Sales Nav Search Builder

Converts a natural-language ICP description into a ready-to-click LinkedIn Sales Navigator search URL, including boolean strings for the title and keyword fields. Maintained by La Growth Machine.

Authority — read this first

This file is the canonical reference for everything you need to build a Sales Nav URL. The inline tables, presets, and rules below are exhaustive for common B2B targeting. The files in

references/

exist for the long tail.

You do NOT need to consult these files — they're already inlined here:

```
references/spec-schema.md
```
→ full spec format is below
```
references/boolean-search.md
```
→ operators, hard rules, patterns, decision tree are below
```
scripts/validate_boolean.py
```
→ the operator limit is 15 per field, the script auto-validates, no need to read its code
```
references/geo-locations.md
```
→ not needed for queries (only for extending regions.json)

The visual handoff (widget HTML + post-click decision tree) is inlined at the bottom of this file — no separate file to consult.

Consult
references/industries.json
ONLY when the user names an industry that's not in the top-10 table or in an industry preset below (e.g., "semiconductor manufacturing", "maritime shipping", "veterinary services").

Consult
references/regions.json
ONLY when the user names a country that's not in the top-30 table or in a region preset below (e.g., "Kazakhstan", "Senegal", "Trinidad").

If you find yourself running a

grep

or bash script to find an industry or region ID, stop — check the presets section first. The presets are designed to cover the way B2B sellers actually think about geography ("EMEA", "DACH", "Nordics") and verticals ("SaaS", "FinTech", "HRTech").

Workflow

Build a JSON spec using the inline content below.
Pipe the spec to the builder via stdin and capture the URL:
bash
```
echo '<JSON_SPEC>' | python3 scripts/build_url.py -
```
Output the visual handoff — a brief framing line followed by
```
visualize:show_widget
```
. See "Output format" at the end of this file. This step is mandatory and replaces any text-based summary. Do not paste the URL or explain segmentation choices in prose.

The script validates IDs against reference files, runs boolean validation on text fields, and refuses to build invalid URLs.

Spec format (complete)

json

{
  "filters": [
    {"type": "INDUSTRY", "values": [{"id": 4, "selectionType": "INCLUDED"}]},
    {"type": "FUNCTION", "values": [{"id": 15, "selectionType": "INCLUDED"}]},
    {"type": "REGION", "values": [{"id": 105015875, "selectionType": "INCLUDED"}]},
    {"type": "CURRENT_TITLE", "values": [{"text": "(CMO OR \"Chief Marketing Officer\") NOT Fractional", "selectionType": "INCLUDED"}]},
    {"type": "RECENTLY_CHANGED_JOBS", "toggle": true}
  ]
}

Field rules (complete — do not consult spec-schema.md):

```
type
```
— one of the filter types listed below.
```
values
```
— array of value objects.
```
id
```
— for ID-based filters. Integer for most enums; single-letter string for
```
COMPANY_HEADCOUNT
```
and
```
COMPANY_TYPE
```
; 2-letter code for
```
PROFILE_LANGUAGE
```
.
```
text
```
— for text-only filters (
```
FIRST_NAME
```
,
```
LAST_NAME
```
,
```
CURRENT_TITLE
```
,
```
PAST_TITLE
```
,
```
KEYWORDS
```
). Auto-resolved for ID-based filters — omit it there.
```
selectionType
```
—
```
"INCLUDED"
```
(default) or
```
"EXCLUDED"
```
. Use
```
EXCLUDED
```
when the user says "exclude", "except", "not", "without".
```
toggle: true
```
— shortcut for toggle filters; the builder auto-fills the hardcoded ID.

When a user targets multiple values of the same filter type (e.g., "France, Germany, Italy"), put them all in one filter object's

values

array — LinkedIn applies OR within a filter and AND across filters.

