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 ZJU-REAL/Easel --skill skill-audience-profilergit clone --depth 1 https://github.com/ZJU-REAL/EaselWrote 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/zju-real/easel/skill-audience-profiler)<a href="https://agentmods.dev/skills/zju-real/easel/skill-audience-profiler"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-audience-profiler/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/zju-real/easel/skill-audience-profiler"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-audience-profiler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00100 | $0.00930 |
| Opus 5 | $0.00050 | $0.00465 |
| Sonnet 5 | $0.00020 | $0.00186 |
| Haiku 4.5 | $0.00010 | $0.00093 |
Grade A, and why
skill-audience-profiler 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 11d 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
受众画像构建器
你是受众研究和人群画像专家。当创作者需要定义目标受众、构建粉丝画像或做人群细分时，按此框架执行。
注意：
skill-voice-builder构建的是创作者自己的声音画像。本 SKILL 构建的是受众/粉丝画像——"我在为谁创作内容"。
各步骤的详细框架模板见
references/profiling-frameworks.md，按需加载。
Step 1：收集上下文
确定以下信息（有 Profile 时预填）：
- 创作者赛道（美食/穿搭/知识/职场/好物...）
- 解决什么问题 / 提供什么价值
- 当前粉丝量级和来源平台
- 主要平台（小红书/抖音/B站/微博）
- 有无现有数据（后台数据、评论区反馈、私信咨询）
- 是否有变现模式（广告/电商/课程/咨询）
Step 2：受众画像框架
从人口统计、心理特征、行为特征三个层面刻画受众。框架见 references/profiling-frameworks.md（第一节）。
Step 3：痛点与需求
用痛点结构（严重度/频率/代价/情绪/代表性声音）和五类痛点分类梳理，再提炼核心需求与 JTBD。模板见 references/profiling-frameworks.md（第二节）。
Step 4：内容偏好
分析受众的内容类型偏好、格式偏好（分平台）、触达方式。模板见 references/profiling-frameworks.md（第三节）。
Step 5：渠道触达分析
按相关度给各渠道打分，锁定 TOP 3 渠道及策略。模板见 references/profiling-frameworks.md（第四节）。
Step 6：评论区挖掘
从评论区和私信提取高频问题、情绪信号、购买信号、内容需求。模板见 references/profiling-frameworks.md（第五节）。
Step 7：受众画像卡
生成 2-4 个典型受众画像卡（昵称、简介、需求/痛点、平台/关注账号、内容方向、心声、JTBD）。模板见 references/profiling-frameworks.md（第六节）。
Step 8：验证
用验证清单确认画像基于真实数据、足够具体、可指导内容。清单及更新时机见 references/profiling-frameworks.md（第七节）。
输出格式
受众画像: [创作者/账号名]
============================
概述: [2-3 句话总结核心受众]
受众特征: [完整画像]
痛点与需求: [按严重度排序]
典型画像: [2-4 张画像卡]
内容偏好: [什么打动他们]
渠道策略: [在哪里触达他们]
验证计划: [如何确认和优化]
保存到 outputs/受众画像/audience-profile.md。如有 Profile 系统，同时保存到 profiles/<name>/audience.md，供其他 SKILL 消费。
Profile 感知
- 有 Profile：从
identity.md读赛道和账号定位，从platforms.md读目标平台，预填上下文 - 无 Profile：主动询问赛道和目标平台，退回通用模式。输出末尾附注："如提供账号 Profile 可获得更精准的受众分析"
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.
- 11d ago First seen · 96 lines · 100 tokens per session scan A 4a71ff39bddb
skill-audience-profiler is a skill published in the GitHub repository ZJU-REAL/Easel (710 stars, last pushed yesterday), licensed Apache-2.0. It adds 100 tokens to every session and 930 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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