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.
npx skills add kangarooking/X-growth-skills --skill x-comment-engagementgit clone --depth 1 https://github.com/kangarooking/X-growth-skillsWrote 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.
[](https://agentmods.dev/skills/kangarooking/x-growth-skills/x-comment-engagement)<a href="https://agentmods.dev/skills/kangarooking/x-growth-skills/x-comment-engagement"><img src="https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-comment-engagement/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.
<a href="https://agentmods.dev/skills/kangarooking/x-growth-skills/x-comment-engagement"><img src="https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-comment-engagement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00164 | $0.03735 |
| Opus 5 | $0.00082 | $0.01868 |
| Sonnet 5 | $0.00033 | $0.00747 |
| Haiku 4.5 | $0.00016 | $0.00374 |
Grade A, and why
x-comment-engagement 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.
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
评论互动策略 + Repost vs Quote
R — 原文 (Reading)
增加评论互动的有效方法：有价值提问、A/B观点引战、坎宁汉姆定律（抛错误答案，利用人们喜欢纠正他人的心理引发评论）、真诚请教、生活日常、找骂策略。使用频率不宜过高，以免损害账号专业性。高质量回复能吃到原帖流量——原帖48k曝光，评论也有1.1k曝光。Repost是"我推荐你看"，Quote是"我基于这个补充观点"。新人多引用。
— Yangyi @Yangyixxxx, X爆款秘籍分享 · 评论互动策略; 木马人 @cnyzgkc, X起号避坑指南 · 三/五
I — 方法论骨架 (Interpretation)
在 X 上，评论和回复不是"附属于原帖的边角料"——它们是独立的曝光入口。一条高质量评论可以吃到原帖的流量分成（原帖 48k 曝光，评论分到 1.1k），且回复的曝光算在你自己头上。
六种评论互动法，按机制分三类：
- 价值型——有价值提问（开放性问题聚拢观点）、真诚请教（学习者姿态获取回复）。建形象 + 涨互动。
- 争议型——A/B 观点引战（抛争议立场激辩论）、坎宁汉姆定律（抛错误事实引纠正）、找骂策略（发傻观点引批评）。短期涨互动快，但频次高损专业形象。
- 低门槛型——生活日常（"今天早餐吃什么"），降评论门槛，适合新号或互动低迷时。
Repost vs Quote 的本质区分：Repost = 替别人做筛选（"这个值得看"），不增加你的辨识度；Quote = 展示你的思考（"我基于这个补充观点"），原作者会看到并可能互动。新人优先 Quote + 增量观点，而非只 Repost。
核心判据：每条评论/Quote 必须有增量价值——重述观点 + 补充经验，或补充一个新角度。只有"太强了""好牛逼"的评论和 Quote 等于寄生噪声，会被当蹭流量。
A1 — 书中的应用 (Past Application)
案例 1: 木马人高质量评论吃 1.1k 曝光
- 问题: 新号发帖没人看，怎么在别人流量好的帖子下获取曝光？
- 方法论的使用: 在一条 48k 曝光的原帖下写高质量回复（非"哈哈""牛x"，而是有增量价值的实质内容）。回复的曝光算在自己头上——原帖 48k 流量中分到 1.1k 曝光给自己的评论。
- 结论: 高质量回复是"借别人的流量池给自己开窗口"。针对流量好的帖子，前期优质回复能吃到一波不错的流量。
- 结果: 木马人评论获得 1.1k 曝光，相当于自己发一条帖子的曝光量，但成本远低于原创。
案例 2: 坎宁汉姆定律"AI教父陈一舟"案例
- 问题: 怎么用最低成本引发大量评论互动？
- 方法论的使用: Yangyi 抛出一个明显错误的事实——"AI教父陈一舟xxxxx"。利用人们喜欢纠正他人错误的心理，评论区大量回复"那是李一舟"（陈一舟与李一舟名字相似但身份完全不同），互动量迅速攀升。
- 结论: 坎宁汉姆定律效果显著——抛错误事实比抛争议观点更容易引发纠正型互动，因为"纠正错误"的心理冲动比"辩论观点"更强。
- 结果: 该案例被 Yangyi 作为坎宁汉姆定律的标杆示例。但 Yangyi 明确警告：使用频率不宜过高，否则受众识破套路，专业形象受损。
案例 3: 新人 Quote vs Repost 的选择
- 问题: 新号看到一条好帖，该 Repost 还是 Quote？
- 方法论的使用: 木马人区分——Repost 呈现形式还是原帖，语义="我推荐你看这个"；Quote 在原帖上加自己的观点，语义="我基于这个补充观点"。新人想让别人记住你，应优先 Quote：一来原作者看到你的观点觉得好会互动，二来给原帖增曝光。
- 结论: Repost 只替别人筛选，不增加辨识度；Quote 展示你的思考，建立个人品牌。
- 结果: 该建议被作为起号避坑指南的核心建议之一，与冷启动策略1（大V评论区抢占）和策略5（Quote+深度观点）同级使用。
A2 — 触发场景 (Future Trigger) ★
用户会在什么情境下需要这个 skill?
- 想在大V帖下评论获取曝光: 新号或中小号想借大V帖子的流量池给自己开窗口，但不知道怎么写评论才不被当蹭流量。
- 纠结 Repost 还是 Quote: 看到一条好帖，不确定该直接转发还是引用加观点，想判断哪个对自己更有利。
- 想引发互动但不想损形象: 想用争议型策略（引战/坎宁汉姆/找骂）涨互动，但担心损害专业形象或踩平台红线。
- 推文评论区冷清: 自己发的帖没人评论，想用互动策略激活评论区。
- Quote 写了但被嫌蹭流量: 用户 Quote 了大V帖但只加了"太强了"之类的无价值评论，被反感或举报。
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.
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.
- 12d ago First seen · 160 lines · 164 tokens per session scan A 26e22c8662c2
x-comment-engagement is a skill published in the GitHub repository kangarooking/X-growth-skills (62 stars, last pushed 1mo ago), licensed MIT. It adds 164 tokens to every session and 3,735 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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