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Trace a paper's intellectual roots: find the 1-2 deepest knowledge trunks it recombines. Based on the Innovation Depth principle — innovation magnitude scales with recombination depth. Use standalone or as an enrichment layer for /read-ken or any bib-note pipeline.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Scaffold a new research project: creates method.md, agenda.md, CLAUDE.md, and AGENTS.md with standard Big 5 folder structure. CLAUDE.md/AGENTS.md embed the lab's truth-over-harmony, coding-discipline, and research-integrity guardrails. Removes the friction of starting from scratch every time.
Use when drafting or revising scientific prose (proposals, paper intros, grant narratives, significance sections) and the sentences feel choppy, list-like, or jump between ideas — or when a reviewer says the writing 'doesn't flow.' Applies the known-new chaining technique distilled from Kangning Huang's NASA NESSF proposal: each sentence ends on a new idea that becomes the next sentence's opening topic (A→B, B→C), and each paragraph's first sentence reaches back to the previous paragraph's last idea. Keywords: flow, transitions, coherence, choppy, doesn't flow, topic chaining, known-new contract, anadiplosis, paragraph hinge.
Analyze causal relationships by searching for and evaluating scientific evidence. Use when the user asks whether X causes Y, inquires about causal mechanisms, or wants to distinguish causation from correlation. Triggers include questions like "does X cause Y", "is there a causal relationship between", "what's the evidence that X leads to Y", or "causation vs correlation" discussions.
Transform dense academic papers into compelling narratives using Malcolm Gladwell's storytelling techniques. Use when users ask to explain, interpret, summarize, or make accessible a research paper, academic article, or scientific study—especially when they want engaging, readable prose rather than technical summaries. Triggers include requests like "explain this paper," "help me understand this research," "make this accessible," "write about this study," or "Gladwell-style explanation."
Transform academic papers into pop-science summaries using the 'ladder-building' science-writing approach developed by Kangning Huang (start from what the reader already knows, then build up step by step). Requires WebSearch to research authors, Read for PDFs. Use when user shares a paper PDF or asks for a paper summary.
Strategic field reconnaissance for a domain you know a little but not enough. Produces an 8-dimension landscape map ending in a gap table ranked by a 3-filter comparative-advantage test against the user's own research pillars. Domain coverage: complexity science, urban science, urban sustainability, industrial ecology, economic geography. Scouting mode only — no prose drafting, no PDF ingestion. Conductor pattern: delegates verification and deep-read work to other Lu Lab skills.
| name | trace-origin |
| description | Trace a paper's intellectual roots: find the 1-2 deepest knowledge trunks it recombines. Based on the Innovation Depth principle — innovation magnitude scales with recombination depth. Use standalone or as an enrichment layer for /read-ken or any bib-note pipeline. |
| allowed-tools | ["Read","WebSearch","WebFetch","Grep","Glob","Bash","Agent"] |
Usage: /trace-origin [PDF path | citation key | paper description]
Purpose: Find the 1-2 deepest intellectual roots (knowledge trunks) that a paper recombines. Not "what does this paper cite?" but "what deep traditions collide here?"
Theory (Innovation Depth): Innovation magnitude scales with recombination depth. Leaf recombination → incremental papers. Branch recombination → new subfields. Trunk recombination → paradigm shifts. This skill measures that depth.
ROOT Physics / Mathematics / Philosophy
│
TRUNK Information Theory Evolutionary Biology Economics
│ │ │
BRANCH Coding theory Population genetics Trade theory
│ │ │
TWIG Error-correcting Neutral theory Gravity models
│ │ │
LEAF Specific 2024 paper Specific 2024 paper Specific 2024 paper
The question: When a paper recombines ideas, how deep is the last common ancestor of those ideas? A paper fusing two 2020 papers from the same lab = leaf LCA. A paper fusing information theory with evolutionary biology = trunk LCA. The deeper the LCA, the bigger the innovation.
Complete all phases IN ORDER.
pdftotext -f 1 -l 3 -layout "[PDF path]" - 2>/dev/null | head -80
Extract: title, authors, abstract, and crucially the introduction's framing — how do the authors position their contribution?
Ask the user for the PDF, or do a web search for the paper by author + year.
WebSearch: "[description] paper [year if known]"
From the introduction, identify:
Output: 2-4 candidate lineage threads (the paper's own declared ancestry).
For each candidate lineage thread from Phase 1, trace it backward toward the trunk.
The paper cites recent work, which cites earlier work, which eventually reaches a seminal anchor — a paper/book/idea that founded or defined a field.
Strategy: Use the 2-hop rule.
WebSearch: "[seminal paper/author] intellectual origins influenced by history of [field]"
WebSearch: "[foundational concept] origin history who invented"
🔴 PARALLELIZE: Launch 2-4 WebSearches simultaneously (one per lineage thread).
