content-performance

content-performance is a skill for Claude Code from sandbaseai/sandbase-skills. It costs 45 tokens per session (369 once invoked), scanned A, original, Apache-2.0.

A tool for comparing how content performs on YouTube, TikTok, Instagram, and Twitter. It examines engagement, formats, posting frequency, and audience responses.

In plain words
What is it for?
Use it to review videos and posts, compare content formats, study comments, and inform a cross-platform content plan.
Why use it?
It helps identify which types of posts and publishing patterns are associated with better audience reactions across these platforms.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the sandbase-skills plugin — 97 skills shipped together

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/sandbaseai/sandbase-skills/content-performance
Any agent
npx skills add sandbaseai/sandbase-skills --skill content-performance
Clone the repo
git clone --depth 1 https://github.com/sandbaseai/sandbase-skills

Made for: Claude Code.

Or install sandbase-skills, the plugin that ships this one along with the rest of its 97 skills.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/content-performance.svg)](https://agentmods.dev/skills/sandbaseai/sandbase-skills/content-performance)
Your own site
<a href="https://agentmods.dev/skills/sandbaseai/sandbase-skills/content-performance"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/content-performance.svg" alt="Measured on agentmods" height="20"></a>
Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 369 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 $0.00045 $0.00369
Opus 5 $0.00023 $0.00185
Sonnet 5 $0.00009 $0.00074
Haiku 4.5 $0.00005 $0.00037

Measured 6d ago against content hash 9f906e43de62, 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 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.

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.

research/content-performance/SKILL.md · 38 lines

What it actually says

Content Performance

Analyze content performance across YouTube, TikTok, Instagram, and Twitter to identify what works. Compares engagement rates, format effectiveness, posting cadence impact, and audience response patterns across platforms for content strategy optimization. Read the API map before selecting a capability.

Call SandBase capabilities

For every selected tool, call sandbase_describe_tool first and use only arguments in its current input schema. Then call sandbase_call_tool with the exact tool_name.

Available Tools

  • youtube_web_v2_video_info
  • youtube_web_v2_video_comments
  • tiktok_app_v3_one_video
  • instagram_v3_post_info
  • instagram_v3_post_comments
  • twitter_web_tweet_detail

Workflow

  1. Understand the user's research question, target, and context.
  2. Call sandbase_describe_tool for each selected tool to confirm parameter schema.
  3. Call sandbase_call_tool with the exact tool_name and schema-defined arguments.
  4. Synthesize findings into a clear, evidence-backed answer.
  5. Cite sources, note evidence gaps, and separate observations from interpretations.

Guidelines

  • Always call sandbase_describe_tool before using any capability.
  • Cite sources and preserve attribution (URLs, usernames, dates, metrics).
  • Separate factual observations from analysis and recommendations.
  • If data is unavailable, note the gap and continue with available evidence.
  • Read-only research only. Never take actions on platforms.
Files

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.

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 · 38 lines · 45 tokens per session scan A 9f906e43de62

Subscribe to this mod's changes

content-performance is a skill published in the GitHub repository sandbaseai/sandbase-skills (126 stars, last pushed 2d ago), licensed Apache-2.0. It adds 45 tokens to every session and 369 once invoked, about $0.0002 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.