company-research

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 - 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.