content-pattern-intelligence

content-pattern-intelligence is a skill for Claude Code, Codex from seranking-planable/smm-skills. It costs 160 tokens per session (4,517 once invoked), scanned A, original, MIT.

A tool that studies which social media posts perform best by looking at engagement, views, and engagement rate. It finds patterns in topics, formats, openings, tone, and structure.

In plain words
What is it for?
Use it to review Planable data or a CSV export and decide what types of posts to create next.
Why use it?
It turns post statistics into specific ideas to test, instead of leaving you to guess why some content worked.

Skill for Claude CodeCodex

Part of the planable-smm plugin — 12 skills, 1 MCP server 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/seranking-planable/smm-skills/content-pattern-intelligence
Any agent
npx skills add seranking-planable/smm-skills --skill content-pattern-intelligence
Clone the repo
git clone --depth 1 https://github.com/seranking-planable/smm-skills

Made for: Claude Code, Codex.

Or install planable-smm, the plugin that ships this one along with the rest of its 12 skills, 1 MCP server.

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-pattern-intelligence

README.md
[![agentmods](https://agentmods.dev/badge/skills/seranking-planable/smm-skills/content-pattern-intelligence.svg)](https://agentmods.dev/skills/seranking-planable/smm-skills/content-pattern-intelligence)
Your own site
<a href="https://agentmods.dev/skills/seranking-planable/smm-skills/content-pattern-intelligence"><img src="https://agentmods.dev/badge/skills/seranking-planable/smm-skills/content-pattern-intelligence.svg" alt="Measured on agentmods" height="20"></a>
Per session 160 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,517 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 $0.00160 $0.04517
Opus 5 $0.00080 $0.02259
Sonnet 5 $0.00032 $0.00903
Haiku 4.5 $0.00016 $0.00452

Measured 5d ago against content hash 6f90c675c48c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

content-pattern-intelligence 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 5d 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.

skills/content-pattern-intelligence/SKILL.md · 451 lines

How it starts

The opening of the file, as written. The whole thing — 451 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content pattern intelligence

Look at what's actually working — then tell the user why, and what to try next.

This skill analyzes top posts across three performance lenses, finds patterns in the content itself (format, hook, topic, tone, structure), and translates them into specific, testable hypotheses for future posts.

This is not a performance report. It's a content brief grounded in data.


Step 0: Detect input mode

Mode A — Planable connector User has Planable connected and wants to pull live post data.

Mode B — CSV upload User has uploaded a CSV export with post-level data (text, date, platform, metrics).

How to detect:

  • File uploaded → Mode B
  • Mentions "CSV", "export", "file", "spreadsheet" → Mode B
  • Mentions workspace name, client, or Planable → Mode A
  • Unclear → ask: "Do you have a CSV with your post data, or should I pull it from Planable?"

Mode A: Planable connector

Step 1: Gather inputs

  • Workspace / client — which workspace to analyze
  • Platform(s) — default to all; narrow if requested
  • Date range — default to last 60–90 days (needs enough posts to find patterns; warn if fewer than 15 posts available)

Step 2: Fetch data — with Instagram-safe refresh logic

Instagram metrics sync is async and slow. Always follow this sequence to avoid timeouts.

Always call get_post_metrics with exactly one pageId per request. Multi-page calls compound timeouts — never bundle multiple pages into a single call.

Step 2a: Read first, check freshness
list_workspaces → find workspaceId
list_pages(workspaceId) → get pageIds
get_page_metrics(workspaceId, pageIds, startDate, endDate)

For each page in the response, check lastMetricAt (the timestamp of the last successful metrics fetch).

  • If lastMetricAt is within the last 3 hours — data is fresh. Skip the refresh entirely. Proceed to Step 2c.
  • If lastMetricAt is older than 3 hours or missing — data is stale. Proceed to Step 2b.

Read the full file on GitHub · 451 lines

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. 5d ago First seen · 451 lines · 160 tokens per session scan A 6f90c675c48c

Subscribe to this mod's changes

content-pattern-intelligence is a skill published in the GitHub repository seranking-planable/smm-skills (3 stars, last pushed 2mo ago), licensed MIT. It adds 160 tokens to every session and 4,517 once invoked, about $0.0008 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.

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