my posts pop", "what should I double down on", or wants a data-backed performance review of their own content. Pulls the user's videos via the SocialGPT MCP, then runs real significance tests (Pearson, Kruskal-Wallis) to rank the factors — length, timing, platform, format — that predict view count, and writes a visual report. Requires the SocialGPT MCP server to be connected.
license	MIT

Content Performance Audit

Turn a creator's own post history into a ranked, statistically-tested answer to "what actually drives my views?" — instead of guessing, you measure.

This skill pairs with the SocialGPT MCP server (https://mcp.gpt.social/mcp). The MCP provides the data; this skill provides the deterministic analysis and a shareable report. If the SocialGPT tools below aren't available, the user needs to connect the MCP first: https://gpt.social/integrations/mcp

When to use

Trigger on requests like: "what drives my views?", "what's working on my account?", "what should I post more of?", "do a performance audit", "why did some videos pop and others flop?"

Workflow

1. Confirm access. Make sure the SocialGPT MCP tools are connected. If list_videos isn't available, point the user to the connect page above and stop.

2. Pull the data. Call the MCP tool:

   list_videos(limit=50, sort="recent", include_analysis=false)
   

   To audit a single connected account, first call list_accounts() and pass its account_id. Save the raw JSON the tool returns to a file named videos.json in the working directory (the whole {"videos": [...]} envelope is fine — the script handles it).

3. Run the analysis (it has no third-party dependencies):

   python scripts/analyze.py videos.json
   

   The script reads videos.json, runs the tests, prints a Markdown summary to stdout, and writes content-performance-report.html.

4. Relay the result. Present the Markdown summary the script printed, then offer the user the generated content-performance-report.html to download / open (on Claude.ai it appears as a downloadable file).

What the script does

For every video with a usable view count (Instagram posts showing 0 plays are treated as missing, since IG hides reel plays), it tests each factor against view count and ranks them by effect size and p-value:

• Continuous factors (video length, engagement rate) → Pearson correlation on log-views, with a real two-sided p-value.
• Categorical factors (platform, day of week, time of day, sequel vs. standalone) → Kruskal-Wallis rank test, robust to the heavy-tailed view distribution.

It flags factors at p<0.05 (significant) and p<0.01 (highly significant), then translates the top significant factors into plain-English actions. See references/methodology.md for the statistical details and caveats.

Notes

• Honest about samples. With fewer than ~6 usable videos it will tell you so rather than invent findings. More posts → more statistical power; 30+ is ideal.
• Correlation ≠ causation. Significant factors are the best hypotheses to test next, not guarantees. The report says this explicitly.
• Re-runnable. Suggest the user re-run monthly; drivers shift as the account grows.
• Part of a larger loop. This is the "find your drivers" stage of the going-viral strategy loop — run it there to turn the drivers it surfaces into your next experiment.