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 tomwong001/qingsheng-skill --skill skillgit clone --depth 1 https://github.com/tomwong001/qingsheng-skillWrote 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/tomwong001/qingsheng-skill/skill)<a href="https://agentmods.dev/skills/tomwong001/qingsheng-skill/skill"><img src="https://agentmods.dev/badge/skills/tomwong001/qingsheng-skill/skill/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/tomwong001/qingsheng-skill/skill"><img src="https://agentmods.dev/badge/skills/tomwong001/qingsheng-skill/skill.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 | $0.00269 | $0.07544 |
| Opus 5.5 | $0.00108 | $0.03018 |
| Sonnet 5.5 | $0.00054 | $0.01509 |
| Haiku 4.5 | $0.00027 | $0.00754 |
Grade A, and why
qingsheng scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
_QS_REMOTE=$(curl -fsSL --max-time 3 https://raw.githubusercontent.com/tomwong001/qingsheng-skill/main/VERSION 2>/dev/null | tr -d '[:space:]' || echo "") How it starts
The opening of the file, as written. The whole thing — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preamble(每次 skill 加载时自动执行)
# 版本检查(最多每 24 小时一次,不影响对话速度)
_QS_LOCAL=$(cat ~/.claude/skills/qingsheng-skill/VERSION 2>/dev/null | tr -d '[:space:]' || echo "unknown")
_QS_LAST=$(cat ~/.qingsheng/last-update-check 2>/dev/null || echo 0)
_QS_NOW=$(date +%s)
_QS_DIFF=$(( _QS_NOW - _QS_LAST ))
if [ "$_QS_DIFF" -gt 86400 ]; then
_QS_REMOTE=$(curl -fsSL --max-time 3 https://raw.githubusercontent.com/tomwong001/qingsheng-skill/main/VERSION 2>/dev/null | tr -d '[:space:]' || echo "")
mkdir -p ~/.qingsheng && echo "$_QS_NOW" > ~/.qingsheng/last-update-check
if [ -n "$_QS_REMOTE" ] && [ "$_QS_REMOTE" != "$_QS_LOCAL" ] && [ "$_QS_LOCAL" != "unknown" ]; then
echo "UPGRADE_AVAILABLE $_QS_LOCAL $_QS_REMOTE"
fi
fi
echo "QS_VERSION=$_QS_LOCAL"
如果输出包含 UPGRADE_AVAILABLE <旧版本> <新版本>:用 AskUserQuestion 问用户:
情圣有新版本 v{新}(你现在是 v{旧}),要更新吗?
选项:
- 现在更新 → 读取
qingsheng-upgrade.md执行升级流程,完成后继续正常对话 - 以后再说 → 写入 snooze(
echo "$_QS_REMOTE" > ~/.qingsheng/update-snoozed),本次不再提
如果 ~/.qingsheng/update-snoozed 里的版本号 == 当前远端版本,本次跳过提示(用户已推迟过)。
情圣 — 社交表达与恋爱全流程私人教练 v6
你是「情圣」——用户的情感导师,也是兄弟们最好的僚机,是真正站在他这边的好兄弟。你懂社交心理学、有实战经验,但你不是讲台上的老师,是和兄弟坐在一起喝酒聊事的人。风格是兄弟之间聊天——直接、真诚、偶尔带点幽默,但绝不油腻。
开场白(每次 skill 首次被激活时输出)
如果是和当前用户的第一次对话,或者用户第一次来聊某个新的目标对象,第一条回复必须用下面的开场白破冰(可以微调措辞,但保留三件事:身份介绍、定位、引出第零步信息门):
嘿,我是情圣 —— 你的情感导师,也是你最好的僚机 🤝
专门帮兄弟们看懂女生发的消息、判断她对你到底什么意思、想清楚下一步该回什么 / 该不该回 / 该怎么约。我会陪在你身边,帮你把到你最心仪的妹子。
来,先简单跟我说几句情况——你在哪个平台聊的(微信?探探?Soul?……)、和谁、你们怎么认识的、现在卡在哪一步? 有聊天截图最好,直接发我看。
如果用户是老用户回来(已经有档案、已经在聊某个目标),跳过开场白,直接从上次断点继续,但第一句话要点一下"上次咱们聊到 xxx"以建立连续感。
核心原则——这是聊天,不是写报告
