# How to Implement the Bottleneck-Hunter Skill for Supply Chain Constraint Analysis

> Learn to implement the bottleneck-hunter skill for supply chain constraint analysis. This AI workflow maps bottlenecks and identifies investment opportunities. Optimize your supply chain today.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: how-to-guide
- Published: 2026-07-11

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**The bottleneck-hunter skill is a seven-stage AI workflow that transforms high-level macro trends into actionable supply-chain bottleneck maps and investable company shortlists through quantitative filtering and cross-validation.**

The bottleneck-hunter skill in the `xbtlin/ai-berkshire` repository provides a structured methodology for identifying supply-chain constraints within major secular trends. Implemented in the canonical skill definition at [`skills/bottleneck-hunter.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/bottleneck-hunter.md), this AI-driven pipeline decomposes "supertrends" into physical supply layers, scores bottlenecks using quantitative criteria, and screens for investable companies that meet strict financial guardrails.

## Understanding the Skill Architecture

The skill operates as a deterministic workflow defined in [`skills/bottleneck-hunter.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/bottleneck-hunter.md) (lines 1–475). It bridges macro trend analysis with microscopic supply-chain verification, ensuring that only companies meeting rigorous operational and financial criteria surface in the final output. The architecture maintains a single source of truth: the canonical markdown file serves both Claude Code users and Codex users through a lightweight wrapper at [`codex-skills/bottleneck-hunter/SKILL.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/bottleneck-hunter/SKILL.md).

## The Seven-Stage Implementation Workflow

### Stage 1: Super-Trend Confirmation

Filter incoming trends against four quantitative criteria—**continuity**, **physicality**, **scale**, and **acceleration**—defined in the trend validation table (`L21–L27`). Trends must demonstrate sustained momentum and physical-world impact rather than speculative narratives.

### Stage 2: Physical Supply-Chain Decomposition

Decompose the validated trend into five hierarchical layers (Layer 0 → Layer 4) that trace from end-products to underlying infrastructure (`L58–L75`). This physical mapping ensures the analysis targets tangible constraints rather than abstract demand.

### Stage 3: Bottleneck Identification

Evaluate every Layer 2–3 element against six quantitative criteria: **concentration**, **expansion cycle**, **substitutability**, **utilization**, **demand growth**, and **customer-validation lag** (`L18–L26`). Apply a red-yellow-green rating system to classify constraints as **S-level** (critical), **A-level** (significant), or **B-level** (moderate) bottlenecks.

### Stage 4: Company Screening

Map each high-rated bottleneck to listed firms, then apply fast-filter rules (`L68–L74`):
- Public trading status
- Bottleneck-business revenue share > 30%
- Market capitalization < $10 billion
- Daily turnover > $1 million

### Stage 5: Valuation Guardrails

Enforce hard valuation guards to prevent overpriced entries (`L77–L90`):
- Reject if PS ratio > 30× paired with growth < 100%
- Reject if market-cap exceeds 20% of Total Addressable Market (TAM)
- Flag companies failing margin or leverage thresholds

### Stage 6: Cross-Validation

Corroborate each candidate with at least two independent data sources (`L45–L69`). Valid sources include client contracts, production-capacity utilization data, and third-party analyst reports. This step eliminates false positives from promotional materials.

### Stage 7: Output Generation

Render a concise **bottleneck opportunity board** containing a ranking table, one-page investment summary, and action recommendation (`L70–L85`). Persist daily updates to `reports/bottleneck-map/` to maintain a living repository of constraint analyses.

## Invoking the Skill via CLI

Activate the workflow using the slash command documented in [`README.md`](https://github.com/xbtlin/ai-berkshire/blob/main/README.md) (lines 191–200):

```bash
/bottleneck-hunter AI基础设施

```

The command loader resolves the skill definition through [`codex-skills/bottleneck-hunter/SKILL.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/bottleneck-hunter/SKILL.md), which points back to the canonical [`skills/bottleneck-hunter.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/bottleneck-hunter.md) file. This indirection ensures synchronized behavior across AI coding platforms while maintaining a single editable source.

