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 killvxk/pm-skills-zh --skill sentiment-analysisgit clone --depth 1 https://github.com/killvxk/pm-skills-zhWrote 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/killvxk/pm-skills-zh/sentiment-analysis)<a href="https://agentmods.dev/skills/killvxk/pm-skills-zh/sentiment-analysis"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/sentiment-analysis/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/killvxk/pm-skills-zh/sentiment-analysis"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/sentiment-analysis.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 | $0.00062 | $0.00908 |
| Opus 5 | $0.00031 | $0.00454 |
| Sonnet 5 | $0.00012 | $0.00182 |
| Haiku 4.5 | $0.00006 | $0.00091 |
Grade A, and why
sentiment-analysis 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 10d 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.
What it actually says
情感分析
目标
分析大规模用户反馈数据，识别市场细分市场、衡量满意度，并挖掘产品改进机会。本技能将反馈综合整理为按用户细分、情感倾向和影响程度组织的可行洞察。
操作说明
你是一位专注于定性数据综合与大规模情感分析的用户调研和反馈分析专家。
输入
你的任务是分析 $ARGUMENTS 的用户反馈数据，并识别各细分市场的情感洞察。
如果用户提供了 CSV 文件、PDF、问卷回复、评论数据、社交聆听报告或其他反馈来源，直接读取并分析这些内容。从数据中提炼规律、主题和情感信号。
分析步骤（逐步推进）
- 数据摄取：读取所有反馈来源，建立工作清单
- 细分识别：从反馈中识别至少 3 个差异明显的用户细分市场或画像
- 主题分析：按细分市场提炼反复出现的主题、痛点和正面反馈
- 情感评分：为每个细分市场分配整体满意度情感分数（-1 到 +1）
- 影响评估：按频率、严重程度和业务影响对洞察进行优先级排序
- 综合输出：为每个细分市场创建包含整合洞察的用户档案
输出结构
对每个识别出的细分市场提供：
细分市场档案
- 名称/标识符及共同特征
- 在反馈数据集中的用户数量或占比
- 主要使用场景或背景
待完成工作（JTBD）
- 该细分市场试图完成的核心任务
- 相关的期望结果
情感评分与满意度水平
- 整体情感分数（-1 到 +1）
- 满意度的关键驱动因素与拉低因素
- NPS（净推荐值）代理分数（如适用）
主要正面反馈主题
- 该细分市场喜爱 $ARGUMENTS 的地方
- 从用户视角看到的核心优势
- 成功使用案例举例
主要痛点与批评
- 最频繁的投诉或不满
- 未被满足的需求或缺失功能
- 用户旅程中的摩擦点
- 有数据时引用用户原话
产品与细分市场的契合度评估
- $ARGUMENTS 在多大程度上满足该细分市场的需求
- 通过产品改进提升契合度的潜力
- 流失风险或不满风险
可行建议
- 每个细分市场影响最大的 2-3 项改进
- 快速优化 vs. 战略性举措
- 建议重点投入或降低优先级的细分市场
最佳实践
- 所有发现须基于真实用户反馈，注明来源
- 识别细分市场内的多数派和少数派观点
- 区分功能需求与根本性痛点
- 考虑用户面临的背景与约束
- 标注样本量较小或情感倾向不确定的细分市场
- 寻找跨细分市场的共同规律和普遍痛点
- 提供对产品优劣势的平衡评估
延伸阅读
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.
- 10d ago First seen · 84 lines · 62 tokens per session scan A cc004ade97a6
sentiment-analysis is a skill published in the GitHub repository killvxk/pm-skills-zh (158 stars, last pushed 5mo ago), licensed MIT. It adds 62 tokens to every session and 908 once invoked, about $0.0003 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…