# Supply chain bottleneck research

A QVeris-powered investment research workflow that maps value chains, finds scarce layers, ranks public-company research priorities, and cites live data capabilities.

## Agent Install Policy

Agents must get explicit user confirmation before installing a skill, writing configuration, or changing the local environment.

1. Read this guide and the manifest: https://qveris.ai/skills/qveris-supply-chain-research/manifest.json
2. Explain to the user what will be installed and which QVeris API actions may be used.
3. Ask for explicit approval before running an install command or changing local configuration.
4. After approval, install the skill and run one starter prompt.

## Official GitHub Source

- Repository: https://github.com/QVerisAI/open-qveris-skills
- Skill path: qveris-supply-chain-research
- Skill source: https://github.com/QVerisAI/open-qveris-skills/tree/main/qveris-supply-chain-research
- Clone and inspect: `git clone https://github.com/QVerisAI/open-qveris-skills.git && cd open-qveris-skills/qveris-supply-chain-research`

## Install Commands

- OpenClaw: `openclaw skills install qveris-supply-chain-research`

## Starter Prompts

### AI infrastructure bottlenecks
Map scarce layers and rank public-company research priorities.

```text
Use QVeris to deeply research AI infrastructure supply-chain bottlenecks. Map the value chain, discover and inspect finance, filings, news, and company data capabilities, call the needed sources, rank the top 5 public-company research priorities, cite QVeris capabilities used, estimate paid Call count, and explain what could weaken each view.
```

### A-share AI semiconductor scan
Build an A-share candidate universe, verify evidence, and rank scarce-layer exposure.

```text
Use QVeris to scan the A-share AI semiconductor value chain. Build at least 20 candidates if data coverage allows, rank layers before companies, call QVeris data for announcements, financial statements, company profiles, and news, then return a top 5 research priority list with evidence strength and main risks.
```

### Challenge a company thesis
Use QVeris evidence to test whether a company truly controls a scarce layer.

```text
Challenge the thesis that this company is a core supplier in its supply chain. Use QVeris to call filings, company profile, financials, quote, news, and relevant social or trade signals. Explain the exact value-chain position, evidence strength, missing proof, and what would make the thesis wrong.
```

## QVeris API Actions

- Discover `POST /search`: Search for market data, financial statements, company profiles, filings, transcripts, announcements, news, and social signal capabilities.
- Inspect `POST /tools/by-ids`: Verify market coverage, ticker parameters, output schema, latency, success rate, and billing_rule before the agent calls a provider.
- Call `POST /tools/execute`: Execute selected data capabilities and compose results into a value-chain bottleneck ranking with source-backed evidence.

## Estimated QVeris Usage

A single-company challenge typically needs a small set of paid calls; a broad theme scan may call several market, filing, financial, news, and company-profile capabilities after free discovery and inspection.

- Typical paid Call executions: 8-25
- Planning estimate: 8-250 credits
- Free actions: Discover, Inspect
- Paid actions: Call
- Note: Ask for user approval before running paid Calls. Inspect each selected capability's billing_rule and reduce scope if the estimated cost is too high.

## Related Cases

- [Use FMP with QVeris](https://qveris.ai/blog/qveris-fmp-finance) - Turn structured financial data into callable agent capabilities for thesis verification.
- [OpenClaw A-share finance assistant](https://qveris.ai/blog/openclaw-a-shares-finance-assistant) - A practical workflow for A-share monitoring and source-backed market research.
- [Twelve Data market capabilities](https://qveris.ai/blog/qveris-twelve-data) - Add market data coverage for global research and candidate screening.

## Human Page

https://qveris.ai/skills/qveris-supply-chain-research