robomotionio ICP Identification
Research a company/idea (deterministic, keyless page fetch) then you, the agent, define the ICP — inclusion + exclusion tables — validate it with the user, and emit a handoff context block for the downstream lead/TAM skill.
When to use
- "Find me leads", "who should I sell to", "who is my ICP?", "map my TAM".
- User provides a company URL/idea and asks for leads or market mapping.
- Sits upstream of all lead-finding and TAM-building skills.
How to run
Step 1 — research the company/idea (deterministic script)
python3 ${SKILL_DIR}/scripts/research_company.py \
--url https://example.com --company "Example" \
--max-pages 8 --output ${WORKSPACE}/company_bundle.json
Python 3 stdlib only. Fetches the homepage + same-domain high-signal pages (pricing,
about, product, solutions, customers, industries) into a {url, title, headings, text}
bundle. Research the URL before asking the user anything answerable from the site.
Step 2 — research market + competitors + buyer signals (you, the agent)
With your search tool: company value prop/pricing (fill gaps the site didn't answer),
market/category + growth stage, top 3-5 competitors ("[company] vs", "[category] competitors"), buyer signals (titles, purchase triggers). Optionally use
perplexity/tavily only if a cited live-web market answer is needed beyond search.
Step 3 — synthesize + validate the ICP (you, the agent)
Write a 5-10 bullet research summary; present for validation. Then define the ICP as two explicit tables:
- Inclusion: titles, seniority, company size, industry, region, signals.
- Exclusion: titles/industries/company types/sizes/specific companies that are NOT a fit.
Keep filter values specific (avoid keyword stuffing). Iterate with the user until approved. Exclusions are first-class — they prevent noisy downstream results.
Step 4 — choose path + hand off (you, the agent)
Recommend TAM mapping (account-first) or lead finding (contact-first) based on ICP + company stage. Emit the ICP context block (Include + Exclude) as JSON so the downstream skill skips redundant intake.
Outputs
- A validated ICP definition (inclusion table + exclusion table), a research summary, the chosen path, and a handoff context block carrying both criteria. Returned in-chat for confirmation and persisted as a workspace asset / Agent Teams attachment.
Credentials / env
- Required: none. The research script is keyless; the ICP synthesis is your job as the agent (no LLM key in the script layer).
- Optional: if
PERPLEXITY_API_KEY/TAVILY_API_KEYis set → cited answer-engine market-sizing; if not → keyless serp/fetch research the agent summarizes (default). The default keyless path needs no key.
Notes & edge cases
- Every intake answer should sharpen a search/exclusion filter; probe vague answers into concrete filter values, not generic strategy questions.
- This skill defines, but does not execute, lead finding — the heavy lifting (Apollo,
scraping) happens in the downstream skill it hands off to (e.g.
apollo-lead-finder). research_company.pycaps each page at 20 KB of text, fetches up to--max-pagesdeep pages, and backs off on HTTP 429/503.