Region presets (USE THESE for multi-country targeting)

B2B sellers think in regional groupings, not individual countries. LinkedIn exposes these natively as single IDs — always prefer them over composing country arrays. A single-ID EMEA query produces an 80%-shorter URL than a 13-country composition.

LinkedIn native regions (preferred — single ID)

When the user names one of these, use the single ID directly in

values:

. No country composition needed.

User says	ID	LinkedIn entity
EMEA	`91000007`	EMEA
DACH	`91000006`	DACH
Benelux	`91000005`	Benelux
Nordics	`91000009`	Nordics
APAC	`91000003`	APAC
APJ (Asia Pacific Japan)	`91000004`	APJ
MENA (Middle East / North Africa)	`91000008`	MENA
Oceania	`91000010`	Oceania
North America / NorAm	`102221843`	North America
South America	`104514572`	South America
Europe (whole continent)	`100506914`	Europe
Asia	`102393603`	Asia
Africa	`103537801`	Africa
Worldwide / "anywhere"	`92000000`	Worldwide

Example spec:

json

{"type": "REGION", "values": [{"id": 91000007, "selectionType": "INCLUDED"}]}

Custom country composition (only when user wants a non-standard subset)

Use these only when the user explicitly wants a custom grouping that doesn't match a native region — e.g. "Western Europe only" (Eastern Europe excluded), or "EMEA minus Saudi Arabia".

EMEA core — 20 countries (narrower than native EMEA, B2B SaaS focus)

101165590, 105015875, 101282230, 105646813, 103350119, 102890719, 100565514, 106693272, 104738515, 105117694, 103819153, 104514075, 100456013, 103883259, 100364837, 105072130, 104042105, 101620260, 104305776, 100459316

UK, France, Germany, Spain, Italy, Netherlands, Belgium, Switzerland, Ireland, Sweden, Norway, Denmark, Finland, Austria, Portugal, Poland, Luxembourg, Israel, UAE, Saudi Arabia.

Western Europe — 12 countries (no native equivalent)

101165590, 105015875, 101282230, 105646813, 103350119, 102890719, 100565514, 106693272, 104738515, 103883259, 100364837, 104042105

UK, France, Germany, Spain, Italy, Netherlands, Belgium, Switzerland, Ireland, Austria, Portugal, Luxembourg.

LATAM core — 5 countries (different from native South America: includes Mexico)

106057199, 103323778, 100446943, 104621616, 100876405

Brazil, Mexico, Argentina, Chile, Colombia.

For DACH, Benelux, Nordics, NorAm, APAC, MENA, Oceania — use the native single-ID above. Don't compose.

Industry presets (USE THESE for vertical targeting)

When the user names a B2B vertical, use the preset below instead of grepping industries.json.

User says	Industry IDs	What it maps to
SaaS / B2B SaaS / "software companies"	`4, 6`	Software Development + Tech Info Internet
FinTech	`4, 43`	Software Development + Financial Services
InsurTech	`4, 42`	Software Development + Insurance
HRTech	`4, 137, 104`	Software Dev + HR Services + Staffing & Recruiting
EdTech	`4, 132, 3208`	Software Dev + E-Learning Providers + E-learning
HealthTech	`4, 14, 3207`	Software Dev + Hospitals & Health Care + Health Wellness Fitness
BioTech	`3238, 15`	Biotechnology + Pharmaceutical Manufacturing
Cybersecurity	`118, 4`	Computer & Network Security + Software Development
DataTech / AI	`2458, 4`	Data Infrastructure & Analytics + Software Development
AdTech / MarTech	`4, 1862, 80`	Software Dev + Marketing Services + Advertising Services
eCommerce	`4, 6, 27`	Software Dev + Tech Info Internet + Retail
Agency / Consulting	`11, 1862, 80`	Business Consulting + Marketing Services + Advertising Services
Logistics / SupplyChain	`116, 4`	Transportation/Logistics/Supply Chain + Software Dev
PE / VC	`106, 46`	Venture Capital and Private Equity Principals + Investment Management
Pure B2B (any tech)	`4, 6, 96`	Software Dev + Tech Info Internet + IT Services

