Visitor Company Brief

Visitor Company Brief

Produce a structured deep-dive on a single named company. Anchor every section in real Leadfeeder data. This is the depth complement to the Daily Visitor Brief (which is breadth across many companies in a 24h window).

Language

Produce the entire response in the language the user wrote in — English request → English output, German request → German output, and so on. This applies to everything the skill emits: the summary, all section headings and field labels, reasoning, edge-case messages, and the credit-consumption warning and confirmation. Do not translate data values — company names, contact names, URLs, page paths, email addresses, tag names, and ICP names stay verbatim as the tools return them. Copy-paste follow-up prompts should also be written in the user's language (they still trigger the right skill). If the input language is unclear, default to English.

When this skill triggers

Trigger when the user names ONE specific company and asks for research, a profile, a brief, or a deep dive. Do not trigger if the user is asking about multiple companies or a ranked list across visitors — that is the Daily Visitor Brief skill. Trigger silently. Honor explicit modifiers in the prompt (engagement window, focus area, depth).

Inputs to determine before executing

Identify these from the user's prompt. Use defaults if unspecified — do not ask.

• Company identifier — the name, domain, or Leadfeeder company ID provided in the prompt. Required. If multiple candidates match, see "Disambiguation" below.
• Engagement window — default last 90 days. Honor explicit overrides ("last 30 days", "this quarter", "since January").
• Focus area — default: full brief. If the user is narrow ("focus on hiring", "just engagement", "ICP fit only"), produce a focused version that drops the other sections.

Workflow

Execute these tool calls. Always re-fetch ICPs/personas (do not cache across runs).

Step 0 — Resolve the Leadfeeder account (before any other tool call)

Determine the account_id to use for every tool call in this skill, in priority order:

1. Account named in the request (highest priority). If the prompt or scheduled-task instruction explicitly names an account — an account ID (e.g. "account 01234") or an unambiguous account name — use that account for all tool calls. This overrides every source below and is the recommended way to make an unattended/scheduled task fully self-contained.
2. Configured Account ID. Otherwise, the plugin's configured Leadfeeder Account ID is: ${user_config.account_id}. If that resolves to a real, non-empty account ID (not blank, and not the literal unsubstituted ${user_config.account_id} token), use it for all tool calls and do not call get_account_info or ask the user.
3. Already selected this session. Otherwise, if an account was already chosen earlier in the session, reuse that.
4. Fallback. Otherwise call get_account_info. If exactly one account is returned, use it. If several are returned, ask the user to pick. When running unattended (e.g. a scheduled task) asking is impossible — if no account was named and none is configured, stop and report that a Leadfeeder Account ID must either be named in the task prompt or set in the plugin configuration for automated runs.

Step 1 — Resolve the company

Determine the Leadfeeder company_id from the user's input.

• If the input is already a numeric company ID (e.g. 228035150), skip to Step 2.
• If the input is a company name or fragment (e.g. "Acme Corp", "Acme"), call search_companies with the name as the query. Pick the highest-confidence match.
• If the input is a domain (e.g. "acme.com"), call search_companies with the domain.
• If search_companies returns multiple plausible matches, present the top 3 (name, country, employee count, industry) and ask the user to disambiguate before continuing. Never silently pick when there is real ambiguity (e.g. two German companies with similar names).
• If search_companies returns no matches, tell the user clearly. Offer to expand the search (e.g. with a broader name fragment) rather than fabricating a profile.

Step 2 — Load ranking context

Call get_icps and get_buyer_personas. Always re-fetch on every invocation. The calls are free (no credits). Use the response to score the target company against ICP and persona criteria in Step 5.

Step 2.5 — Subscription coverage & credit confirmation — before any credit-consuming call

get_company, search_companies_signals, and get_company_financials are credit-consuming — 1 credit per company unless it was accessed within the last 12 months (companies in your web-visits feed are inside that free window). First probe coverage for free: call search_web_visits filtered by company_id over the last 12 months (reuse it for Step 4 engagement).

