social-media-account-audit

social-media-account-audit is a skill for Claude Code, Codex from kevinnft/ai-agent-skills. It costs 33 tokens per session (2,186 once invoked), scanned A, original, MIT.

A workflow for auditing social-media accounts using profile data, engagement figures, and analytics screenshots. Engagement measures how actively an audience reacts to an account's content.

In plain words
What is it for?
Reviewing TikTok or Instagram profiles, extracting statistics, comparing past and current performance, diagnosing drops, and suggesting data-based improvements.
Why use it?
It helps identify why performance has changed and separates possible causes from guesses by calculating and interpreting account metrics.

Skill for Claude CodeCodex

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

Good fit Reviewing TikTok or Instagram profiles, extracting statistics, comparing past and current performance, diagnosing drops, and suggesting data-based improvements.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kevinnft/ai-agent-skills/social-media-account-audit
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.

Any agent
npx skills add kevinnft/ai-agent-skills --skill social-media-account-audit
Clone the repo
git clone --depth 1 https://github.com/kevinnft/ai-agent-skills

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 social-media-account-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/social-media-account-audit/github.svg)](https://agentmods.dev/skills/kevinnft/ai-agent-skills/social-media-account-audit)
Your own site
<a href="https://agentmods.dev/skills/kevinnft/ai-agent-skills/social-media-account-audit"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/social-media-account-audit/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.

agentmods 80×15 button for social-media-account-audit

Your own site · 80×15
<a href="https://agentmods.dev/skills/kevinnft/ai-agent-skills/social-media-account-audit"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/social-media-account-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,186 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00033 $0.02186
Opus 5 $0.00016 $0.01093
Sonnet 5 $0.00007 $0.00437
Haiku 4.5 $0.00003 $0.00219

Measured 8d ago against content hash e81252626431, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

social-media-account-audit scanned grade A with 1 finding 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 8d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s -L -A "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36" \
skills/social-media/social-media-account-audit/SKILL.md · 199 lines

How it starts

The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Social Media Account Audit

When to Use

  • User shares analytics screenshots (LIVE stats, dashboard data, engagement metrics)
  • User asks "why is my account/LIVE performing badly?"
  • User shares a TikTok/Instagram profile link for review
  • User wants to compare past vs current performance
  • User asks for growth strategy based on data

Workflow Overview

1. Scrape profile data (if link provided)
2. Extract metrics from screenshots (if provided)
3. Calculate health ratios
4. Diagnose root causes
5. Deliver actionable recommendations

Step 1: Scrape TikTok Profile Data

Fetch HTML

curl -s -L -A "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36" \
  "https://www.tiktok.com/@USERNAME" -o /tmp/tiktok_profile.html

Extract Basic Stats (grep approach — fast)

grep -oP '"uniqueId":"[^"]*"|"nickname":"[^"]*"|"signature":"[^"]*"|"followerCount":\d+|"followingCount":\d+|"heartCount":\d+|"videoCount":\d+|"verified":[a-z]+' /tmp/tiktok_profile.html

Extract Full Profile (Python — comprehensive)

import json, re
from datetime import datetime

with open('/tmp/tiktok_profile.html') as f:
    html = f.read()

match = re.search(
    r'<script id="__UNIVERSAL_DATA_FOR_REHYDRATION__"[^>]*>(.*?)</script>',
    html, re.DOTALL
)
data = json.loads(match.group(1))
scope = data['__DEFAULT_SCOPE__']
user_info = scope['webapp.user-detail']['userInfo']
user = user_info['user']
stats = user_info['stats']

# Key fields
print(f"Username: @{user['uniqueId']}")
print(f"Bio: {user['signature']}")
print(f"Created: {datetime.fromtimestamp(int(user['createTime']))}")
print(f"Category: {user.get('commerceUserInfo', {}).get('category', 'N/A')}")
print(f"Commerce User: {user.get('commerceUserInfo', {}).get('commerceUser', False)}")
print(f"TT Seller: {user.get('ttSeller', False)}")
print(f"Verified: {user['verified']}")
print(f"Followers: {stats['followerCount']:,}")
print(f"Following: {stats['followingCount']:,}")
print(f"Likes: {stats['heartCount']:,}")
print(f"Videos: {stats['videoCount']}")

Read the full file on GitHub · 199 lines

Files

What ships with it

2 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. 8d ago First seen · 199 lines · 33 tokens per session scan A e81252626431

Subscribe to this mod's changes

social-media-account-audit is a skill published in the GitHub repository kevinnft/ai-agent-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 2,186 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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