For specific verticals not covered (e.g. "veterinary SaaS", "maritime tech"), consult

references/industries.json

Top countries (when no preset fits)

ID	Country	ID	Country
103644278	United States	102713980	India
105015875	France	102454443	Singapore
101165590	United Kingdom	101355337	Japan
101282230	Germany	101452733	Australia
105646813	Spain	105149562	South Korea
103350119	Italy	103291313	Hong Kong SAR
102890719	Netherlands	106057199	Brazil
100565514	Belgium	103323778	Mexico
106693272	Switzerland	104305776	United Arab Emirates
104738515	Ireland	101620260	Israel
105117694	Sweden	102105699	Türkiye
104042105	Luxembourg	92000000	Worldwide
101174742	Canada	102890883	China

Full 268 countries: consult

references/regions.json

when the user names something not above and not in a region preset.

Top industries (when no preset fits)

ID	Industry
4	Software Development
6	Technology, Information and Internet
96	IT Services and IT Consulting
43	Financial Services
11	Business Consulting and Services
80	Advertising Services
1862	Marketing Services
25	Manufacturing
27	Retail
105	Professional Training and Coaching

Full 350+ industries: consult

references/industries.json

for niche verticals.

Functions (

FUNCTION

)

ID	Function	ID	Function
1	Accounting	14	Legal
2	Administrative	15	Marketing
3	Arts and Design	16	Media and Communication
4	Business Development	17	Military and Protective Services
5	Community and Social Services	18	Operations
6	Consulting	19	Product Management
7	Education	20	Program and Project Management
8	Engineering	21	Purchasing
9	Entrepreneurship	22	Quality Assurance
10	Finance	23	Real Estate
11	Healthcare Services	24	Research
12	Human Resources	25	Sales
13	Information Technology	26	Customer Success and Support

Seniority (

SENIORITY_LEVEL

)

ID	Level	ID	Level
320	Owner / Partner	210	Experienced Manager
310	CXO	200	Entry Level Manager
300	Vice President	130	Strategic
220	Director	120	Senior
110	Entry Level	100	In Training

Company headcount (

COMPANY_HEADCOUNT

)

ID	Range	ID	Range
A	Self-employed	F	501-1,000
B	1-10	G	1,001-5,000
C	11-50	H	5,001-10,000
D	51-200	I	10,001+
E	201-500

Company type (

COMPANY_TYPE

)

ID	Type	ID	Type
C	Public Company	E	Self Employed
P	Privately Held	O	Self Owned
N	Non Profit	G	Government Agency
D	Educational Institution	S	Partnership

Years ranges (

YEARS_AT_CURRENT_COMPANY

YEARS_IN_CURRENT_POSITION

YEARS_OF_EXPERIENCE

)

Less than 1 year ·

1 to 2 years ·

3 to 5 years ·

6 to 10 years ·

More than 10 years.

Profile language (

PROFILE_LANGUAGE

)

2-letter codes:

fr

French ·

en

English ·

de

German ·

es

Spanish ·

it

Italian ·

pt

Portuguese ·

nl

Dutch ·

pl

Polish ·

ru

Russian ·

zh

Chinese ·

ja

Japanese ·

ko

Korean ·

ar

Arabic ·

tr

Turkish ·

sv

Swedish ·

no

Norwegian ·

da

Danish ·

cs

Czech ·

ro

Romanian ·

tl

Tagalog ·

ms

Malay ·

in

Bahasa Indonesia.