Then, before any credit-consuming call, do not call get_company or search_companies_signals yet — flag it and get a single go-ahead. The target isn't guaranteed to be a visitor, so use this conditional wording, never the generic "1 credit per company" alarm:

To build this brief I need {Company}'s firmographics and signals using get_company and search_companies_signals — credit-consuming tools. If {Company} is in your Web Visitors feed, its 12-month access window has already started, so no credits are charged for a record already unlocked within the last 12 months. If it hasn't visited in the last 12 months, this will consume about 2 credits (firmographics 1 + signals 1; +1 more only if you later ask for financials). Want me to proceed?

On a clear yes → proceed with Steps 3–6 and report the actual meta.credits.charged (0 for a covered/visitor record). On no → give only the free basics (name and website from search_companies, and "no visit activity in the last 12 months") and stop.

Step 3 — Pull firmographics

Only proceed here once Step 2.5 is COVERED or the user has explicitly consented to the credit cost. Call get_company for the resolved company_id with include=crm_connections.crm_record.crm_owner,tags. Track meta.credits.charged (0 = it was in the free 12-month window; 1 = this call charged).

Capture: industry, employee_count and range, location (HQ city and country), B2B/B2C orientation, founded year, revenue/earnings/net_worth where present, intent score and tier (from attributes.intent.score (0–10, may be null) and attributes.intent.score_tier — nested under attributes, not a top-level intent.score; if null, show "not yet scored"). Prefix the displayed score with a tier dot by score_tier: 🟢 high · 🟠 medium · ⚪️ low (omit the dot when "not yet scored"), description, legal form, register status.

Use one canonical figure per metric across the entire brief. get_company returns both an exact employee_count (e.g. 382) and a coarser range/bucket. Treat the exact employee_count as the single source of truth and use that same number everywhere — the one-sentence intro, the Snapshot, and any reasoning. If you round it in prose, round the same number consistently (382 → "~380" or "380+" — never "230+" or a different bucket). Same rule for revenue and any other figure. Two different numbers for the same metric in one brief (e.g. intro says "230+ employees", Snapshot says "382") is a trust-breaker — the intro and Snapshot must never disagree.

CRM connections (sideloaded). Request include=crm_connections.crm_record.crm_owner (plain include=crm_connections returns only a stub — crm_record as {id, type} with no name, URL, or owner). A populated relationships.crm_connections means the company is in the CRM. Each crm_record is JSON:API-shaped and polymorphic (crm_record.type = crm_organization | crm_contact | crm_lead): read URL from crm_record.attributes.crm_url; name by type (crm_organization → attributes.name; crm_contact/crm_lead → attributes.first_name + attributes.last_name); owner from crm_record.relationships.crm_owner.attributes.name (say "owner not set" if only a stub). Render as a markdown link — the record name is the clickable text and crm_record.attributes.crm_url is the target; never print the raw URL or leave the name unlinked. This complements the firmographics with the company's status in the user's own CRM — surface it in the Snapshot and factor it into the recommendation.

Tags (sideloaded via include=tags). Capture each assigned tag's attributes.name and attributes.color. Note: color is an integer palette index (0–N), not a hex code — the API doesn't expose the hex, and a plain markdown brief can't render background-filled chips anyway. So surface tags as plain-text chips (backtick `Hot Lead` style) using their names; do not invent a colour. Show them in the Snapshot because customers use tags to prioritise accounts, so they inform the recommended next move.

Ignore web_engagement.last_visit_date — it lags the real visits stream. Use Step 4 as the source of truth for visit timing.

Flag register status out_of_business or non_company immediately and stop the brief. Tell the user the company is not actionable and skip the rest of the workflow.

Do not call get_company_financials by default. It charges per call. Only call it if the user explicitly asks for "financials", "company financials", "balance sheet", or similar.

Step 4 — Pull engagement history

Call search_web_visits with filters.company_id set to the resolved company. Use the user's engagement window (default last 90 days).

Aggregate the response to produce:

• Total sessions in the window
• First seen and last seen timestamps in the window
• Top landing pages by visit count (deduplicate paths, show top 5)
• Top traffic sources (Direct, Google organic, Bing organic, Google CPC, LinkedIn, etc.)
• Trend — qualitative read: accelerating, stable, declining, dormant. Compare the most recent 30 days vs the 30 days before.
• Identified visits — count only. Do NOT expose contact names or emails. Note the number of distinct identified contacts.
• Deep visits — flag any session with page_depth >= 3 or visit_length >= 60s as a "deep visit" worth calling out. In the output use plain language (this brief is read by sales reps too): "viewed 3 pages", "spent 71 seconds" — never "page depth 3" or "dwell". page_depth/visit_length are internal API fields; translate them into plain page counts and seconds, never print the field name or the word "depth".

