llm-wiki-setup
Co-create a personal investment-research LLM Wiki (Andrej Karpathy's pattern) where the user's OWN analysis framework becomes a living CLAUDE.md — by interviewing them, NOT by handing them a template. Use whenever the user wants to build a compounding research knowledge base, 投研第二大脑, 投研知识库, or 个人投研 wiki; instantiate Karpathy's LLM Wiki gist for finance/investing; turn their stock-picking, analyst-tracking, or earnings-watching workflow into a structured markdown vault; or build a wiki tracking companies / industries / macro / analysts over time. Pure markdown + wikilinks, NO RAG / vector DB (Karpathy's core idea — do not over-engineer). Also triggers for ingesting research reports / earnings calls / expert notes into an existing wiki, and for post-earnings prediction→fulfillment reviews. Core value = extracting the user's personal investment preferences into THEIR OWN schema, never imposing a standard one.
pinned to #029876dupdated last month
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Pulled from SKILL.md at publish time.
帮用户搭一个金融投研专用 LLM Wiki(Karpathy 模式):纯 markdown 文件 + [[wikilink]] 互联 + LLM 维护,知识随用复利。
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Release history
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Contents
帮用户搭一个金融投研专用 LLM Wiki(Karpathy 模式):纯 markdown 文件 + [[wikilink]] 互联 + LLM 维护,知识随用复利。
但核心不是给一份投研模板——是引导用户把他自己的投资判断方式,提炼成他专属的 CLAUDE.md。
★ 先读这一条(这个 skill 的灵魂)
每个人用自己的语言、自己的投资偏好,建自己的 CLAUDE.md。
两个投资者看同一家公司,关注点可能完全不同——一个看「下季度订单能否超市场预期」,另一个看「管理层电话会上的语气和信心」。给他们同一份模板,就抹掉了让 wiki 有用的那个东西。
- ✅ 你的工作 = 访谈用户 → 提炼他的关注维度 → 用他的话写进 CLAUDE.md
- ❌ 你的失败 = 套一份「标准投研 schema」让他填空,或让他照抄
examples/
examples/investment-research-CLAUDE.md 是一个人长成的样子,给用户看可能性,禁止照抄。它像模板一样被搬走,这个 skill 就失败了。
不碰的红线(Karpathy 原意,别 over-engineer)
纯 markdown + wikilink + grep。不加 RAG / 向量库 / embedding。 知识靠预编译进结构化页「复利」,不是每次 query 重新检索原始文档——这是本模式相对 RAG 的根本区别,也是 Karpathy 的核心 idea。别加回任何检索层,别加 knowledge graph / 自动 health-check 之类机制(社区有些版本加了,那是 over-engineer)。
机制层 vs 规则层(贯穿全程的区分)
| 内容 | 处置 | |
|---|---|---|
| 机制层 | 三层目录 + wikilink + lint + git hook | ✅ 通用工程结构,scripts/init_vault.py 直接装 |
| 规则层 | 看哪些维度 / 怎么记观点 / 要不要分析师归属 / 怎么复盘 / 要长报告还是三行 | ❌ 用户的投资大脑,访谈长出来,绝不给模板 |
机制层照抄没问题(它是 Karpathy 模式的工程卫生,跟「你怎么投资」无关)。规则层照抄 = 背叛方法论。
工作流
Phase 0 — 判断意图
- 新建 vault → Phase 1
- 已有 vault,ingest 一份源 → 直接读
references/ingest_sop.md - 已有 vault,财报后复盘某标的 →
references/fulfillment_sop.md - query → 读 vault 的
index.md+ 相关页,带 citation 综合答;好答案回填 synthesis
Phase 1 — scaffold 机制层
python scripts/init_vault.py <目标目录>
建空骨架(三层目录 + lint + hook 占位 + 空 index/log + CLAUDE 骨架)。这一步只装机制层,不写任何 schema。
Phase 2 — 访谈共创 CLAUDE.md ★核心步骤
读 references/interview.md,按它的 8 个维度一条条访谈用户,把回答用他自己的话写进 <vault>/CLAUDE.md 规则层的占位。
- 一次问一个维度,别一口气灌
- 用户不在乎的维度直接砍(极简 > 全面)
- 卡住才翻
examples/给灵感,明说「别抄,挑你戳中的」 - 自检:写好的 CLAUDE.md 像不像「这个人」?像通用模板就重来
Phase 3 — 启用防腐
cd <vault> && git init
git config core.hooksPath .githooks # local 配置,换机/重 clone 要重设
PYTHONUTF8=1 uv run --no-project --with pyyaml python3 scripts/lint-vault.py wiki # 确认绿灯
已有 vault — 刷新机制层工具
更新本 skill 后,显式刷新已复制进 vault 的 linter 与 hook:
python scripts/init_vault.py --refresh-tools <vault>
只更新 scripts/lint-vault.py 与 .githooks/pre-commit,不碰 wiki/、raw/ 或用户的 CLAUDE.md。文件有变化时先保留 .before-refresh 备份;若备份已存在则 fail-fast,先审阅并移走旧备份再重跑。
Phase 4 — 首次 ingest 演示
拿用户一份真实的源(研报 / 电话会 / 纪要),按 references/ingest_sop.md 走一遍 HITL 5 卡点,让他亲眼看到 wiki 怎么从源长出来。用用户自己的素材,不要用 examples。
后续运营(按需读 references)
| 场景 | 读 |
|---|---|
| ingest 新源 | references/ingest_sop.md(doc_type 用用户自己定的分类) |
| 财报后复盘 | references/fulfillment_sop.md(分析师回测调 analyst-track-record skill,别重造) |
| vault 卫生(派生值漂移) | references/prune_discipline.md |
| 复盘页对抗审查 | references/counter_review.md |
| 怎么访谈提炼用户的投资大脑 | references/interview.md(Phase 2 的完整方法) |
为什么这个 skill 是 inline(不设 context: fork)
它要调 analyst-track-record skill(复盘回测)、跑 Bash(scaffold / lint)、可能并行 Task 取财报数据——subagent 不能调 skill 或 spawn subagent,所以必须 inline。
Next Step
vault 搭好、用户开始 ingest 卖方研报后,如果他想回测某分析师过去准不准 → 建议接 analyst-track-record skill(双维度命中率,有 validated 脚本)。
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mh install skills/llm-wiki-setup