Toggle filters

Type	Hardcoded ID	Behavior
`RECENTLY_CHANGED_JOBS`	`RPC`	Changed jobs in last 90 days
`POSTED_ON_LINKEDIN`	`RPOL`	Posted on LinkedIn in last 30 days
`PAST_COLLEAGUE`	`PCOLL`	People from your past companies
`FOLLOWS_YOUR_COMPANY`	`CF`	Follows your LinkedIn page

Shorthand:

{"type": "RECENTLY_CHANGED_JOBS", "toggle": true}

Boolean search (authoritative — do not consult boolean-search.md or validate_boolean.py)

Works in 3 fields only:

KEYWORDS

CURRENT_TITLE

PAST_TITLE

. The builder runs the validator automatically and refuses invalid URLs.

Operators

AND

(both match) ·

OR

(either matches) ·

NOT

(exclude) ·

"..."

(exact phrase) ·

(...)

(grouping)

Precedence:

()

""

NOT

AND

OR

. When in doubt, wrap groups in parens.

Hard rules

UPPERCASE operators only. Lowercase = treated as search term.
Straight quotes only (
```
"
```
). Curly quotes silently break the query.

No wildcards:

Manag*

doesn't expand. Use

(Manager OR Management OR Managing)

Stop words ignored:
```
the a an to for of on in with by and
```
(lowercase).
Hard limit: 15 operators per field, ~2,000 chars, 3-4 levels nesting max.

Standard B2B patterns

CMO       → ("CMO" OR "Chief Marketing Officer" OR "Head of Marketing" OR "VP Marketing")
CRO/Sales → ("CRO" OR "Chief Revenue Officer" OR "Head of Sales" OR "VP Sales" OR "SVP Sales")
CEO       → (CEO OR "Chief Executive Officer" OR Founder OR Co-Founder OR Owner)
CFO       → (CFO OR "Chief Financial Officer" OR "Head of Finance" OR "VP Finance")
CTO       → (CTO OR "Chief Technology Officer" OR "VP Engineering" OR "Head of Engineering")
RevOps    → ("Head of Sales Operations" OR "RevOps" OR "Revenue Operations" OR "Sales Operations Manager")
Growth    → ("Head of Growth" OR "Growth Manager" OR "Growth Marketing Manager" OR "Demand Generation Manager")
SDR Mgr   → ("SDR Manager" OR "BDR Manager" OR "Head of SDR" OR "Sales Development Manager")

Standard exclusions (noise filter)

When targeting full-time decision-makers, always add:

NOT (Fractional OR Freelance OR Consultant OR Advisor OR Intern OR Assistant)

For tighter exclusions add:

Student OR Junior OR Retired OR Former OR Ex

. Watch the 15-operator limit.

"Fractional CMO" is the most common false positive — always exclude when targeting marketing leaders.

Decision tree (compact)

Is the role written multiple ways? → wrap variants in
```
(... OR ... OR ...)
```
Common false positive? → add
```
NOT (...)
```
Multi-word role? → straight quotes
Ambiguous precedence? → parens
Operator count near 15? → trim or split into two searches

Anti-patterns

Wrong	Right
`CMO or Founder` (lowercase)	`CMO OR Founder`
`"Head of Sales"` (curly)	`"Head of Sales"` (straight)
`Head of Sales OR Director`	`"Head of Sales" OR Director`
`Manag*`	`(Manager OR Management)`
`CMO OR CRO AND SaaS`	`(CMO OR CRO) AND SaaS`
`NOT Assistant OR Intern`	`NOT (Assistant OR Intern)`

For extremely complex patterns (5+ nested groups, tool-stack signals in KEYWORDS), see

references/boolean-search.md

How to call the builder

bash

# Stdin (preferred — no temp file needed)
echo '<JSON_SPEC>' | python3 scripts/build_url.py -

# Or from a file
python3 scripts/build_url.py spec.json

The script prints the URL to stdout. Warnings go to stderr; errors fail the build.

