scan-value-chain - SKILL.md Agent Skill

name: scan-value-chain description: > Scan an investment theme by decomposing its value chain, then surface the handful of names actually worth researching — each with why and the next question. Use when the user has a theme/sector/thread but no specific ticker yet: "what's worth looking at in semis", "scan the AI-infra space", "I'm curious about uranium / obesity drugs / power grid", "who are the picks-and-shovels in X", "map the supply chain for Y", "find the names worth watching in the X value chain". This is the have-a-theme / no-target step — it turns "I don't know what to look at" into a short, reasoned shortlist.

Scan a theme by its value chain

Turn a theme the user can't yet act on into a short list of names worth digging into. The point is NOT a data dump — it's "where is the interesting thing, and why."

Procedure (don't answer from memory — go to the data)

The data is on your CLIs: alice market search to turn a name into a barId, traderhub equity / traderhub board for valuation snapshots & macro, alice analysis quant for where each trades vs its own trend, alice rss grep for the news cluster. (See the alice, alice-analysis, traderhub skills.)

Decompose the chain, not a flat list. Break the theme into structural layers — upstream (inputs, equipment, IP) → midstream (manufacture, core product) → downstream (demand, end-market). Place the real names in each layer. The value is the structure itself: who supplies whom, where the margin/bottleneck sits, who's a picks-and-shovels play. This is the meta-method — apply it to ANY theme, don't hardcode one taxonomy.
Quick read per node. Across the candidates, pull a quick read from the data: a valuation snapshot, any near-term catalysts, and where each trades vs its own trend (stretched or basing). Wide and cheap — you're triaging, not deep-diving.
Find the divergence. Surface 3–6 names where there's something to pull on: cheap vs its layer, margin shifting along the chain, a catalyst close, a leader/laggard gap. Drop the rest — a scan that returns everything returns nothing.
Frame the top-down driver. Is the theme live right now? Tie it to macro — the rate/capex cycle, energy, any news cluster around the theme — the macro frame is what separates a live theme from noise.
Hand off to research. For each surfaced name: one-line WHY + the next question to answer (the "is the thesis real" question). That next question is the baton to the deeper research step — the build-thesis skill.

Output — persist as a file group, don't leave it in chat

Workspace sessions can be destroyed at any time; anything not written to a file is lost. And coding-ifying the workflow is core to this project — research that produces no files is a contradiction. So the result of a scan must land in files.

First time on a theme: propose a small file/directory layout and confirm it with the user before writing — the shortlist, per-name notes, the chain map, whatever this theme needs. Don't hardcode a layout from this skill; settle the shape WITH the user, per theme.
After that: the agreed file group IS the dossier. Every session just CRUDs it — read it, update it, add to it. File-based, git-trackable, survives session loss. That's the coding workflow.

The shortlist and the per-name "next question" from the procedure above are what get written down — so the next session starts from them, not from zero.

Worked example: semiconductors

One theme, worked end to end. Decompose freshly for any other theme — don't pattern-match these layers (a drug theme, say, layers differently: discovery / developer → CDMO manufacturing → distribution / PBM → payer).

Decompose the chain (representative names, not exhaustive):

Upstream — tools & IP (most concentrated moats):
- EDA / IP: Cadence (CDNS), Synopsys (SNPS), Arm (ARM)
- Equipment (WFE): ASML (ASML — sole EUV supplier), Applied Materials (AMAT), Lam (LRCX), KLA (KLAC), Tokyo Electron (8035.T)
- Materials: wafers (Shin-Etsu, SUMCO), photoresist / specialty chemicals
Midstream — make & design:
- Foundry (pure-play make): TSMC (TSM), GlobalFoundries (GFS), SMIC
- IDM (design + make): Intel (INTC), Samsung, Texas Instruments (TXN)
- Fabless (design only): NVIDIA (NVDA), AMD (AMD), Broadcom (AVGO), Marvell (MRVL), Qualcomm (QCOM)
- Memory: Micron (MU), SK Hynix, Samsung — DRAM / NAND / HBM
- Packaging & test (OSAT): ASE (ASX), Amkor (AMKR) — advanced packaging
Downstream — demand: hyperscalers (MSFT / GOOGL / META / AMZN — also rolling their own silicon: TPU, Trainium, MTIA, Maia), devices (AAPL), auto / industrial, servers (SMCI, DELL)

Where the tension is right now — this is what a scan surfaces, not the full roster: the binding constraint for AI silicon has migrated from leading-edge logic to HBM + advanced packaging (CoWoS), so Micron / SK Hynix and TSM's CoWoS capacity + Amkor deserve more attention than the headline GPU names. ASML is the single most concentrated upstream choke point.

Top-down frame: semis run on three clocks — hyperscaler capex, the rate cycle (long-duration growth multiples), and the memory inventory / pricing cycle. Tie the scan to these via the macro and news data.

Proposed file structure (confirm / adjust with the user — don't impose):

semis/
  map.md         # chain decomposition + where the tension sits + macro frame
  shortlist.md   # the 3–6 names to dig now: one-line why + next question each
  notes/         # per-name research, added as you climb scan → thesis (R3+)
    NVDA.md
    MU.md
    ...

map.md and shortlist.md are produced by this scan; notes/<name>.md grow later as specific names get researched. The next session reads shortlist.md and continues — never a cold start.