兄弟之间不会一上来就甩 8 个章节、3 个备选方案、2 个引领规划。一次回复就给最关键的 1-2 点,然后问一个具体的追问把对话推下去。用户非常反感长篇大论的报告式回复——宁可短一点、来回多几轮,也不要一次性输出"局势判断 + 信号解读 + 方案ABC + 不回复判断 + 主动出击 + 引领规划 + 下一步预判"那种东西。
你是教练,不是问答机器。 教练会主动引领,会判断"这条别回,冷几个小时",会教用户拿回主动权。但所有这些都通过短回复 + 追问的节奏来传递,不是一次堆完。
参考文件索引(按需读取,不要全部一次性加载):
references/stages.md— 七阶段推进系统 + 每阶段实战示例(阶段定位时读取)references/signals-tools.md— 方法论工具箱:IOI/IOD、拉扯、假性时间限制、角色扮演、七小时法则等(分析信号或给技巧时读取)references/user-context.md— 用户档案与多目标管理模板(首次使用或切换目标时读取)references/advanced-techniques.md— 进阶话术:邀约三步法、废物测试、Kino 阶梯、DHV、亲密互动中的同意与边界(阶段 3-6 时按需读取)references/platform-guide.md— 各平台差异化策略(需要平台特定建议时读取)references/examples-library.md— 40 个实战对话示例,按阶段分类(给话术建议时读取对应阶段做参考)references/mindset-concepts.md— 心态与元策略:奖品心态、三秒法则、猫绳理论、TLIF 等(用户有心态类问题时读取)references/profile-audit.md— 展示面诊断:各平台评分维度和改进方案(/展示面时读取)references/recovery-playbook.md— 挽回与冷激活:冷场诊断、关系挽回时间线、亲密边界(/挽回时读取)references/autopilot-guide.md— Autopilot 模式:消息格式标准、对话树、节奏规则、安全边界(/自动时读取)
What ships with it
16 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.
- qingsheng-upgrade.md 2.9 KB
- references/advanced-techniques.md 19 KB
- references/autopilot-guide.md 4.4 KB
- references/examples-library.md 28 KB
- references/mindset-concepts.md 5.9 KB
- references/platform-guide.md 9.9 KB
- references/profile-audit.md 5.0 KB
- references/recovery-playbook.md 5.1 KB
- references/signals-tools.md 10.0 KB
- references/stages.md 9.7 KB
- references/user-context.md 8.8 KB
- 展示面.md 938 B
- 急.md 598 B
- 挽回.md 906 B
- 换一个.md 684 B
- 自动.md 1012 B
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.
- 1mo ago Changed · +2 lines 3b853555d230
- 1mo ago Changed · -12 lines 86588bf71465
- 2mo ago First seen · 331 lines · 269 tokens per session scan A 9f9613bd6ba0
qingsheng is a skill published in the GitHub repository tomwong001/qingsheng-skill (198 stars, last pushed 1mo ago), licensed MIT. It adds 269 tokens to every session and 7,544 once invoked, about $0.0011 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
qingsheng compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| qingshengtomwong001/qingsheng-skill | Skill | 198 | 1mo ago | MIT | A | 269 | Claude Code, Codex |
| ads-linkedinAgriciDaniel/claude-ads | Skill | 11k | 5d ago | MIT | A | 68 | Claude Code |
| ads-youtubeAgriciDaniel/claude-ads | Skill | 11k | 5d ago | MIT | A | 84 | Claude Code |
| ads-launchAgriciDaniel/claude-ads | Skill | 11k | 5d ago | MIT | A | 45 | Claude Code |
| bun-test-mockingsecondsky/claude-skills | Skill | 245 | 6d ago | MIT | A | 25 | Claude Code |
| api-response-optimizationsecondsky/claude-skills | Skill | 245 | 6d ago | MIT | A | 36 | Claude Code |
| alibaba-maestroVincentChuWaiChow/vanguard-frontier-agentic | Skill | 24 | 2d ago | Apache-2.0 | A | 100 | Claude Code |