# Equity research report

Generate a source-backed equity research report with live fundamentals, market data, valuation context, news, risks, and QVeris call trace.

## 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-equity-research-report/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-equity-research-report
- Skill source: https://github.com/QVerisAI/open-qveris-skills/tree/main/qveris-equity-research-report
- Clone and inspect: `git clone https://github.com/QVerisAI/open-qveris-skills.git && cd open-qveris-skills/qveris-equity-research-report`

## Install Commands

- OpenClaw: `openclaw skills install qveris-equity-research-report`

## Starter Prompts

### Single-company report
Write a full research draft for one public company.

```text
Analyze NVDA using QVeris. Build an equity research report with business summary, latest price action, fundamentals, valuation context, peer comparison, recent filings or news, upside/downside risks, QVeris capabilities used, paid Call count, and what evidence is still missing.
```

### Peer benchmark
Compare one company against peers before writing the conclusion.

```text
Use QVeris to compare AMD, NVDA, and INTC across revenue growth, margin trend, valuation, market reaction, news catalysts, and key risks. Rank the evidence quality before giving a view.
```

### Bear-case review
Pressure-test an optimistic thesis.

```text
Use QVeris to challenge the bull case for AAPL. Pull financials, recent news, filings, and peer valuation, then produce the strongest bear-case memo and the evidence that would disprove it.
```

## QVeris API Actions

- Discover `POST /search`: Find quotes, financial statements, company profiles, peers, filings, transcripts, and news capabilities.
- Inspect `POST /tools/by-ids`: Check ticker coverage, fields, history length, latency, success rate, and billing_rule before paid calls.
- Call `POST /tools/execute`: Execute selected sources and compose them into a research report with citations and a QVeris call trace.

## Estimated QVeris Usage

This workflow usually needs 6-18 paid Calls after free Discover and Inspect preflight. Cost depends on providers, ticker count, and time window.

- Typical paid Call executions: 6-18
- Planning estimate: 6-180 credits
- Free actions: Discover, Inspect
- Paid actions: Call
- Note: Ask for explicit approval before paid Calls. Inspect billing_rule for every selected capability and reduce scope if the estimate is too high.

## Related Cases

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

## Human Page

https://qveris.ai/skills/qveris-equity-research-report