content-growth-review

content-growth-review is a skill for Claude Code, Codex from chengkj99/kj-skills. It costs 103 tokens per session (1,397 once invoked), scanned A, original, MIT.

A review workflow for updated data from creator platforms such as Douyin, WeChat Channels, WeChat Official Accounts, and Xiaohongshu. It turns performance data and comments into findings about what is helping or limiting audience growth.

In plain words
What is it for?
Use it to review creator-media results, diagnose content and conversion problems, create topics from comments, and update content planning lists.
Why use it?
It helps explain weak follower growth or unusual engagement instead of relying on guesses. It also turns findings into adjusted content direction and planned topics.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present. Also seen: $skill-name invocation.

Part of the kj-skills plugin — 34 skills, 1 command, 1 hook shipped together

Good fit Use it to review creator-media results, diagnose content and conversion problems, create topics from comments, and update content planning lists.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chengkj99/kj-skills/content-growth-review
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 chengkj99/kj-skills --skill content-growth-review
Clone the repo
git clone --depth 1 https://github.com/chengkj99/kj-skills

Made for: Claude Code, Codex.

Or install kj-skills, the plugin that ships this one along with the rest of its 34 skills, 1 command, 1 hook.

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-growth-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/chengkj99/kj-skills/content-growth-review/github.svg)](https://agentmods.dev/skills/chengkj99/kj-skills/content-growth-review)
Your own site
<a href="https://agentmods.dev/skills/chengkj99/kj-skills/content-growth-review"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/content-growth-review/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 content-growth-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/chengkj99/kj-skills/content-growth-review"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/content-growth-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 103 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,397 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00103 $0.01397
Opus 5 $0.00051 $0.00698
Sonnet 5 $0.00021 $0.00279
Haiku 4.5 $0.00010 $0.00140

Measured 12d ago against content hash 8e0bc6b344c1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

content-growth-review 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 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/collect_growth_inputs.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-growth-review/SKILL.md · 172 lines

How it starts

The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content Growth Review

Use this skill after media data has been updated. It is the analysis layer, not the crawler.

  • Use $kangjian-douyin-media or another platform collector first to refresh raw data.
  • Use this skill to diagnose growth problems, decide content direction, and produce topic plans.
  • Default workspace is the LLM Wiki repo.
  • Default review output path: raw/studio/funnel/reviews/.
  • Default topic output path: raw/studio/funnel/backlog/manual/.

Core Workflow

  1. Collect the latest inputs with scripts/collect_growth_inputs.py.
  2. Read the generated input bundle and any referenced source reports.
  3. Diagnose data problems by separating:
    • content-market fit
    • topic clarity
    • account positioning
    • hook/opening strength
    • format and series continuity
    • comment-to-follow conversion
    • product/course/community conversion
  4. Produce a review report.
  5. Convert high-value findings into topic suggestions.
  6. Write selected topic pools to raw/studio/funnel/backlog/manual/ unless the user only wants chat advice.

Quick Start

python3 <skill_dir>/scripts/collect_growth_inputs.py \
  --wiki-root "<wiki-root>" \
  --platform douyin \
  --output "/tmp/content-growth-review-inputs.md"

Then read /tmp/content-growth-review-inputs.md and the linked source files.

Required Reads

Always check the relevant files if they exist:

  • raw/studio/funnel/published/<platform>/latest-*.md
  • latest raw/studio/funnel/published/<platform>/*review-analysis.md
  • latest raw/studio/funnel/published/<platform>/*full-ledger.md only when comment examples or exact work details are needed
  • raw/studio/funnel/backlog/manual/
  • raw/studio/funnel/backlog/queue.md
  • wiki/playbooks/topic-bank/series-planning.md

For Douyin, also check:

  • raw/studio/funnel/published/douyin/manifest/*.json if present
  • raw/studio/funnel/published/douyin/runs/ when comparing snapshots

Review Report Contract

Write a dated Markdown report when doing a real review:

Read the full file on GitHub · 172 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. 12d ago First seen · 172 lines · 103 tokens per session scan A 8e0bc6b344c1

Subscribe to this mod's changes

content-growth-review is a skill published in the GitHub repository chengkj99/kj-skills (14 stars, last pushed 11d ago), licensed MIT. It adds 103 tokens to every session and 1,397 once invoked, about $0.0005 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.

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