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 endearqb/endearqb-skills --skill endearqb-community-profilergit clone --depth 1 https://github.com/endearqb/endearqb-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/endearqb/endearqb-skills/endearqb-community-profiler)<a href="https://agentmods.dev/skills/endearqb/endearqb-skills/endearqb-community-profiler"><img src="https://agentmods.dev/badge/skills/endearqb/endearqb-skills/endearqb-community-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/endearqb/endearqb-skills/endearqb-community-profiler"><img src="https://agentmods.dev/badge/skills/endearqb/endearqb-skills/endearqb-community-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 | $0.00226 | $0.03989 |
| Opus 5 | $0.00113 | $0.01995 |
| Sonnet 5 | $0.00045 | $0.00798 |
| Haiku 4.5 | $0.00023 | $0.00399 |
Grade A, and why
community-profiler scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 419 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Community Profiler v4.1.0 — 技术社区成员画像与评估
概述
将群聊记录解析为结构化成员画像，支持三种使用场景：
- 运营决策：KOL 筛选、低价值成员风险评分
- 个人研究：社区生态与知识流动分析
- 团队协作：成员贡献与角色识别
输出架构（JSON + Viewer 分离）
本技能采用 数据与视图分离 架构：
输出文件
report.json— AI 分析结果的唯一结构化输出（必须）viewer.html— 从assets/viewer.html复制到输出目录的独立可视化查看器（自动）report.md— 文字分析报告（仅用户明确要求时）assets/— 抓取的链接/图片内容（有外部资源时）
输出流程（严格按此顺序）
Step A：分析群聊 → 生成 report.json → 保存到输出目录
Step B：从技能 assets/ 复制 viewer.html 到输出目录（与 report.json 同级）
Step C：使用 present_files 将 viewer.html（第一个）和 report.json 呈现给用户
关键命令序列
# 1. 创建输出目录
OUTPUT_DIR="/mnt/user-data/outputs/groupchat/$(date +%Y-%m-%d_%H%M%S)"
mkdir -p "$OUTPUT_DIR"
# 2. 保存 report.json（由 AI 分析生成，用 create_file 写入）
# → $OUTPUT_DIR/report.json
# 3. 复制 viewer.html 到输出目录
cp /mnt/skills/user/community-profiler/assets/viewer.html "$OUTPUT_DIR/viewer.html"
# 4. 通过 present_files 呈现给用户
# present_files(["$OUTPUT_DIR/viewer.html", "$OUTPUT_DIR/report.json"])
⚠️ 重要：viewer.html 和 report.json 必须在同一目录下。viewer.html 通过
fetch('./report.json')自动加载数据。
所有文件保存至 groupchat/YYYY-MM-DD_HHMMSS/（按分析时间戳新建）。
Step 0：规模判断与预处理策略
首先判断消息量级，选择对应工作流：
| 规模 | 消息数 | 策略 |
|---|---|---|
| 小型 | < 200 条 | 全量分析，一次性输出 |
| 中型 | 200-1000 条 | 分块分析（每块200条），subagent 资产抓取与主分析并行 |
| 大型 | > 1000 条 | 分块分析 + 向用户报告进度，HTML 启用虚拟滚动 |
0.1 广告/垃圾信息过滤
标记为 spam，不计入评分：
- 营销话术（购买/优惠/引流）
- 同一人 3 条内容相似度 > 80%
- 连续 5 条以上纯表情包
- 短链 + 邀请码格式
0.2 Bot 识别排除
排除昵称含 Bot/助手/机器人/通知/公告 或发言模式高度规律的账号，单独列出。
0.3 无时间戳处理
无时间戳时：时序子项跳过，权重自动切换：
- 有时间戳：活跃度20% · 内容质量30% · 互动影响25% · 专业权威15% · 社区粘性10%
- 无时间戳：活跃度15% · 内容质量35% · 互动影响30% · 专业权威20% · 社区粘性0%
Step 1：解析输入
| 格式 | 处理 |
|---|---|
| 微信/TG/Slack/Discord 导出文本 | 解析时间戳、昵称、消息 |
| 截图 | OCR 后按文本处理 |
| JSON / CSV | 直接读取字段映射 |
| 多文件/分段粘贴 | 合并去重 |
字段缺失时：说明缺失内容，继续最佳估计，不中止。
Step 2：外部资源抓取（Subagent 并行）
与主分析流程并行执行，不阻塞成员评分。
去重与优先级
- 按 URL/图片 hash 去重，只抓取一次，累计
share_count - 优先：技术文章/文档/仓库/被 ≥3 人提及的链接
- 低优先：社交主页、电商页
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 · 419 lines · 226 tokens per session scan A 0c0af20ddab9
community-profiler is a skill published in the GitHub repository endearqb/endearqb-skills (19 stars, last pushed 8d ago), licensed MIT. It adds 226 tokens to every session and 3,989 once invoked, about $0.0011 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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