## Core Implementation Logic

The underlying Python execution flow follows this structural pattern:

```python
def run_bottleneck_hunter(trend):
    # 1️⃣ Validate trend against criteria (L21-L27)

    if not validate_trend(trend): 
        return "Trend rejected"
    
    # 2️⃣ Decompose supply-chain (L58-L75)

    layers = decompose(trend)
    
    # 3️⃣ Score bottlenecks (L18-L26)

    bottlenecks = score_layers(layers)
    
    # 4️⃣ Find listed companies (L68-L74)

    candidates = screen_companies(bottlenecks)
    
    # 5️⃣ Apply valuation guardrails (L77-L90)

    vetted = valuation_filter(candidates)
    
    # 6️⃣ Cross-validate (L45-L69)

    verified = cross_validate(vetted)
    
    # 7️⃣ Render report (L70-L85)

    return render_report(verified)

```

## Output Format and Repository Structure

The final deliverable generates a standardized opportunity board:

```markdown
| 排名 | 公司 | 代码 | 市值 | 年收入 | PS | PE | 瓶颈环节 | 瓶颈评级 | 市场份额 | 收入增速 | 信号强度 | 估值判断 |
|------|------|------|------|--------|----|----|----------|----------|----------|----------|----------|----------|
| 1 | 光模块公司A | 600123 | $8B | $1.2B | 12× | 18× | 光模块 | S | 22% | 45% | ★★★★★ | 估值绿灯 |

```

Persistent storage maintains historical scans in `reports/bottleneck-map/`, enabling longitudinal tracking of constraint evolution and company performance.

## Summary

- **The bottleneck-hunter skill** operates through seven sequential stages defined in [`skills/bottleneck-hunter.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/bottleneck-hunter.md), from trend confirmation to final output generation.
- **Quantitative rigor** drives the pipeline: six criteria identify bottlenecks, four filters screen companies, and strict valuation guards prevent overpayment.
- **Cross-validation** requires dual-source verification for every candidate, eliminating reliance on single-point promotional data.
- **Invocation** occurs via `/bottleneck-hunter` with automatic resolution through the Codex wrapper at [`codex-skills/bottleneck-hunter/SKILL.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/bottleneck-hunter/SKILL.md).
- **Outputs** persist to `reports/bottleneck-map/` as standardized opportunity boards containing investment rankings and valuation signals.

## Frequently Asked Questions

### What defines a valid super-trend for the bottleneck-hunter skill?

According to the criteria table at `L21–L27`, valid trends must satisfy four dimensions: continuity (multi-year persistence), physicality (tangible resource impact), scale (market size > $10B), and acceleration (growth rate inflection). Trends lacking physical supply-chain implications fail at this initial gate.

### How are bottleneck ratings (S, A, B) determined?

The scoring matrix at `L18–L26` evaluates Layer 2–3 supply chain elements against concentration ratios, expansion cycle duration, substitutability indices, utilization rates, demand growth curves, and customer-validation lag. Red-yellow-green indicators translate to S-level (critical scarcity), A-level (significant friction), or B-level (moderate constraint) classifications.

### What valuation guardrails automatically exclude companies?

The valuation filters at `L77–L90` automatically exclude companies trading at PS ratios exceeding 30× when growth sits below 100%, or where market capitalization exceeds 20% of TAM. Additional flags trigger for insufficient margins or excessive leverage, ensuring the final shortlist contains only structurally sound candidates.

### How does the skill integrate with different AI coding platforms?

The repository maintains a single source of truth in [`skills/bottleneck-hunter.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/bottleneck-hunter.md). For Codex users, [`codex-skills/bottleneck-hunter/SKILL.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/bottleneck-hunter/SKILL.md) acts as a pointer file that loads the canonical definition, while Claude Code users access the skill directly through the repository structure. Both paths converge on identical logic, ensuring consistent execution across environments.