# company-research

Researches companies, competitors, financials, and news via Exa search. Gathers structured market data directly in the coding agent session.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 933 installs · ★ 63
- Author: exa-labs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/exa-labs/agent-skills/tree/HEAD/skills/company-research/SKILL.md
- Updated: 2026-10-19
- HTML: https://affpapa.org/en/nevibecode/skills/exa-labs-company-research

## What it does

The skill routes requests between two tools based on task complexity. Deep company research, competitor breakdowns, and target lists run through `agent_run`. This agent automatically breaks down queries, executes multiple search passes, and structures the findings using an `outputSchema`. When a run takes longer than the MCP timeout, it polls the task using `runId` until completion.

Quick lookups rely on `web_search_advanced_exa` across categories like `company`, `news`, or `people`. Filter restrictions are strict here. Attempting to add published date filters or domain exclusions to the `company` category triggers a 400 error, so temporal constraints must stay in the query text.

Raw search outputs never get dumped into main context to protect token budgets. The agent falls back to a browser session only when pages require JavaScript rendering or authentication.

## Requirements
- Works with: LinkedIn

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add exa-labs/agent-skills --skill company-research -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/company-research/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/company-research/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add exa-labs/agent-skills --skill company-research -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/company-research/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add exa-labs/agent-skills --skill company-research -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/company-research/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install exa-labs/agent-skills company-research
```

2. **Manually: put the files in place**
- File: `.github/skills/company-research/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add exa-labs/agent-skills --skill company-research -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/exa-labs/agent-skills --path skills/company-research
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/company-research/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add exa-labs/agent-skills --skill company-research -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/exa-labs/agent-skills/tree/HEAD/skills/company-research/SKILL.md

## When to use it
- compile a list of startups with funding stages and URLs
- extract key executives and professional profile links
- track recent company press coverage and product news
- run competitor analysis with structured JSON schemas

## Example prompts
- Find 25 AI infrastructure startups in San Francisco with their websites and funding stages.
- Research Anthropic: funding history, valuation, executives, competitors, and news.
- Check recent news about AI safety published this year.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 172a63b4df96c7e0.

BEGIN THIRD-PARTY FILE 172a63b4df96c7e0
````text
---
name: company-research
description: Company research using Exa. Finds company info, competitors, news, financials, LinkedIn profiles, builds company lists. Use when researching companies, doing competitor analysis, market research, or building company lists.
context: fork
---

# Company Research

## Tool Selection (Critical)

Two Exa surfaces, two jobs:

- **Exa Agent** (`agent_run`) — the default for company research. Use it for deep dives, competitor analysis, multi-angle research (product + funding + news + people), and building company lists. One Agent run handles query decomposition, multi-step searching, and synthesis internally — do not orchestrate many manual searches for work an Agent run covers.
- **`web_search_advanced_exa`** — quick, low-latency lookups: a fast `category: "company"` discovery pass, a single news check, or finding a homepage.

Do NOT use other Exa tools.

## Deep Dives and Lists: Exa Agent

Agent runs may stream to completion in one call. If a run outlives the MCP call window, continue waiting with its returned run ID.

1. Call `agent_run` with a natural-language `query` and, when you want repeatable structure, an `outputSchema` (bound arrays with `maxItems`).
2. If it returns `status: "running"` with a `runId`, call `agent_run` again with only that `runId` until `outputReady` is true.
3. Read `output.text` or `output.structured`, plus `output.grounding` citations, from the `agent_run` result.

Useful inputs: `systemPrompt` (source preferences, dedup rules), `input.exclusion` (companies to avoid), `previousRunId` (a new follow-up run based on a completed run), `effort` (`"low"` default; `"auto"` or `"high"` for more depth).

### Example: company deep dive

```
agent_run {
  "query": "Research Anthropic: product lines, funding history and valuation, key executives, main competitors, and notable news from the last 6 months.",
  "effort": "auto",
  "outputSchema": {
    "type": "object",
    "properties": {
      "overview": { "type": "string" },
      "funding": { "type": "array", "maxItems": 10, "items": { "type": "object", "properties": { "round": { "type": "string" }, "amount": { "type": "string" }, "date": { "type": "string" } }, "required": ["round"] } },
      "competitors": { "type": "array", "maxItems": 10, "items": { "type": "string" } },
      "key_people": { "type": "array", "maxItems": 10, "items": { "type": "object", "properties": { "name": { "type": "string" }, "title": { "type": "string" } }, "required": ["name", "title"] } }
    },
    "required": ["overview", "competitors"]
  }
}
```

### Example: build a company list

```
agent_run {
  "query": "Find 25 AI infrastructure startups headquartered in San Francisco. For each, include what they build and their latest funding stage.",
  "effort": "auto",
````
END THIRD-PARTY FILE 172a63b4df96c7e0

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