Vibe Research is a local financial research workspace in which an AI agent gathers market data, performs multi-step analysis, and preserves reports, evidence, calculations, and research history. It is for investment research across Chinese, US, and Hong Kong stocks, including market reviews, company studies, portfolios, debates, and backtesting. The catalogue contains skills and an instruction for working with this research agent.
Borrowing it
Nothing to install: this file belongs to simonlin1212/Vibe-Research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/simonlin1212/Vibe-Research/main/.agents/skills/industry-chain/SKILL.mdgit clone --depth 1 https://github.com/simonlin1212/Vibe-ResearchWrote 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/simonlin1212/vibe-research/industry-chain)<a href="https://agentmods.dev/skills/simonlin1212/vibe-research/industry-chain"><img src="https://agentmods.dev/badge/skills/simonlin1212/vibe-research/industry-chain/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/simonlin1212/vibe-research/industry-chain"><img src="https://agentmods.dev/badge/skills/simonlin1212/vibe-research/industry-chain.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 | $0.00212 | $0.01929 |
| Opus 5 | $0.00106 | $0.00964 |
| Sonnet 5 | $0.00042 | $0.00386 |
| Haiku 4.5 | $0.00021 | $0.00193 |
Grade A, and why
industry-chain 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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
产业链下钻与不可替代性(industry-chain)
对应 AGENTS.md 投资哲学第一条——"在景气向上的行业里找不可替代的公司"——的操作手册。产业链结构是公开知识,挖出来不等于 alpha;本 skill 的价值在于把"不可替代性"从形容词变成带证据的标签,并给出它被市场定价程度的校准。
0. 三条纪律
- 每一条产业链事实(谁供谁、份额、产能、良率、认证进度、扩产周期)都要有证据 id(公告 / 研报 / 新闻 / 行业数据端点),说不出来源的写"待补",不凭印象填。
- 不可替代性标签只有四个值:
tech_moat(技术 / 工艺 / 专利 / know-how 别人短期追不上)/capacity_moat(工艺成熟度 / 产能布局 / 客户认证周期让对手赶不上,红利期内份额集中)/both/待补。每个标签后必须跟一句证据。 - 本 skill 只回答"它在链条哪一层、凭什么不可替代、市场定价到什么程度",不回答"买不买"。
1. 六步下钻(每一层问同样四个问题)
把公认的龙头当作"需求入口",不在龙头停留,沿供应链往上游走:
需求入口(整机 / 云厂 / 终端龙头)→ ① 关键部件 → ② 核心器件 / 芯片 → ③ 关键材料 → ④ 衬底 / 耗材 → ⑤ 设备 / 检测 → ⑥ 设备的核心零件
每一层四问:
- 不可替代吗?(技术 / 工艺 / 专利;或认证周期 / 产能爬坡期)
- 供给刚性吗?(扩产周期多长、良率爬坡多久、有没有替代路线、新进入者要多久)
- 集中度如何?(寡头 / 双寡头 / 分散;份额证据)
- 被看见了吗?(市值、研报覆盖数、一致预期机构数——"大家都知道"的卡口通常已经定价)
停在"不可替代 + 供给刚性 + 集中 + 关注度低"同时成立的那一层;A 股按"龙头 + 可研究到的最深一层"两条线并记(有无可交易标的只是事实记录,不是结论)。
2. 物理硬筛子(比商业叙事可靠的约束)
| 约束类型 | 看什么 | 证据来源(注册表端点示例) |
|---|---|---|
| 扩产周期 | 新产线从投资到满产要多久;设备交期 | 公告(fetch_announcements / cninfo_announcements)、研报(em_reports / em_industry_reports) |
| 良率 / 工艺成熟度 | 良率爬坡曲线、单位成本、批量稳定性 | 研报、公司互动问答(cninfo_irm)、新闻(em_stock_news / rss_news) |
| 认证周期 | 进入大客户 BOM 需要的验证时长、换供应商成本 | 公告、研报、行业新闻 |
| 替代路线 | 有没有技术路线切换会绕开这一层(路线断层风险) | 行业研报、海外同行(yahoo_news / sec_filings 等) |
| 物料 / 资源垄断 | 全球供给是否集中于少数地区 / 企业(中国独有物理卡口:部分稀土永磁、特种化工材料) | 行业数据、宏观端点 |
| 行业归属与景气 | 行业分类、同行对比、景气方向 | sw_industry、em_industry_comparison、em_concept_blocks |
物理不撒谎:优先用"扩产周期 / 良率 / 认证周期"这类可核的约束,而不是"品牌 / 生态 / 赛道好"这类叙事。
3. 校准:卡口越硬越贵
- 卡口的不可替代性一旦被广泛认知,估值会把它计价完("卡口越硬越贵");本 skill 的标签要与 valuation 的 PEG / 分位一起看:硬卡口 + 高 PEG 是常态,不是机会信号。
- 预期差四问(判断"方向新"维度有没有预期差):① 方向新吗(认知未形成)② 会供不应求吗(需求新增 × 供给刚性)③ 本市场有标的吗 ④ 已经炒过了吗(最关键——多数热门方向卡在这一问)。识别信号:需求侧"路线切换 / everyone betting on";供给侧"售罄 / 交期拉长 / 涨价函"。
- 默认假设与证伪:对任何"新方向",先假设它已被市场定价,再用 ④ 问去证伪——证据是该方向相关标的的涨幅 / 换手 / 研报覆盖数 / 一致预期修正(注册表端点可取),拿不到证据就写"定价程度待补",不凭印象下"已炒过 / 没炒过"的结论。在此前提下可用的分析视角是:① 方向真 + 有订单兑现路径时,分析"兑现确定性";② 有实业、尚未被概念化的标的,分析"基本面与定价的差距";③ 赛道已全部计价时,只比较"业绩兑现度 + 增速能否消化估值"(回到 valuation 口径)。这三条是分析框架,用于组织证据与裁决点,不是筛选或买卖指令。
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 · 68 lines · 212 tokens per session scan A 70c65a93a620
industry-chain is a skill published in the GitHub repository simonlin1212/Vibe-Research (2,435 stars, last pushed 3d ago), licensed MIT. It adds 212 tokens to every session and 1,929 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.
Other skills, from other repositories
daily-deep-brief
A scheduled, pre-market investment briefing for Hong Kong and United States stocks. A deterministic preparation step gathers data and an agent adds judgment, while a later step validates and publishes the result.
hk-stock-analysis
A workspace-aware analysis workflow for Hong Kong-listed stocks. It retrieves prices, technical indicators, market comparisons, and news through a local data pipeline, then adds Hong Kong-specific investment context.
us-stock-analysis
Workspace-aware US stock analysis for kcn. Routes through clawock analyze-us / clawock us-quotes instead of generic web search, then layers fundamental/technical/news analysis on top. Use when user asks to analyze a US ticker (e.g. "analyze AAPL", "look at RKLB", "compare TSLA vs NVDA"), check earnings, run…
portfolio-swarm-review
Multi-agent swarm review of kcn's current holdings. Inspired by TauricResearch/TradingAgents framework already in workspace — three-tier analysis (analysts → bull/bear debate → risk debate + judge) with confidence scoring. Use for post-close reviews, holiday/next-session planning, pre-add sizing decisions, and any…
investment-decision
Run a clawock investment decision — read the prepared request, research with the host's own tools, write decision.json with evidence and an explicit bull/bear debate, and let Python validate and settle. Use when the user asks for an investment decision or a clawock run request is present.
investment-decision
Read the clawock request file, write decision.json, let clawock validate. Use when a clawock run request is present in .clawock/work/.