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content-performance-audit

content-performance-audit is a skill for Claude Code, Codex from scrollmark/socialgpt-mcp. It costs 118 tokens per session (888 once invoked), scanned A, original, MIT.

A data-based review of a creator’s own videos to identify which factors are linked to more views. It uses statistical testing rather than relying only on impressions or intuition.

In plain words
What is it for?
Use it to audit video performance, compare posts that succeeded or failed, find topics or formats to repeat, and decide what content to make more often.
Why use it?
It helps answer what is actually working on an account and separates repeatable patterns from lucky one-off results. The report is based on the creator’s post history when SocialGPT access is connected.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 10 1mo ago A scan 118 tokens original MIT
View the source file scrollmark/socialgpt-mcp gpt.social
Install

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.

agentmods
npx agentmods add skills/scrollmark/socialgpt-mcp/content-performance-audit
Any agent
npx skills add scrollmark/socialgpt-mcp --skill content-performance-audit
Clone the repo
git clone --depth 1 https://github.com/scrollmark/socialgpt-mcp

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for content-performance-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/scrollmark/socialgpt-mcp/content-performance-audit.svg)](https://agentmods.dev/skills/scrollmark/socialgpt-mcp/content-performance-audit)
Your own site
<a href="https://agentmods.dev/skills/scrollmark/socialgpt-mcp/content-performance-audit"><img src="https://agentmods.dev/badge/skills/scrollmark/socialgpt-mcp/content-performance-audit.svg" alt="Measured on agentmods" height="20"></a>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 888 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00118 $0.00888
Opus 5 $0.00059 $0.00444
Sonnet 5 $0.00024 $0.00178
Haiku 4.5 $0.00012 $0.00089

Measured 6d ago against content hash 5eb53bd2ed47, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

content-performance-audit 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 6d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/analyze.py, scripts/sgpt_lib.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/content-performance-audit/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 83 lines

Files

What ships with it

3 files 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.

Changes

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.

  1. 6d ago First seen · 83 lines · 118 tokens per session scan A 5eb53bd2ed47

Subscribe to this mod's changes

content-performance-audit is a skill published in the GitHub repository scrollmark/socialgpt-mcp (10 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 888 once invoked, about $0.0006 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 2026-08-31.

Analytics and metricsMarketing and SEOai-agentschatgptclaudecreator-toolscursor
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