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Analyze high-growth industries with supply-chain bottleneck investing. Use to map value chains, score chokepoints, validate listed companies, and build research watchlists.
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Basierend auf der SOC-Berufsklassifikation
| name | serenity-invest-skill |
| description | Analyze high-growth industries with supply-chain bottleneck investing. Use to map value chains, score chokepoints, validate listed companies, and build research watchlists. |
| license | MIT |
| compatibility | Agent Skills compatible. Designed for Claude Code and OpenAI Codex. Framework use is offline; current market/company facts require web or market-data tools. |
| metadata | {"version":"1.0.0","maintainer":"xiaowei","domain":"investment-research"} |
Use this skill to perform supply-chain bottleneck investment research. The goal is not to chase the hottest end market, but to work backward from an explosive demand trend and identify the upstream nodes that are hardest to expand, easiest to underprice, and most likely to become chokepoints when the thesis becomes real.
This skill helps the agent produce:
The skill must not present outputs as guaranteed returns or personalized investment advice. It should frame results as research candidates and explicitly state uncertainty.
Activate this skill when the user asks about any of the following:
Do not use this skill for:
Serenity-style research asks seven questions in order:
Rule of thumb:
Do not only buy the story. Look for the thing that becomes scarce if the story comes true.
Identify the demand driver and its expected time horizon.
Minimum fields:
If the request involves current market facts, prices, holdings, filings, policy, earnings, or news, use current sources before making factual claims.
Represent the industry as a directed acyclic graph, not merely a linear table.
Default layers:
Keep drilling upstream until at least one of these appears:
Score every important node from 1 to 5 on these dimensions:
| Factor | Meaning |
|---|---|
| Demand acceleration | How fast downstream demand can grow. |
| Supply inelasticity | How hard it is to expand supply quickly. |
| Expansion lag | Time needed to add meaningful capacity. |
| Supplier concentration | Degree to which supply is controlled by few players. |
| Qualification difficulty | Customer certification, regulatory approval, or reliability hurdle. |
| Technical difficulty | Yield, precision, process complexity, or know-how barrier. |
| Pricing power | Ability to raise prices or sustain margins in shortage. |
| Capital intensity | Capex burden and financing difficulty. |
Use this normalized score:
Bottleneck Pressure Score, 0-100 =
100 * (
0.18*demand_acceleration +
0.18*supply_inelasticity +
0.14*expansion_lag +
0.14*supplier_concentration +
0.12*qualification_difficulty +
0.10*technical_difficulty +
0.08*pricing_power +
0.06*capital_intensity
) / 5
Interpretation:
| Score | Meaning |
|---|---|
| 85-100 | Severe chokepoint candidate. |
| 70-84 | Strong bottleneck, worth mapping to companies. |
| 55-69 | Moderate bottleneck; thesis needs more proof. |
| 40-54 | Related but likely competitive or elastic. |
| <40 | Not a bottleneck under this framework. |
For each high-scoring node, identify companies at three levels:
| Level | Company type | Serenity relevance |
|---|---|---|
| Level 1 | Obvious leaders and end-market winners | Higher certainty, lower awareness gap. |
| Level 2 | Critical component/module suppliers | Often attractive if exposure is meaningful. |
| Level 3 | Materials, equipment, test, infrastructure, and qualification gatekeepers | Most Serenity-like if under-researched and supply-constrained. |
Do not treat a company as a candidate merely because it mentions the theme. Require a plausible revenue path.
For each company, verify:
Use references/evidence-checklist.md for a stricter evidence hierarchy.
After node scoring, score each company from 1 to 5:
| Factor | Meaning |
|---|---|
| Node quality | Bottleneck Pressure Score of the node it maps to. |
| Revenue exposure | Related revenue as a share of the business. |
| Evidence quality | Strength of filings, orders, customers, and product data. |
| Awareness gap | How under-recognized the market appears to be. |
| Operating leverage | Ability to convert revenue growth to profit growth. |
| Financial quality | Balance sheet, cash flow, dilution, and execution quality. |
| Liquidity fit | Whether liquidity matches the user's risk preference. |
Suggested formula:
Company Opportunity Score, 0-100 =
100 * (
0.25*node_quality_scaled_1_to_5 +
0.20*revenue_exposure +
0.18*evidence_quality +
0.14*awareness_gap +
0.10*operating_leverage +
0.08*financial_quality +
0.05*liquidity_fit
) / 5
- risk_penalty
Risk penalty is 0-25 points based on overvaluation, hype, customer concentration, weak finances, governance, regulatory risk, and lack of verifiable exposure.
A complete answer should include:
Use references/output-templates.md for tables.
Reject or downgrade candidates with these traits:
Use these files only when needed:
references/framework.md — detailed research process and scoring definitions.references/evidence-checklist.md — evidence hierarchy and company verification checklist.references/output-templates.md — tables for reports, screens, and watchlists.references/risk-controls.md — risk filters, disconfirmation signals, and compliance guardrails.examples/ai-infrastructure-example.md — example output for AI infrastructure.examples/robotics-example.md — example output for humanoid robotics.scripts/score_bottlenecks.py — calculate bottleneck scores from CSV.scripts/score_companies.py — calculate company opportunity scores from CSV.scripts/validate_skill.py — validate basic Agent Skills frontmatter conventions.