x-benchmark-research

x-benchmark-research is a skill for Claude Code, Codex from kangarooking/X-growth-skills. It costs 169 tokens per session (3,726 once invoked), scanned A, original, MIT.

A research method for studying successful social-media accounts and their popular posts to find ideas for a distinct content approach.

In plain words
What is it for?
Use it to find comparable accounts, examine recent popular posts, study audience interaction, understand how accounts make money, and develop a different angle.
Why use it?
It helps replace guesswork with patterns drawn from accounts that have already attracted attention.

Skill for Claude CodeCodex

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

Good fit Use it to find comparable accounts, examine recent popular posts, study audience interaction, understand how accounts make money, and develop a different angle.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kangarooking/x-growth-skills/x-benchmark-research
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 kangarooking/X-growth-skills --skill x-benchmark-research
Clone the repo
git clone --depth 1 https://github.com/kangarooking/X-growth-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 x-benchmark-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-benchmark-research/github.svg)](https://agentmods.dev/skills/kangarooking/x-growth-skills/x-benchmark-research)
Your own site
<a href="https://agentmods.dev/skills/kangarooking/x-growth-skills/x-benchmark-research"><img src="https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-benchmark-research/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 x-benchmark-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/kangarooking/x-growth-skills/x-benchmark-research"><img src="https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-benchmark-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 169 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,726 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.
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 $0.00169 $0.03726
Opus 5 $0.00084 $0.01863
Sonnet 5 $0.00034 $0.00745
Haiku 4.5 $0.00017 $0.00373

Measured 12d ago against content hash e4347e26dcb4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

x-benchmark-research 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.

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.

x-benchmark-research/SKILL.md · 167 lines

How it starts

The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.

对标账号研究 — 5步法+变形出差异

R — 原文 (Reading)

找对标不是抄内容,而是借别人验证过的流量逻辑。5步:1.找到3-5个对标账号(搜索from:xxx min_faves:10);2.分析近90天爆款;3.学互动策略(前30分钟积极互动);4.拆变现模式(bio+置顶);5.找差异化定位。变形三公式:场景细化、人群聚焦、观点反转。

— AIP出海教程 2.3 对标账号研究5步法 / 9.1 看对标:从爆款里挖"可复用的需求"


I — 方法论骨架 (Interpretation)

对标研究的核心不是"抄谁的内容",而是"借谁验证过的流量逻辑"——别人已用真实数据证明某类需求存在,你借这个验证结果,用自己的差异化重新填空。

5步层层递进:

  1. 找对标:选3-5个"低粉高爆"账号(粉丝不多但单条爆款多=流量逻辑可迁移,而非靠粉丝基数),用高级搜索 from:xxx min_faves:10 筛爆款,优先用户重合度80%+、近60天有3篇以上爆文的账号,避免"伪对标"
  2. 拆爆款:分析近90天爆款,抓隐性需求(标题高频痛点词、数据爆发的关键段落),而非只看表面内容
  3. 学互动:观察前30分钟积极互动+社群加热的冷启动模式
  4. 拆变现:从bio和置顶帖看出变现路径(广告/课程/咨询/订阅/带货)
  5. 变形出差异:用三公式在已验证需求上创新——场景细化、人群聚焦、观点反转

关键原则:"借别人验证过的流量逻辑,不抄内容"。前4步是手段,第5步(差异化)才是目的。


A1 — 书中的应用 (Past Application)

案例 1: @dotey(宝玉) — 深层干货赛道 0→15万粉拆解

  • 问题: 想在AI深层干货赛道找到增长路径和变现模式
  • 方法论的使用: AIP教程用5步法拆解@dotey的三阶段增长——找对标(深层干货赛道低粉高爆账号)→拆90天爆款(发现nano banana prompt是爆发点,抓到"可复用的prompt工程"隐性需求)→学互动(教程型+观点型组合,前30分钟社群加热)→拆变现(bio显示咨询/课程/赞助三层)→变形(在深层干货基础上加个人工程经验差异化,而非纯搬运prompt)
  • 结论: 0-1w靠教程型(提示词入门,转化率10%)→1-5w增加观点型+争议(模型能力论,转化率18%,推付费课程)→5-10w深层干货(nano banana prompt病毒教程10万+浏览,转化率25%,开发咨询+会员)
  • 结果: 15万粉,收入结构:咨询40%+课程30%+赞助30%

案例 2: @ahhhhfs — 搬运赛道 0→73万粉拆解

  • 问题: 想用搬运路线快速涨粉,但需要找到差异化避免同质化
  • 方法论的使用: AIP教程拆解——找对标(资源搬运类高收藏账号)→拆爆款(列表型结构化整理"10个AI工具"转发率10%,隐性需求=读者要的是"整理好的入口"不是"更多信息")→学互动(高收藏型+CTA订阅TG)→拆变现(广告+TG付费频道)→变形(从纯搬运升级为搬运+整合,加教程元素做差异化)
  • 结论: 0-1w列表型+免费实用→1-5w优化为高收藏型+教程→5-10w病毒帖55万浏览。从第一级(纯搬运)升级到第二级(搬运+整合资讯)
  • 结果: 73万粉(泛流量路线),收入结构:广告50%+订阅30%+赞助20%

案例 3: @dontbesilent12 — 赚钱赛道 0→5.7万粉拆解

  • 问题: 想在赚钱赛道找到差异化定位(赛道拥挤,如何不沦为营销号)
  • 方法论的使用: AIP教程拆解——找对标→拆90天爆款(故事型+痛点钩子,隐性需求=读者要"可执行的赚钱思路"不是"鸡汤")→学互动(引入争议观点激发辩论,回复率20%)→拆变现(免费小贴士→咨询→课程逐步升级)→变形(从纯故事型引入观点型+教程元素混合,做"有态度的活人"而非千篇一律的营销号)
  • 结论: 0-1w故事型+高频(每周5-7帖,转化率10-15%)→1-5w引入争议观点(回复率20%,推付费咨询)→5-10w混合型+教程(转化率20%+,开发千元级课程)
  • 结果: 5.7万粉,收入结构:课程40%+咨询30%+广告/合作30%

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

  1. 用户刚开始做X或重新定位,不知道找谁对标学习
  2. 用户已有赛道方向(如AI工具),问"怎么找差异化""别人已经做了我怎么不同"
  3. 用户想系统分析某个竞品账号的爆款逻辑和变现模式
  4. 用户发了一段时间内容效果不好,想找对标看看自己缺什么
  5. 用户想从已验证的爆款需求出发,生成有差异化的原创选题

Read the full file on GitHub · 167 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. 12d ago First seen · 167 lines · 169 tokens per session scan A e4347e26dcb4

Subscribe to this mod's changes

x-benchmark-research is a skill published in the GitHub repository kangarooking/X-growth-skills (62 stars, last pushed 2mo ago), licensed MIT. It adds 169 tokens to every session and 3,726 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 2026-08-30.

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