For each lineage thread, classify its depth:
| Depth | Definition | Example |
|---|---|---|
| Root | Foundational discipline (math, physics, philosophy) | Calculus, thermodynamics, logic |
| Trunk | Major intellectual tradition (>50 years, >1 field) | Information theory, natural selection, general equilibrium |
| Branch | Established subfield (10-50 years) | Urban scaling, neutral ecology, deep learning |
| Twig | Active research program (5-15 years) | CD index, transformer architecture |
| Leaf | Specific recent contribution (<5 years) | A particular 2024 paper |
Goal: Find the deepest divergence point between the paper's lineage threads. That's the innovation depth.
For each root (1-2, ideally 2), construct a lineage chain:
ROOT 1: [Trunk-level tradition]
└─ [Branch]: [key figure/work, year]
└─ [Twig]: [more recent development]
└─ THIS PAPER's contribution from this thread
ROOT 2: [Trunk-level tradition]
└─ [Branch]: [key figure/work, year]
└─ [Twig]: [more recent development]
└─ THIS PAPER's contribution from this thread
RECOMBINATION DEPTH: [Trunk / Branch / Twig / Leaf]
The last common ancestor of Root 1 and Root 2 is at the [X] level.
Based on recombination depth:
| LCA Depth | Innovation Type | Rarity | Example |
|---|---|---|---|
| Trunk–Trunk | Paradigm shift | Very rare | Darwin (geology × economics → evolution) |
| Trunk–Branch | Field creation | Rare | Bettencourt (physics × urban studies → urban scaling) |
| Branch–Branch | Subfield innovation | Common | Most interdisciplinary papers |
| Twig–Twig | Incremental extension | Very common | Most papers |
| Leaf–Leaf | Replication/refinement | Routine | Follow-up studies |
/trace-origin)Present the results as a structured summary:
## Intellectual Roots: [Paper Citation]
### Root 1: [Tradition Name]
**Lineage**: [Root] → [Branch] → [Twig] → this paper
**Key ancestor**: [Seminal work/figure]
**What this paper inherits**: [specific concept, method, or framing]
### Root 2: [Tradition Name]
**Lineage**: [Root] → [Branch] → [Twig] → this paper
**Key ancestor**: [Seminal work/figure]
**What this paper inherits**: [specific concept, method, or framing]
### Recombination Analysis
**LCA depth**: [Trunk / Branch / Twig / Leaf]
**Innovation type**: [from table above]
**The recombination**: [1 sentence: what collision of ideas makes this paper possible]
Return a compact block to be inserted into the bib note:
## Intellectual Roots
- **Root 1**: [Tradition] via [Seminal Work] → [what this paper inherits]
- **Root 2**: [Tradition] via [Seminal Work] → [what this paper inherits]
- **Recombination depth**: [Level] — [1-sentence explanation]
Where it fits in /read-ken: After Phase 3 (Field Context). The intellectual roots enrich the "So What?" section with deeper historical grounding.
Where it fits in any bib-note pipeline: Between field-context analysis and connection discovery. trace-origin provides the deep ancestry; connection discovery maps to your existing notes.
How to call from another skill: Spawn as subagent:
Agent: trace-origin analysis
subagent_type: general-purpose
prompt: "Run /trace-origin for [paper]. Return the compact integration output block."
Root 1: Allometric scaling (biology) └─ Kleiber's law (1932): metabolic rate ~ mass^0.75 └─ West, Brown, Enquist (1997): fractal network theory └─ THIS PAPER: applies scaling framework to cities
Root 2: Urban economics └─ Marshall (1890): agglomeration economies └─ Jacobs (1961): diversity drives urban vitality └─ THIS PAPER: social network density as mechanism
LCA depth: Trunk–Trunk (biology × economics) Innovation type: Field creation (urban scaling as new field) The recombination: What if cities are organisms? Metabolic scaling theory meets agglomeration economics.
This skill operationalizes the Innovation Depth principle: every paper has roots, and the depth of those roots tells you the magnitude of the innovation. Most papers are leaf-level recombinations. The ones that matter recombine trunks.
The skill is deliberately lean — it doesn't replace /read-ken's author research or any other skill's connection discovery. It adds one thing: the vertical dimension. Where those skills map the horizontal network (what connects to what), trace-origin maps the vertical ancestry (where does this come from, and how deep?).
This skill formalizes a habit Mingzhen learned from Lars Hedin (Princeton ecology). When reading a paper, Lars would ask: where does this idea really come from? Not the immediate citation, but the deep root. That habit of digging past the bibliography into the intellectual ancestry is what this skill automates.