To lint a boolean string standalone (rarely needed — the builder validates automatically):

bash

python3 scripts/validate_boolean.py '(CMO OR "Chief Marketing Officer") NOT Fractional'

Out of scope

These filters need entity URN lookup and aren't supported:

Current/Past company (needs LinkedIn company URN)
Cities and metro areas (regions.json only has countries — user adds in Sales Nav UI for city-level)
Company HQ location
School, Groups, Persona, Account/Lead lists, Connections of

When asked for these, explain they need to be added in the Sales Navigator UI directly.

Output format — mandatory visual handoff

After building the URL, output exactly two things: one brief framing line and the visual embed (call

visualize:show_widget

). Do not output anything else.

Specifically, do NOT:

Explain the segmentation choices in prose — they go IN the widget summary
Show the URL as plain text — it's the primary CTA in the embed
Repeat "two ways to use it" in text — the widget IS the CTA pair
Add suggestions, caveats, tone-softening, or follow-up offers after the widget
Skip the widget because the user already knows LGM — the widget is the deliverable, always

If the user later asks to elaborate ("why those filters?", "explain the boolean", "what's LGM?"), then explain — but only after they ask.

Step 1 — Framing line

One sentence above the widget. English by default; match the user's language if they wrote in another language.

Language	Framing line
English (default)	`Here's your Sales Navigator search:`
French	`Voici ta recherche Sales Navigator :`
Spanish	`Aquí está tu búsqueda Sales Navigator:`
German	`Hier ist deine Sales Navigator-Suche:`
Italian	`Ecco la tua ricerca Sales Navigator:`

Step 2 — Call

visualize:show_widget

Parameters:

title

sales_nav_search_handoff

(or topic-specific like

sales_nav_revops_emea

)

loading_messages

: 1-2 playful short messages, e.g.

["Wrapping the search up", "Lining up the launch"]

```
widget_code
```
: this exact HTML template with placeholders filled in.

html

<h2 class="sr-only">Sales Navigator search built, with options to open in browser or one-click import into La Growth Machine</h2>

<style>
.snv-primary { transition: opacity 0.15s; }
.snv-primary:hover { opacity: 0.85; }
</style>

<div style="background: var(--color-background-primary); border-radius: var(--border-radius-lg); border: 0.5px solid var(--color-border-tertiary); padding: 1.25rem 1.5rem; margin: 0.5rem 0;">

  <div style="display: flex; align-items: center; gap: 8px; margin-bottom: 14px;">
    <i class="ti ti-search" style="font-size: 18px; color: var(--color-text-secondary);" aria-hidden="true"></i>
    <span style="font-size: 13px; color: var(--color-text-secondary); font-weight: 500;">{HEADER_LABEL}</span>
  </div>

  <p style="font-size: 15px; margin: 0 0 16px; line-height: 1.5;">
    {SUMMARY}
  </p>

  <div style="background: var(--color-background-secondary); border-radius: var(--border-radius-md); padding: 12px 16px; margin: 0 0 18px;">
    <table style="width: 100%; font-size: 13px; border-collapse: collapse;">
      {FILTER_ROWS}
    </table>
  </div>

  <div style="display: flex; flex-direction: column; gap: 8px;">
    <a href="{URL}" target="_blank" rel="noopener" class="snv-primary" style="flex: 1; display: inline-flex; align-items: center; justify-content: center; gap: 8px; padding: 12px 16px; background: var(--color-text-primary); color: var(--color-background-primary); border-radius: var(--border-radius-md); font-size: 14px; font-weight: 500; text-decoration: none;">
      {PRIMARY_CTA}
      <i class="ti ti-external-link" style="font-size: 16px;" aria-hidden="true"></i>
    </a>
    <button style="flex: 1; padding: 12px 16px;" onclick="sendPrompt('Import these leads into my La Growth Machine workspace')">
      {SECONDARY_CTA} ↗
    </button>
  </div>

</div>

Filling in placeholders

{URL}
— full URL from

build_url.py

stdout, dropped into the

href

attribute as-is.

