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dym-nuwa-skill-distill-a-person

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Updated 10-18 01:22

About this skill

Skill: dym-nuwa-skill-distill-a-person — Distill a person (direct path, chat-only)

Why use it: you already know who you want to distill (Munger, Feynman, Paul Graham, Trương Nhất Minh…) and want to produce a runnable skill with traceable sources. What it produces: a self-contained skill folder .claude/skills/<tên>-perspective/ comprising SKILL.md, references/research/01-06.md, references/sources/.

⚠️ This entire file is a CHAT command, typed into the agent's conversation window, not the terminal. Shell commands are in 05-helper-scripts-cli.md.


Startup command

蒸馏一个保罗·格雷厄姆
造一个张小龙的视角Skill
distill Paul Graham

What the agent will ask (Phase 0A) and how to answer so you don't get stuck

SKILL.md specifies 6 confirmation items. The most important item is Level (mức chắt lọc) — because this is a long task with many agents and many search rounds; the repo's documentation states outright that a single run on a high-end model can cost tens of USD:

Level Research scope Use when Cost
Fast (nhanh) 3 dimensions (著作 + 对话 + 表达), max 5 sources per dimension testing effectiveness / a person with little material / a tight budget ≈ 1/3 of the standard level
Standard (chuẩn, default) full 6 dimensions most cases medium
Deep (sâu) 6 dimensions + loading all original material (books/subtopics/quotations) planning to open-source a polished version highest

Answer briefly to run the standard level directly:

Nhân vật: Charlie Munger. Chân dung toàn diện. Dùng làm cố vấn tư duy.
Skill mới (chưa có sẵn). Không có tư liệu local. Mức 标准.

Want it cheap:

蒸馏芒格,走快速档,只要3个维度

If you have original material on hand, say so right away — PDF books, interview transcripts, video subtitles, blog exports. The repo calls this Local corpus mode: the agent reads the material you provide first, then only searches the web for the missing dimensions. The quality is markedly higher than pure web search.

Tôi có 2 quyển PDF và 3 file transcript ở ~/tulieu/munger/, ưu tiên dùng chúng.

6-phase pipeline, and 4 stop gates you must approve

Phase What the agent does Deliverable
0.5 Create the skill directory tree before research references/research/, references/sources/{books,transcripts,articles}/
1 Spin up 6 subagents in parallel, each agent one dimension 01-writings.md … 06-timeline.md
🔴 1.5 GATE — research quality summary table table of number of sources / key findings / points of contradiction
2 Distill the thinking framework (3–7 mental models, 5–10 heuristics, expressive DNA, tensions, honesty boundaries) distillation notes
🔴 2.5 GATE — confirm the distillation before writing summary of N models / N heuristics
3 Assemble into SKILL.md according to references/skill-template.md SKILL.md complete
🔴 4 LOCK — 3 tests by independent subagents pass/fail report on 6 criteria
🔴 5 LOCK — 2 agents refining in parallel improvement patch

Six research dimensions in Phase 1:

# Dimension What to hunt for Output file
1 Works arguments repeated ≥3 times (= true beliefs), self-coined terminology, list of suggested books 01-writings.md
2 Dialogue how they respond when pressed, improvised analogies, moments of changing stance, sentences declining to answer 02-conversations.md
3 Expression high-frequency words/syntax, controversial stances, style of humor 03-expression-dna.md
4 The Other patterns outsiders observe, criticisms, comparisons with peers 04-external-views.md
5 Decision-making background + logic of major decisions, recollections, places where words and actions contradict 05-decisions.md
6 Timeline milestones, turning points in thought, dynamics of the last 12 months (anti-obsolescence) 06-timeline.md

Don't skip checkpoint 1.5. The document states clearly: research quality determines the ceiling of skill quality — blocking here is far cheaper than redoing it in Phase 4. If you want to add something, say it directly:

Chiều 05 决策 mỏng quá, chỉ 2 case. Chạy lại agent 5, tìm thêm các quyết định giai đoạn 1990-2000.

Six criteria of Phase 4 (know them in advance to avoid surprises)

Criterion Pass Fail
Number of mental models 3–7, each with sourced evidence <3 or >10
Limitations of each model clearly state the conditions under which it fails only list advantages
Recognizability of expressive DNA read 100 characters and recognize who it is generic ChatGPT voice
Boundaries of honesty ≥3 specific limitations only "cannot replace the person themselves"
Internal tension ≥2 pairs of contradictions overly consistent viewpoints (= fake)
Ratio of primary sources >50% mostly secondary retellings

These six criteria have a self-check script — see 05-helper-scripts-cli.md. Iteration ceiling: Phase 2→4 loop at most 2 times; if after 2 rounds there are still failing items, record them in the boundaries of honesty and deliver the best available version, without endless polishing.

Generated directory structure

.claude/skills/<person>-perspective/
├── SKILL.md
├── scripts/
└── references/
    ├── research/
    │   ├── 01-writings.md
    │   ├── 02-conversations.md
    │   ├── 03-expression-dna.md
    │   ├── 04-external-views.md
    │   ├── 05-decisions.md
    │   └── 06-timeline.md
    └── sources/{books,transcripts,articles}/

Self-containment rule: every research file must be located inside the skill directory. Copying the whole directory works on another machine — this is the condition for open release.

Verified pitfalls

  • Chinese figures: Phase 0.5 automatically switches the source strategy to original Bilibili videos / 小宇宙 podcasts / reputable Chinese newspapers. The source blacklist always includes Zhihu, WeChat Official Accounts, Baidu Baike — all three are excluded in every mode.
  • The generated skill's frontmatter description must be < ~300 words, and the skill-loader has a hard cap of 1024 characters. Stuffing in long-tail keywords will trigger an over-limit error, burn tokens every session, and increase false activations.
  • Example in examples/ is inconsistent in structure: Jobs/Karpathy/Trump/MrBeast use references/research/01-06.md; while Munger/Feynman/Taleb/Musk/Naval use flat, freely named files placed in references/. merge_research.py only works with the 01-06 structure (verified: running on munger-perspective reports ❌ 目录不存在 and exits 1).