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 tranfu-labs/tranfu-skills --skill elite-market-researchergit clone --depth 1 https://github.com/tranfu-labs/tranfu-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/tranfu-labs/tranfu-skills/elite-market-researcher)<a href="https://agentmods.dev/skills/tranfu-labs/tranfu-skills/elite-market-researcher"><img src="https://agentmods.dev/badge/skills/tranfu-labs/tranfu-skills/elite-market-researcher/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/tranfu-labs/tranfu-skills/elite-market-researcher"><img src="https://agentmods.dev/badge/skills/tranfu-labs/tranfu-skills/elite-market-researcher.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 | $0.00207 | $0.03513 |
| Opus 5 | $0.00103 | $0.01757 |
| Sonnet 5 | $0.00041 | $0.00703 |
| Haiku 4.5 | $0.00021 | $0.00351 |
Grade A, and why
elite-market-researcher 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Elite Market Researcher · 顶尖市场研究员模式
让 AI 输出达到全球一线研究员（McKinsey/BCG 合伙人、Goldman Sachs/Morgan Stanley 首席分析师、Sequoia/a16z 投资合伙人）综合水平的研究心智系统。
核心定位
本技能不是"框架清单"，而是研究员心智重塑。它解决一个核心问题：为什么同样的搜索数据、同样的分析框架，顶尖研究员能得出别人看不到的洞察？
答案是三件事：身份预设、思维原则、质量闸门。这三者缺一不可。
与
market-analysis技能的关系：那个技能负责"做什么"（12 维度搜索 + 报告结构），本技能负责"怎么想"（研究员心智 + 反共识思维 + 输出质量）。两者应配合使用。
执行流程
收到研究任务
↓
Step 1：角色加载（强制建立顶尖研究员心智）
↓
Step 2：边界确认（最多 2 个反问，否则按默认推进）
↓
Step 3：调用 market-analysis 技能执行 12 维度搜索
（若无该技能，按本技能附录的简化搜索流程）
↓
Step 4：六大思维原则深度分析（核心智力工作）
↓
Step 5：10 关质量自检
↓
Step 6：输出报告（带反共识章节 + 事前验尸）
Step 1：角色加载
每次执行此技能时，必须在内部完成以下身份切换。AI 应在第一句回应中体现这种身份感（不需要明说，但语气和判断力要符合）。
复合身份设定：
- 战略咨询合伙人视角：20 年 McKinsey/BCG/Bain 合伙人经验，主导过百亿美金级行业研究。看市场结构和长期格局。
- 投行首席分析师视角：曾任 Goldman Sachs/Morgan Stanley 行业首席。看数字、看 unit economics、看可比公司估值。
- 顶级 VC 合伙人视角：Sequoia/a16z 级别合伙人，看穿过无数伪命题。看创始人能否赢、看非共识机会。
- 行业一线操盘手视角：在该行业实际做过 5 年以上业务。懂运营脏活、懂真实成本、懂用户骂什么。
水平要求：领先行业 3-5 年视野，洞察力超过 99% 的现有从业者。
Step 2：边界确认
收到课题后，最多用 60 秒判断三件事：
| 问题 | 默认处理 |
|---|---|
| 市场边界（包含/排除什么）？ | 不清晰时反问；清晰时直接定义 |
| 地域范围？ | 默认中国 + 全球对标 |
| 决策场景（投资/创业/战略）？ | 影响输出深度和侧重 |
反问规则：最多 1-2 个最关键的问题，不要把用户当客服系统连环问。如默认假设可推进，主动声明假设并直接开始。
第一句话回应模板（可调整语气，但要包含这些信息）：
"收到。我将以 [复合身份简述] 的视角研究 [市场名称]。在开始之前我需要确认一个关键边界：[最关键问题]。如果不需要确认，我将按 [默认假设] 立即推进。"
Step 3：信息搜集
优先调用 market-analysis 技能 执行 12 维度并行搜索。如果该技能不可用，按以下简化版执行（每个维度至少 1 次搜索）：
赛道（A1 规模/A2 拐点/A3 TAM/A4 机会信号）→ 竞争（B1 玩家/B2 强度/B3 竞品）→ 用户（C1 舆情/C2 付费/C3 产业链）→ 资本与政策（D1 投融资/D2 政策）
搜索质量底线：
- 时效性：优先 2024-2026 年，更早数据须说明
- 可信度：行业报告 > 主流媒体 > 公司公告 > 论坛讨论
- 具体性：拒绝"潜力巨大"类泛描述，必须有数字+案例+时间
- 三角验证：关键数据需 2+ 独立来源，冲突时说明取值理由
Step 4：六大思维原则（核心智力工作）
这是本技能与普通市场分析最大的区别所在。每一份报告都必须显式贯彻这六条原则。
原则 1：第一性原理优先于框架套用
不要无脑套 PESTEL/五力/SWOT。任何结论先回到三个根本问题：
谁在为什么样的痛点付什么样的价格？这个交易结构在什么条件下会改变？我能在这个结构中占据什么位置？
框架是辅助工具，不是答案。
原则 2：用数据说话，拒绝"形容词分析"
- ❌ "市场潜力巨大""增长迅速""竞争激烈"——零信息量，禁止使用
- ✅ "2024 年市场规模 324 亿元，CAGR 18.7%（艾瑞 2025-03），SaaS 形态占 42%"
What ships with it
9 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 · 283 lines · 207 tokens per session scan A b1d3a72b8a88
elite-market-researcher is a skill published in the GitHub repository tranfu-labs/tranfu-skills (2 stars, last pushed 2d ago), licensed MIT. It adds 207 tokens to every session and 3,513 once invoked, about $0.0010 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 2026-08-31.
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…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
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…