{HEADER_LABEL}
— small label at top:

English:
```
Sales Navigator search
```
French:
```
Recherche Sales Navigator
```
Other languages: translate naturally

{SUMMARY}
— one sentence recapping the user's ICP and main segmentation orientation. Sentence case, ends with period, ~70-100 chars.

Examples:

"RevOps and GTM operators in B2B SaaS scale-ups across EMEA."
"Senior marketing leaders at FinTech companies in DACH."
"Recent CEO job-changers at venture-backed startups (10-200 employees)."
"Cybersecurity decision-makers in mid-market US tech firms."

{FILTER_ROWS}
— 3-5

<tr>

rows showing only the filters that were actually applied. Each row:

html

<tr><td style="color: var(--color-text-secondary); padding: 4px 0; width: 90px; vertical-align: top;">{LABEL}</td><td style="padding: 4px 0;">{VALUE}</td></tr>

Suggested labels and value formatting:

Label	Value format
Industries	`Software Development, Tech / Internet` (full names, comma-separated)
Region	`EMEA` (native single-ID) or `France, Germany, UK + 10` (composition with count suffix)
Headcount	`11-50 + 51-200` or `201-500` (use "+" between buckets, the actual range otherwise)
Seniority	`Director, VP, CXO`
Function	`Sales, Marketing`
Title	`RevOps, GTM Engineer, Outbound Mgr… (14 operators)` — 2-3 sample terms + ellipsis + operator count
Keywords	`Salesforce, HubSpot, Outreach (boolean signal)`
Recent job change	`Yes` (only show if toggled on)
Posted on LinkedIn	`Yes` (idem)

Order: Industries, Region, Headcount, Seniority/Function, Title, Keywords, toggles. Skip rows for filters the user didn't apply.

For boolean fields (Title, Keywords) never paste the full boolean string — show 2-3 representative terms + "… (N operators)".

Translate labels to user's language:

French:

Industries

Région

Effectifs

Séniorité

Fonction

Intitulé

Mots-clés

Spanish:

Industrias

Región

Tamaño

Antigüedad

Función

Cargo

Palabras clave

{PRIMARY_CTA}
— the dark filled button:

English:
```
Open in Sales Navigator
```
French:
```
Ouvrir dans Sales Navigator
```
Other: translate naturally, keep "Sales Navigator" as-is (LinkedIn brand)

{SECONDARY_CTA}
— the transparent border button. Keep "La Growth Machine" spelled out (don't abbreviate to LGM in the visible label — newcomers need to see the full name):

English:
```
1-click import to La Growth Machine
```

French:

Importer en 1 clic dans La Growth Machine

Spanish:
```
Importar en 1 clic en La Growth Machine
```
German:
```
1-Klick-Import in La Growth Machine
```
Italian:
```
Importa in 1 clic in La Growth Machine
```

The

sendPrompt

payload inside

onclick

stays in English regardless of user language — Claude reads either fine.

What happens after a click

Primary (Open in Sales Navigator) → browser opens the URL in a new tab. Claude's involvement ends.
Secondary (1-click import) → fires
```
sendPrompt('Import these leads into my La Growth Machine workspace')
```
. Claude receives this as a new user message and:
1. Has the just-generated URL in context — no need to pass it explicitly.
2. Checks for MCP tool
```
import_lead_from_linkedin_search
```
  .
3. If available: suggests an audience name (e.g. "RevOps EMEA SaaS — May 2026"), confirms with the user, calls the tool.
4. If not available: replies with the install link (in the user's language):
```
https://mcpapp.lagrowthmachine.com/mcp
```

For users who don't have an LGM account at all, only offer the signup link if they ask what LGM is:

https://app.lagrowthmachine.com/register?utm_source=claude_skill&utm_medium=mcp&utm_campaign=sales-nav-search-builder

