dependency-map

Follow CLAUDE.md Writing Standards strictly. Lead with the dependency and its magnitude, no hedge words, tables over prose.

Extract the dependency fingerprint of one thesis: the specific external variables the bull case is actually a bet on. The output is the atom the portfolio-correlation workflow fans out to find names that secretly co-depend on the same variable.

Arguments

$ARGUMENTS = a ticker or thesis name. If empty, ask which thesis.

Read-only

This skill REPORTS analysis; it does not modify vault files (no lock, snapshot, or manifest). To persist a finding, the user asks explicitly ("add this to the [[thesis]]").

Mental Models gate (MANDATORY — CLAUDE.md)

Before analysis, read [[Mental Models/Generalist - Overview]] plus any [[Mental Models/Industry - X]] / [[Mental Models/Lens - X]] the thesis touches (Semiconductors, Automation & AI Readiness, Value Layer Monopoly). Apply the READING PROTOCOL — models are lenses, hunt the single falsifying datapoint. The Value Layer Monopoly lens is primary here: a dependency the company owns (a layer everything above must traverse) is the inverse of a dependency it is exposed to.

Method (single source of truth — the portfolio-correlation workflow reads this section)

1. Read the thesis (Theses/TICKER - Name.md) in full; skim its ## Related Research for supporting evidence. Read _graph.md once for cluster peers (graph-primer Mode A — orient, do not skip reads).
2. Enumerate dependencies in five buckets, each as a concrete named variable (not a category):
   • Demand-side: key customers / end-markets whose spend the revenue rides (name them; note revenue concentration if known).
   • Supply-side: critical inputs / suppliers / capacity the COGS or roadmap depends on (e.g. a specific foundry node, a single-source component, HBM allocation).
   • Technology transition: the shift the thesis is levered to (e.g. accelerated compute, hybrid bonding, GAA, on-device inference) — and what stalls it.
   • Macro driver: the macro variable the bull case needs (AI capex, rates, FX, China access, memory cycle) — kept light here; macro-exposure goes deep.
   • Single points of failure: the one input/customer/approval whose loss breaks the thesis.
3. Rate each dependency: direction (needs it up / down), magnitude (load-bearing / contributory / minor), and stated vs unstated (is it named in the thesis, or a hidden assumption?).
4. Name the 2–3 load-bearing dependencies — the variables that, if they move against the company, break the thesis regardless of execution. These are the correlation atoms.

Output (solo run)

A dependency table — columns: Dependency | Bucket | Direction | Magnitude | Stated? | Note — followed by a 2–3 sentence load-bearing summary naming the variables the thesis truly rests on, and any dependency the company owns rather than is exposed to (Value-Layer angle). No filler.

Workflow reuse

portfolio-correlation (.claude/workflows/portfolio-correlation.js) fans this ## Method out across every thesis, read-only, then finds names whose load-bearing dependencies coincide — correlated bets the market prices independently. Keep ## Method the single source of truth; edits here flow to the workflow automatically.