Playbooks

Query Patterns

Query Patterns

234 queries tested against the live API across 11 batches. 190 pass (81%).

Reliability by Category

CategoryPass Rate
SalesNav search + filters96%
Profile lookup + dates100%
Typeahead resolution100%
Error handling100%
Natural user questions90%
ABM scenarios93%
Edge cases (Unicode, pagination)100%
RevOps/GTM80%
Content/feed (bot detection)0%

Sales & Outreach Patterns

Prospect list — VP Eng at fintech who posted recently:

1{ "filters": [
2  { "type": "CURRENT_TITLE", "values": [{ "text": "VP Engineering" }] },
3  { "type": "INDUSTRY", "values": [{ "text": "Financial Services" }] },
4  { "type": "REGION", "values": [{ "text": "London" }] },
5  { "type": "POSTED_ON_LINKEDIN", "values": [] }
6]}

Champion tracking — ex-customers at prospect companies:

1{ "filters": [
2  { "type": "PAST_COMPANY", "values": [{ "id": "1426", "text": "Barclays" }] },
3  { "type": "CURRENT_COMPANY", "values": [{ "text": "Revolut" }] }
4]}

High-intent leads — people who follow your company AND viewed your profile:

1{ "filters": [
2  { "type": "FOLLOWS_YOUR_COMPANY", "values": [] },
3  { "type": "VIEWED_YOUR_PROFILE", "values": [] }
4]}

ABM Patterns

Buying committee — map all decision makers at a target:

1{ "filters": [
2  { "type": "CURRENT_COMPANY", "values": [{ "text": "Stripe" }] },
3  { "type": "SENIORITY_LEVEL", "values": [{ "id": "6" }, { "id": "7" }, { "id": "8" }] }
4], "maxResults": 25 }

Whitespace analysis — find prospects excluding existing customers:

1{ "filters": [
2  { "type": "INDUSTRY", "values": [{ "text": "Banking" }] },
3  { "type": "CURRENT_TITLE", "values": [{ "text": "Chief Data Officer" }] },
4  { "type": "CURRENT_COMPANY", "values": [
5    { "id": "1426", "text": "Barclays", "selectionType": "EXCLUDED" }
6  ]}
7]}

Market Intelligence

Hiring velocity — compare joiners vs leavers:

1// Joiners: CURRENT_COMPANY + RECENTLY_CHANGED_JOBS
2// Leavers: PAST_COMPANY + RECENTLY_CHANGED_JOBS

Stealth startup detection:

1{ "filters": [
2  { "type": "COMPANY_HEADCOUNT", "values": [{ "id": "B" }] },
3  { "type": "SENIORITY_LEVEL", "values": [{ "id": "8" }] },
4  { "type": "RECENTLY_CHANGED_JOBS", "values": [] },
5  { "type": "REGION", "values": [{ "text": "London" }] },
6  { "type": "INDUSTRY", "values": [{ "text": "Technology, Information and Internet" }] }
7]}

Recruiting

Passive candidates — top-uni engineers at target company:

1{ "filters": [
2  { "type": "CURRENT_COMPANY", "values": [{ "text": "Google" }] },
3  { "type": "CURRENT_TITLE", "values": [{ "text": "Machine Learning Engineer" }] },
4  { "type": "SCHOOL", "values": [
5    { "text": "Imperial College London" },
6    { "text": "University of Cambridge" },
7    { "text": "University of Edinburgh" }
8  ]}
9]}

Boomerang hires — ex-employees on the move:

1{ "filters": [
2  { "type": "PAST_COMPANY", "values": [{ "id": "42124361", "text": "newmode.ai" }] },
3  { "type": "RECENTLY_CHANGED_JOBS", "values": [] }
4]}

Diaspora & Geographic

Greek tech in Silicon Valley (university as nationality proxy):

1{ "filters": [
2  { "type": "SCHOOL", "values": [{ "text": "National Technical University of Athens" }] },
3  { "type": "REGION", "values": [{ "text": "San Francisco Bay Area" }] }
4]}

Edge Cases Confirmed Working

  • 6 simultaneous filters → returns results
  • 10 OR values in one filter → works
  • 4 exclusion values → all excluded correctly
  • Unicode search (Greek characters) → works
  • Very long keywords (100+ chars) → works
  • Toggle-only searches → works
  • Multi-user switching (X-User-Id) → works

What You Cannot Do

  1. Sort by connection count — no ranking API
  2. Filter by graduation year — all alumni returned
  3. Filter by previous location — only current REGION
  4. AND same filter type — multiple values = OR, use keywords workaround
  5. Gender filter — not available
  6. Company age — no founding date filter
  7. Absolute dates — only “recently” (relative)