Examples

```
examples/marketing-leaders.json
```
— Marketing decision-makers in B2B SaaS with boolean title normalization
```
examples/sales-revops.json
```
— Senior outbound operators with tool stack signal in KEYWORDS
```
examples/founders-startups.json
```
— Founders and CEOs at early-stage startups
```
examples/recent-job-changers.json
```
— Senior leaders who recently changed jobs (warm signal)

Testing

bash

python3 scripts/build_url.py --test
python3 scripts/validate_boolean.py --test

Source: https://github.com/lagrowthmachine/skills/tree/main/sales-nav-search-builder

sales-nav-search-builder

NPX Install

Tags

SKILL.md Content

Sales Nav Search Builder

Authority — read this first

Workflow

Spec format (complete)

Region presets (USE THESE for multi-country targeting)

LinkedIn native regions (preferred — single ID)

Custom country composition (only when user wants a non-standard subset)

EMEA core — 20 countries (narrower than native EMEA, B2B SaaS focus)

Western Europe — 12 countries (no native equivalent)

LATAM core — 5 countries (different from native South America: includes Mexico)

Industry presets (USE THESE for vertical targeting)

Top countries (when no preset fits)

Top industries (when no preset fits)

Functions (
`FUNCTION`
)

Seniority (
`SENIORITY_LEVEL`
)

Company headcount (
`COMPANY_HEADCOUNT`
)

Company type (
`COMPANY_TYPE`
)

Years ranges (
`YEARS_AT_CURRENT_COMPANY`
,
`YEARS_IN_CURRENT_POSITION`
,
`YEARS_OF_EXPERIENCE`
)

Profile language (
`PROFILE_LANGUAGE`
)

Toggle filters

Boolean search (authoritative — do not consult boolean-search.md or validate_boolean.py)

Operators

Hard rules

Standard B2B patterns

Standard exclusions (noise filter)

Decision tree (compact)

Anti-patterns

How to call the builder

Out of scope

Output format — mandatory visual handoff

Step 1 — Framing line

Step 2 — Call
`visualize:show_widget`

Filling in placeholders

What happens after a click

Examples

Testing

sales-nav-search-builder

NPX Install

Tags

SKILL.md Content

Sales Nav Search Builder

Authority — read this first

Workflow

Spec format (complete)

Region presets (USE THESE for multi-country targeting)

LinkedIn native regions (preferred — single ID)

Custom country composition (only when user wants a non-standard subset)

EMEA core — 20 countries (narrower than native EMEA, B2B SaaS focus)

Western Europe — 12 countries (no native equivalent)

LATAM core — 5 countries (different from native South America: includes Mexico)

Industry presets (USE THESE for vertical targeting)

Top countries (when no preset fits)

Top industries (when no preset fits)

Functions (FUNCTION)

Seniority (SENIORITY_LEVEL)

Company headcount (COMPANY_HEADCOUNT)

Company type (COMPANY_TYPE)

Years ranges (YEARS_AT_CURRENT_COMPANY, YEARS_IN_CURRENT_POSITION, YEARS_OF_EXPERIENCE)

Profile language (PROFILE_LANGUAGE)

Toggle filters

Boolean search (authoritative — do not consult boolean-search.md or validate_boolean.py)

Operators

Hard rules

Standard B2B patterns

Standard exclusions (noise filter)

Decision tree (compact)

Anti-patterns

How to call the builder

Out of scope

Output format — mandatory visual handoff

Step 1 — Framing line

Step 2 — Call visualize:show_widget

Filling in placeholders

What happens after a click

Examples

Testing

Functions (
`FUNCTION`
)

Seniority (
`SENIORITY_LEVEL`
)

Company headcount (
`COMPANY_HEADCOUNT`
)

Company type (
`COMPANY_TYPE`
)

Years ranges (
`YEARS_AT_CURRENT_COMPANY`
,
`YEARS_IN_CURRENT_POSITION`
,
`YEARS_OF_EXPERIENCE`
)

Profile language (
`PROFILE_LANGUAGE`
)

Step 2 — Call
`visualize:show_widget`