Step 5 — Pull contacts on file

Call search_contacts filtered by the company ID. Use the response to produce a non-PII summary:

• Total contacts on file (count)
• Department breakdown (e.g. 12 in Engineering, 8 in Sales, 5 in Marketing)
• Seniority breakdown (e.g. 2 C-level, 4 VPs, 10 managers, 15 ICs)
• Buyer Persona matches — for each configured persona, count how many contacts match; flag ✅ when ≥1 and ❌ when zero (an honest gap callout — surface the empty personas, don't hide them)
• Decision-maker availability — ✅ Yes / ⚠️ Limited / ❌ No: are there senior-enough contacts (top management, VP/Director level, etc.) on file to make outreach worth doing?

Do NOT list individual contact names, emails, phone numbers, or LinkedIn URLs in the brief output. Contact detail belongs in the separate Find My Buyer skill, which is invoked explicitly. This brief is a research overview, not a contact list.

Step 6 — Pull recent signals

For a COVERED company (visited in the last 12 months) this is free — always run it; do not gate or skip it on cost grounds. For an UNCOVERED company it charges 1 credit (if the company has signals), so only run it under the consent obtained in Step 2.5. Call search_companies_signals for the single company_id. Apply the same default filters as the Daily Visitor Brief:

• Recency window: event_date.from = today minus 90 days (or override the user's engagement window if longer).
• Exclude regulatory noise: Pass an explicit categories array containing every category EXCEPT regulatory_compliance_updates. Full default categories:

["business_expansion", "competitive_landscape", "event_participation", "industry_recognition", "leadership_changes", "corporate_challenges", "mergers_and_acquisitions", "customer_acquisition", "investment_activity", "product_and_service_development", "partnerships_and_collaborations", "financial_struggles", "job_ads", "register_updates"]

Group the returned signals by category. Highlight the ones that move the recommended action (hiring spikes, funding events, leadership changes, M&A activity, financial stress).

Signal language — plain, and always explain relevance. The category filtering is an internal mechanic — never surface it in the output (no "non-job-ad", no category names, no "excluding regulatory", no "90-day window" jargon). When nothing relevant is found, say exactly "No notable signals in the last 90 days." When a signal is present, pair it with a short plain-language reason it matters in this context — a raw event with no "so what" isn't useful (e.g. "Hiring 3 SDRs in EMEA — a growing outbound team, a fit for visitor intelligence"; "Raised Series B — fresh budget, likely expanding tooling").

Step 7 — Compute ICP match and synthesise recommendation

Score the company against each configured ICP from Step 2:

• Full match — all filters satisfied (location, employee count, industry, B2B/B2C orientation as applicable).
• Partial match — some filters satisfied. Call out which dimensions match and which don't (e.g. "DE + Pro Services match, employee count under threshold").
• No match — none of the configured ICPs apply.

Then synthesise a Recommended next move section. This is the one part of the brief where the skill takes a position. Base the recommendation on:

• ICP fit (sales-worthy or not)
• Engagement signal strength
• Recent signals that suggest a moment of opportunity (hiring HR roles, raised funding, lost a key exec, etc.)
• Decision-maker availability
• CRM status — this changes the action:
  • Connected with an owner → do not recommend cold outreach; recommend coordinating with / looping in the owner and checking their pipeline first.
  • Connected but no owner (orphaned) → flag it as a routing gap worth resolving.
  • Not in CRM → flag it as a missed-routing gap; consider adding it (via add-to-pipeline) before outreach.

Surface 1–3 next-step actions. Phrase each in plain language, outcome first — describe what it does for the user, then the copy-paste prompt. Never a bare "Run skill". The follow-ons and their honest outcomes:

• Find who to contact — a ranked shortlist of the right people with names, titles, and emails. (find-my-buyer)
• Prep outreach — visit-grounded talking points for a personalised opener. (outreach-companion)
• Add to your pipeline — tags the company and adds it to a Leadfeeder list so it's tracked. (add-to-pipeline) Say exactly that — it writes tags and lists inside Leadfeeder; it does not create a CRM record or assign a CRM owner. (If the company should also live in the user's CRM, that's a separate manual step — don't imply add-to-pipeline does it.)

