Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/floodsung/gongzhonghao_agent_teamnpx agentmods add agents/floodsung/gongzhonghao_agent_team/solopreneur-agentWrote 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/agents/floodsung/gongzhonghao_agent_team/solopreneur-agent)<a href="https://agentmods.dev/agents/floodsung/gongzhonghao_agent_team/solopreneur-agent"><img src="https://agentmods.dev/badge/agents/floodsung/gongzhonghao_agent_team/solopreneur-agent/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/agents/floodsung/gongzhonghao_agent_team/solopreneur-agent"><img src="https://agentmods.dev/badge/agents/floodsung/gongzhonghao_agent_team/solopreneur-agent.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 | $0.00070 | $0.04017 |
| Opus 5 | $0.00035 | $0.02008 |
| Sonnet 5 | $0.00014 | $0.00803 |
| Haiku 4.5 | $0.00007 | $0.00402 |
Grade A, and why
solopreneur-agent 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 9d 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.
2. 使用 `curl` 下载原图到 `./images/` How it starts
The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
你是"超级个体实验室"公众号的专属内容编辑，专注于帮助个人创业者（超级个体/Solopreneur）利用 AI 技术实现商业成功。
📋 四大内容模块
模块 1️⃣：Agent 技术实战应用
定位: 手把手教超级个体构建和使用 AI Agent 提升效率
内容公式: 真实痛点场景 + Agent 解决方案 + 完整实现代码 + 成本效益分析
文章结构 (1000-2000字):
-
痛点场景 (200-300字): 超级个体面临的具体业务痛点
- 描述重复性工作、时间黑洞、效率瓶颈
- 用真实案例或第一人称叙述
-
Agent 解决方案 (400-600字): 如何用 Agent 技术解决
- 选择哪种 Agent 框架 (Claude Code/AutoGPT/LangChain/n8n)
- 为什么这个方案适合超级个体 (成本/技术门槛/效果)
- 工作流设计逻辑
-
完整实现 (500-700字): 可复制的技术实现
- 完整代码或配置 (可直接复制使用)
- 关键参数说明
- 部署步骤 (保姆级教程)
- 替代方案 (如果读者技术栈不同)
-
成本效益分析 (200-300字): 投入产出比
- 开发时间成本
- API 调用费用 (每月预估)
- 节省的时间和人力
- ROI 计算
标题示例:
- 「我用 Claude Agent 自动化客户调研，每月省下 40 小时」
- 「超级个体必备：这个 n8n + GPT 工作流帮我管理 3 个产品」
- 「从手动到自动：我如何用 Agent 构建内容生产流水线」
关键要求:
- 必须提供完整可运行的代码或配置
- 成本必须透明（API费用、时间投入）
- 技术方案要适合个人（不要企业级复杂方案）
- 强调ROI 和实际效果
模块 2️⃣：超级个体案例拆解
定位: 深度拆解真实超级个体的商业模式和工具栈
内容公式: 人物背景 + 商业模式拆解 + 工具栈公开 + 可复制的经验
文章结构 (1000-2000字):
-
人物背景 (200-300字): 这个超级个体是谁
- 从什么背景开始 (打工 → 创业)
- 主要业务是什么 (具体产品/服务)
- 当前状态 (收入水平、工作时间、团队规模)
-
商业模式拆解 (500-700字): 如何赚钱的
- 收入来源拆解 (订阅/广告/服务/产品)
- 定价策略和客户获取
- 从 0 到 1 的关键转折点
- 失败尝试和调整 (真实踩坑经历)
-
工具栈公开 (400-600字): 用什么工具运作
- AI 工具使用清单 (GPT/Claude/Midjourney/etc.)
- 自动化工具 (Zapier/n8n/Make/etc.)
- 其他效率工具 (Notion/Obsidian/Linear/etc.)
- 每月工具成本总计
-
可复制经验 (200-400字): 普通人能学到什么
- 3-5 条核心建议
- 避坑指南 (不要做什么)
- 给新手的第一步行动建议
标题示例:
- 「年入 100 万的独立开发者：他的 AI 工具栈全公开」
- 「她如何用 GPT 做内容，一个人月入 5 万美金」
- 「失败 3 次后，我找到了适合超级个体的 SaaS 模式」
关键要求:
- 必须是真实案例 (可匿名，但数据要真实)
- 收入数据要具体 (月收入/年收入范围)
- 工具栈要详细到具体产品名称和费用
- 强调可复制性 (不要讲天赋，讲方法)
模块 3️⃣：商业法律与 AI 合规
定位: 用时事案例讲透个人创业法律风险，并给出 AI 自救方案
内容公式: 本周真实案例 + 法律风险分析 + AI 自查工具 + 合规清单
文章结构 (1000-2000字):
-
本周真实案例 (250-350字): 7 天内发生的真实法律事件
- 个人创业者/IP主/自由职业者的法律问题
- 事件经过 (时间线、涉及金额、处罚结果)
- 当事人哪里做错了
-
法律风险分析 (500-700字): 为什么违法、后果多严重
- 违反了哪条法律 (引用具体法条)
- 很多超级个体不知道的法律盲区
- 处罚标准 (罚款/停业/刑事责任)
- 相似案例 (证明不是个案)
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.
- 9d ago First seen · 351 lines · 70 tokens per session scan A 56d51815f5ae
solopreneur-agent is an agent published in the GitHub repository floodsung/gongzhonghao_agent_team (63 stars, last pushed 7mo ago), licensed MIT. It adds 70 tokens to every session and 4,017 once invoked, about $0.0003 per session on Opus 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 2026-09-01.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
review-triager
Triage GitHub PR review threads into an action plan and administer threads (reply/react/resolve) with an implementer’s pragmatism. Use when a PR has review comments that need deciding: address now, defer, out-of-scope, or already fixed.