company-research · v0.1.0 · 2026-07-18 · sha256 03dafb9fe55c5df5
company-research v0.1.0A
Immutable. This exact content is served forever at /api/v1/blob/03dafb9fe55c5df5.
---
name: company-research
version: 0.1.0
description: >
Research companies, competitors, funding, news, leadership, and market context
with Exa Agent and advanced search. Use when researching companies, competitor
analysis, market research, or building company lists. Writes findings the
caller can fold into brand/competitors.md or brand/landscape.md. Prefer this
over ad-hoc web search for company deep dives. Distinct from competitive-intel
(full brand competitive file methodology) - this skill is the Exa research
engine those foundation skills should call.
allowed-tools:
- Bash
- Read
- Write
- Skill
author: Exa Labs (ported by mktg)
license: MIT
user-invocable: true
metadata:
openclaw:
emoji: 🏢
---
## On Activation
1. Read `brand/positioning.md`, `brand/competitors.md`, and `brand/audience.md` if present. Ground queries in the brand's category and known competitors. All optional.
2. Confirm Exa MCP Agent tools or `EXA_API_KEY`. If missing, stop with the install hint from Prerequisites / `mktg doctor`.
3. Default to Exa Agent for deep dives and lists; use advanced search only for quick single lookups.
4. When `/cmo` or a research agent owns the brand write, return structured findings + sources - do not silently overwrite `brand/competitors.md` unless the user asked to update brand memory.
# Company Research
## mktg runtime note
Prefer **Exa MCP** when available (tools: `web_search_exa`, `web_search_advanced_exa`, `web_fetch_exa`, `agent_run`).
If MCP Agent tools use the older create/wait/get names (`agent_create_run`, `agent_wait_for_run`, `agent_get_run_output`), use those equivalently.
Without MCP, call the HTTP API with `x-api-key: $EXA_API_KEY` (`POST https://api.exa.ai/search`, `/contents`, `/agent`).
Firecrawl remains the path for deep scrape of a **known URL** after Exa discovery.
## Tool Selection (Critical)
Two Exa surfaces, two jobs:
- **Exa Agent** (`agent_run`, or legacy `agent_create_run` / `agent_wait_for_run` / `agent_get_run_output`) - 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 are async: create the run, wait for it, then read the output.
1. `agent_create_run` with a natural-language `query` and, when you want repeatable structure, an `outputSchema` (bound arrays with `maxItems`). Returns an `agent_run_...` ID.
2. `agent_wait_for_run` until the run is `completed` (call again if still running).
3. `agent_get_run_output` - read `output.text` or `output.structured`, plus `output.grounding` citations.
Useful inputs: `systemPrompt` (source preferences, dedup rules), `input.exclusion` (companies to avoid), `previousRunId` (follow-up runs), `effort` (`"auto"` default; `"high"` for hard research).
### Example: company deep dive
```
agent_create_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_create_run {
"query": "Find 25 AI infrastructure startups headquartered in San Francisco. For each, include what they build and their latest funding stage.",
"effort": "auto",
"outputSchema": {
"type": "object",
"properties": {
"companies": {
"type": "array",
"maxItems": 25,
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"website": { "type": "string", "format": "uri" },
"description": { "type": "string", "description": "in 12 words or less" },
"funding_stage": { "type": "string" }
},
"required": ["name", "website", "description"]
}
}
},
"required": ["companies"]
}
}
```
## Quick Lookups: Advanced Search
Use `web_search_advanced_exa` when a single fast search answers the question. Tune `numResults` to intent (a few → 10-20; comprehensive → 50-100; specified → match it).
### Categories
- `company` → homepages, rich metadata (headcount, location, funding, revenue)
- `news` → press coverage, announcements
- `people` → public professional profiles
- No category (`type: "auto"`) → general web results, broader context
Default to `type: "auto"`. Prefer `highlights` for content extraction; do not stack text + highlights + summary in one call.
### Category-Specific Filter Restrictions
Unsupported category/filter combinations return 400 errors:
- `category: "company"` does not support published-date or crawl-date filters, `excludeDomains`, or exact-text filters; express constraints like "founded after 2020" in the query instead
- `category: "people"` does not support published-date, crawl-date, domain, or exact-text filters; put all filtering in the natural-language query
- Without a category (or with `news`), domain and date filters work fine
### Examples
Discovery pass:
```
web_search_advanced_exa {
"query": "AI infrastructure startups San Francisco",
"category": "company",
"numResults": 20,
"type": "auto"
}
```
News check:
```
web_search_advanced_exa {
"query": "Anthropic AI safety",
"category": "news",
"numResults": 15,
"startPublishedDate": "2025-01-01"
}
```
Key people:
```
web_search_advanced_exa {
"query": "VP Engineering AI infrastructure",
"category": "people",
"numResults": 20
}
```
## Token Isolation
Never dump raw search results into main context. Spawn Task agents for Advanced Search calls; for Agent runs, go straight from `output.structured` to the final answer.
## Browser Fallback
Fall back to Claude in Chrome only when content is auth-gated or requires JavaScript rendering.
## Output Format
Return:
1) Results (structured list; one company per row)
2) Sources (URLs; 1-line relevance each - use `output.grounding` from Agent runs)
3) Notes (uncertainty/conflicts)
## References
- Exa Agent guide: https://docs.exa.ai/reference/agent-api-guide
- Company Search reference: https://docs.exa.ai/reference/verticals/company-for-coding-agents
- Exa MCP setup: https://docs.exa.ai/reference/exa-mcp
- Full docs for LLMs: https://docs.exa.ai/llms.txt
## Anti-Patterns
| Anti-pattern | Why it fails | Instead |
|---|---|---|
| Using Claude native WebSearch instead of Exa | Misses niche competitors, companies, and cited sources Exa ranks highly. | Use this skill (or Exa MCP) for all open-ended web research. |
| Calling Exa without `EXA_API_KEY` / MCP auth | Requests 401 and the agent invents results. | Set `EXA_API_KEY` (dashboard.exa.ai) or configure `.mcp.json`; surface the fix via `mktg doctor`. |
| Dumping raw result JSON into the user chat | Burns context and hides the answer. | Synthesize; cite URLs from grounding / result lists. |
## Attribution
Ported from [exa-labs/agent-skills](https://github.com/exa-labs/agent-skills) - adapted for mktg's drop-in contract on 2026-07-18.
Upstream commit: 390ffee2d7e1d0dce2ed8efe4994c2b3c1c0173b
Drift detection: if the upstream skill changes, re-run `mktg-steal https://github.com/exa-labs/agent-skills` to evaluate the diff.