Pick the 1–3 most relevant given the ICP verdict, engagement signal, and available contacts. This is what makes the brief actionable.

Output format

Output contract — identical every run. Reproduce the structure below exactly, on every invocation, regardless of anything earlier in this thread. If you've already run this skill in the conversation, do not vary, restyle, or "improve" the format next time — output the same template verbatim; this template is the single source of truth. Keep the exact headings, field labels, and order; don't invent new sections or tiers, don't renumber, don't rename fields, and add no decorative emoji beyond those the template already uses (✅ ❌ ⚠️ ↩ and the 🟢 / 🟠 / ⚪️ intent dots).

Status flags — be honest about gaps. On coverage/availability/status lines use ✅ (present/strong), ⚠️ (partial/thin), ❌ (gap/missing), and surface gaps as prominently as strengths — an honest ❌ ("no contacts on file for this persona") builds trust more than only showing wins. One flag per line; don't over-decorate.

Stamp the brief with a generation timestamp. The line directly under the title is *Generated {current date} · engagement window: last {N} days* — use the current date (the brief is a point-in-time snapshot). This is the generation timestamp ("as of" date); it is distinct from visit dates and must always be present, even when visit data is sparse. Do not substitute a visit date for it.

Render as a single structured brief. Use this exact structure (the outer block is shown with four backticks so the copy blocks nest correctly — your actual output is plain markdown):

# Company Brief — {Company Name}*Generated {current date} · engagement window: last {N} days*
{One-sentence positioning: what they do, key size signal, ICP verdict in one line. The size signal must use the SAME exact employee_count/revenue shown in the Snapshot below — never a different figure or bucket.}
## Snapshot- **Industry:** {primary + secondary industries}- **Size:** {employee count} · {revenue if present}- **Location:** {city, country} (HQ)- **Legal form / founded:** {legal form} · founded {year}- **Intent score:** {🟢 high / 🟠 medium / ⚪️ low} {score} ({tier})- **ICP match:** {✅ Full / ⚠️ Partial / ❌ No match} — {one-line reason}- **Tags:** {Assigned tags as plain-text chips by name — e.g. `` `Hot Lead` ``, `` `DACH` ``. Omit the line if none. Tag colours are configured in Leadfeeder but can't render as background-filled chips in this markdown brief, so show names only.}- **CRM:** {If connected: "[{record name}]({crm_record.attributes.crm_url}) · Owner: {owner name — or 'owner not set'}" (record name derived by type per the capture rules) — one line per record if more than one is linked. If not: "Not found in your CRM".}
## Engagement (last {N} days)- **Total sessions:** {N}- **First seen / Last seen:** {date} / {date}- **Top landing pages:** {/path1, /path2, /path3}- **Top sources:** {Direct, Google CPC, Bing organic}- **Deep visits:** {count and short description of the deepest one, e.g. "1 deep visit yesterday — 3 pages, 71s, hit /pricing"}- **Trend:** {accelerating / stable / declining / dormant} — {one-line interpretation}- **Identified visits:** {N sessions from {M} distinct identified contacts} (names withheld; see Find My Buyer skill)
## People on file ({N} contacts)- **Departments:** {top 3 departments with counts}- **Seniority:** {C-level: X, VP/Director: Y, Manager: Z, IC: W}- **Buyer Persona matches:** (flag each — ✅ if ≥1 match, ❌ if zero)  - ✅ "{Persona name}" → {N matching contacts}  - ❌ "{Persona name}" → 0 (gap: no contacts on file)- **Decision-maker availability:** {✅ Yes / ⚠️ Limited / ❌ No} — {one-line on what's there}
## Recent signals (last 90 days){Group by category. Use a sub-heading per non-empty category. Omit empty categories entirely. Each grouping's cluster gets a one-line "why it matters"; never expose filter mechanics. If nothing relevant: replace this whole section with the single line "No notable signals in the last 90 days."}
### Hiring ({N} ads)- {Role 1} — {date}- {Role 2} — {date}- ... (cap at 5, summarise the rest as "+N more")*Why it matters: {one plain line, e.g. "hiring across HR + IT Security suggests an org rebuild — a fit for our RevOps narrative"}.*
### Leadership changes ({N})- {Event} — {date}*Why it matters: {e.g. "new decision-maker settling in — a natural moment to introduce a new approach"}.*
### Funding / Investment ({N})- {Event} — {date}*Why it matters: {e.g. "fresh capital — expansion mode, likely evaluating new tooling"}.*
(Other categories as relevant, each with its own "why it matters" line.)
## Recommended next move
{One short paragraph synthesising the read. Then 1–3 actions, formatted as a list.}
- **{Action 1}** — {short rationale, e.g. "Find who to contact — a ranked shortlist of the right people."}- **{Action 2}** — {short rationale}- **{Action 3}** — {short rationale}
Copy any of these to run the next step (each is a ready-to-send prompt with a one-click copy button):
```Who should I reach out to at {Company Name}?```
```Prep me for outreach to {Company Name}```
---
**Company website:** {company.url from get_company}

