company-research

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

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.