Copy-block convention. Emit one fenced code block per recommended action above — fenced blocks give the user a one-click Copy button. Map each action to its ready-to-send prompt: find-my-buyer → Who should I reach out to at {Company Name}?; outreach-companion → Prep me for outreach to {Company Name}; add-to-pipeline → Add {Company Name} to a list in Leadfeeder. Only include the blocks for the actions you actually recommended.

Edge cases

• Company not found by search_companies. Tell the user clearly. Offer to expand search ("try a different name fragment or a domain"). Do not fabricate a profile.
• Multiple plausible matches. Present top 3 with disambiguation criteria (country, employee count, industry). Wait for user input before continuing.
• Register status is out_of_business or non_company. Stop the brief immediately. Tell the user and offer an alternative ("Did you mean a different entity?"). Do not waste tokens on a dead company.
• No web visits in the engagement window. Say so clearly. Optionally offer to expand the window. Do not invent visits.
• No contacts on file. Say so. Flag this as a gap — recommend running enrichment ("Want me to find contacts via the Find Contact Data job?") but do not auto-trigger it (cost).
• No signals in the 90d window. Say "No notable signals in the last 90 days." Move on. This is not unusual for smaller or quieter companies.
• ICP mismatch. Surface the verdict honestly. Do not pad with "but they could still be interesting" unless the engagement signal is unusually strong (e.g. deep pricing visits).
• User asks for financials. Then and only then call get_company_financials. Quote the credit cost in the response.
• Tool errors. Report transparently. Do not silently substitute stale data.

Source citation rule

Every claim in the brief must be grounded in data returned by the Leadfeeder MCP tools. Use the company's website (company.url field from get_company) as the actionable link in the footer. CRM record name, URL, and owner must come from the sideloaded crm_record (and its crm_owner) under relationships.crm_connections — crm_record.attributes.crm_url, the name from crm_record.attributes per record type, and crm_record.relationships.crm_owner.attributes.name — never infer that a company is (or isn't) in the CRM without that data; if no connection is returned, treat it as "Not found in your CRM". Do not invent URLs.

Internal consistency (trust). Beyond the canonical-figure rule in Step 3, every fact restated across sections — counts, country/city lists, dates, page paths — must match exactly. A stated count must equal the items you list, and a location set must be the same set wherever it appears (cities and countries resolving to the same places). The intro, Snapshot, Engagement, People, and Recommended-next-move sections must never disagree about the same fact. Re-check before finishing.

Known gap (same as Daily Visitor Brief): The MCP get_company response does not currently include a Leadfeeder app deeplink. Until the MCP adds this, the brief footer links out to the prospect's own website only. If a future MCP version returns an app URL field, update this skill to add an "Open in Leadfeeder" line below the "Company website" line.

What NOT to do

• Do not call a visitor an "account" or a "hot/warm lead." A website visitor is a company — "account" implies a CRM relationship this data doesn't carry. Reserve "account" for the user's Leadfeeder account (workspace/ID) or a genuine CRM record; use "high-intent", "active", "engaged", or "returning" instead of "hot"/"warm".

• Do not expose individual contact PII (names, emails, phone numbers, LinkedIn URLs). Counts, departments, seniority levels, and persona-match counts are fine. Identifying detail is not.

• Do not trigger enrichment jobs (create_company_enrichment_job, create_find_contact_data_job). This brief is research, not action. Offer to invoke them as a "Recommended next move" only.

• Do not call get_company_financials by default. It charges per call. Only on explicit request.

• Do not invent data. ICP match, signal counts, page views, contact counts — only report what the tools return.

• Do not chain to outreach drafting, CRM writes, or list management on direct user invocation without explicit opt-in. Recommend them as next moves using the skill names above. Exception: if invoked by the Leadfeeder Agent as part of a declared multi-step chain, the agent may proceed with find-my-buyer, outreach-companion, or add-to-pipeline without waiting for confirmation.

• Do not produce a brief for an out_of_business company. Stop and notify the user instead.

Example: good output

# Company Brief — Acme Corp*Generated 2026-05-29 · engagement window: last 90 days*
European technical inspection, testing, certification, and training group. €1.58B revenue, 14,271 employees across 70+ countries. ✅ Full ICP match — DE · Pro Services · enterprise size · B2B.
## Snapshot- **Industry:** Technical Testing & Analysis · Management Consultancy · Educational Support- **Size:** 14,271 employees · €1.58B revenue (2023)- **Location:** Hannover, DE (HQ) · 70+ country presence- **Legal form / founded:** GmbH- **Intent score:** 🟢 10 (HIGH)- **ICP match:** ✅ Full — DE location, Pro Services industry, 10000+ employee bucket, B2B- **Tags:** `Tier-1 Pro Services` · `DACH`- **CRM:** Not found in your CRM
## Engagement (last 90 days)- **Total sessions:** 4- **First seen / Last seen:** 2026-03-12 / 2026-05-29- **Top landing pages:** /- **Top sources:** Bing organic, Direct- **Deep visits:** None — all sessions were single-page- **Trend:** Stable, single visits across the window. Today's visit is the first in 2 weeks.- **Identified visits:** 1 session from 1 distinct identified contact (names withheld; see Find My Buyer skill)
## People on file (2,838 contacts)- **Departments:** Engineering (612), IT (487), Operations (340), HR (210)- **Seniority:** C-level: 14 · VP/Director: 87 · Manager: 412 · IC: 2,325- **Buyer Persona matches:**  - ✅ "Head of Marketing" → 6 matching contacts  - ✅ "Sales Director" → 11 matching contacts  - ✅ "Revenue Operations Manager" → 4 matching contacts  - ❌ "IT Security Lead" → 0 (gap: no contacts on file)- **Decision-maker availability:** ✅ Yes — strong senior coverage across HR, IT, Sales
## Recent signals (last 90 days)
### Hiring (13 ads)- Strategic HR Business Partner — 2026-04-22- IT Security Specialist (Cloud & Web) — 2026-04-08- SAP Frontend Engineer — 2026-05-20- HR Controlling / People Analytics Manager — 2026-05-06- Working Student Finance — 2026-05-26- +8 more across HR, Engineering, IT Security*Why it matters: hiring is concentrated in strategic HR functions, IT Security, and SAP front-end — an org rebuild that aligns with our visitor-intent / RevOps narrative.*
## Recommended next move
Active expansion signal: 13 open roles in 90 days concentrated in HR strategic functions, IT Security, and SAP front-end. Combined with full ICP fit and 2,800+ contacts on file, this is a Tier-1 outbound target. The hiring pattern suggests org-design or function-rebuild work that aligns with our visitor-intent / RevOps narrative.
- **`find-my-buyer`** — surface the Strategic HR Business Partner and Head of Marketing contacts to lead with- **`outreach-companion`** — draft a personalised opener grounded in the hiring spike + today's visit- **Add to your pipeline** — not in your CRM yet (missed-routing gap): tag it "Tier-1 Pro Services" and add it to your DACH enterprise list in Leadfeeder so it's tracked. (`add-to-pipeline` — writes tags/lists in Leadfeeder; getting it into your CRM is a separate manual step.)
Copy any of these to run the next step:
```Who should I reach out to at Acme Corp?```
```Prep me for outreach to Acme Corp```
---
**Company website:** http://www.acme.com

Keep briefs tight and actionable. The reader is deciding "do I open this company today or not." The Recommended next move section is what converts the brief into work.