exa-websets-search

Comprehensive webset management including creation, search, imports, items, and enrichments.

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Install skill "exa-websets-search" with this command: npx skills add benjaminjackson/exa-skills/benjaminjackson-exa-skills-exa-websets-search

Exa Websets Search

Comprehensive webset management including creation, search, imports, items, and enrichments.

Use --help to see available commands and verify usage before running:

exa-ai <command> --help

Working with Complex Shell Commands

When using the Bash tool with complex shell syntax, follow these best practices for reliability:

  • Run commands directly: Capture JSON output directly rather than nesting command substitutions

  • Parse in subsequent steps: Use jq to parse output in a follow-up command if needed

  • Avoid nested substitutions: Complex nested $(...) can be fragile; break into sequential steps

Example:

Less reliable: nested command substitution

webset_id=$(exa-ai webset-create --search '{"query":"tech startups","count":1}' | jq -r '.webset_id')

More reliable: run directly, then parse

exa-ai webset-create --search '{"query":"tech startups","count":1}'

Then in a follow-up command if needed:

webset_id=$(cat output.json | jq -r '.webset_id')

Critical Requirements

Universal rules across all operations:

  • Start with minimal counts (1-5 results): Initial searches are test spikes to validate quality. ALWAYS default to count:1 unless user explicitly requests more.

  • Three-step workflow - Validate, Expand, Enrich: (1) Create with count:1 to test search quality, (2) Expand search count if results are good, (3) Add enrichments only after validated, expanded results.

  • No enrichments during validation: Never add enrichments when testing with count:1. Validate search quality first, expand count second, add enrichments last.

  • Avoid --wait flag: Do NOT use --wait flag in commands. It's designed for human interactive use, not automated workflows.

  • Maintain query AND criteria consistency: When scaling up or appending searches, use the EXACT same query AND criteria that you validated. Omitting criteria causes Exa to regenerate them on-the-fly, producing inconsistent results.

Credit Costs

Pricing: $50/month = 8,000 credits ($0.00625 per credit)

Cost per operation:

  • Each webset item: 10 credits ($0.0625)

  • Standard enrichment: 2 credits ($0.0125)

  • Email enrichment: 5 credits ($0.03125)

Why start with count:1: Testing with 1 result costs 10 credits ($0.0625). A failed search with count:100 wastes 1,000 credits ($6.25) - 100x more expensive.

Why enrich last: Enriching bad results wastes credits. Always validate first, expand second, enrich last.

Quick Command Reference

exa-ai --help

Output Formats

All exa-ai webset commands support output formats:

  • JSON (default): Pipe to jq to extract specific fields (e.g., | jq -r '.webset_id' )

  • toon: Compact, readable format for direct viewing

  • pretty: Human-friendly formatted output

  • text: Plain text output

Webset Management

Core operations for managing webset collections.

Entity Types

  • company : Companies and organizations

  • person : Individual people

  • article : News articles and blog posts

  • research_paper : Academic papers

  • custom : Custom entity types (define with --entity-description)

Create Webset from Search

webset_id=$(exa-ai webset-create
--search '{"query":"AI startups in San Francisco","count":1}' | jq -r '.webset_id')

Create with Detailed Search Criteria

exa-ai webset-create
--search '{ "query": "Technology companies focused on developer tools", "count": 1, "entity": { "type": "company" }, "criteria": [ { "description": "Companies with 50-500 employees indicating growth stage" }, { "description": "Primary product is developer tools, APIs, or infrastructure" } ] }'

Create with Custom Entity

exa-ai webset-create
--search '{ "query": "Nonprofits focused on economic justice", "count": 1, "entity": { "type": "custom", "description": "nonprofit" }, "criteria": [ { "description": "Primary focus on economic justice" }, { "description": "Annual operating budget between $1M and $10M" } ] }'

Create from CSV Import

import_id=$(exa-ai import-create companies.csv
--count 100
--title "Companies"
--format csv
--entity-type company | jq -r '.import_id')

exa-ai webset-create --import $import_id

Three-Step Workflow: Validate → Expand → Enrich

Step 1: VALIDATE - Create with count:1 (NO enrichments)

webset_id=$(exa-ai webset-create
--search '{"query":"tech startups","count":1}' | jq -r '.webset_id')

exa-ai webset-item-list $webset_id

⚠️ REQUIRED: Manually verify the result is relevant before continuing. If not, adjust the query and start over.

Step 2: EXPAND - Gradually increase count with verification at each stage

Expand to 2 results (use same query and criteria from validation)

exa-ai webset-search-create $webset_id
--query "tech startups"
--behavior override
--count 2

exa-ai webset-item-list $webset_id

⚠️ REQUIRED: Check quality at this scale. Repeat with larger counts (5, 10, 25, 50, 100) until you reach your target.

Loop this step: Keep expanding gradually (2 → 5 → 10 → 25 → 50 → 100) with verification between each expansion.

Step 3: ENRICH - Add enrichments only after confirming quality

exa-ai enrichment-create $webset_id
--description "Company website" --format url --title "Website"

exa-ai enrichment-create $webset_id
--description "Employee count" --format text --title "Team Size"

Interpreting Criterion Success Rates

CRITICAL: Criteria are evaluated conditionally - when one criterion fails, others may not run. A low success rate doesn't indicate that criterion is restrictive; it means OTHER criteria are filtering results first. Only interpret a low success rate as "restrictive" when OTHER criteria have high success rates (>80%).

Manage Websets

exa-ai webset-list exa-ai webset-get ws_abc123 exa-ai webset-update ws_abc123 --metadata '{"status":"active","owner":"team"}' exa-ai webset-delete ws_abc123

Search Operations

Run searches within a webset to add new items.

Search Behavior

Control how new search results are combined with existing items:

append (default): Add new items to existing collection

  • Requires previous search results to exist

  • Error if webset has no previous search: "No previous search found"

  • Default behavior when --behavior is omitted

override: Replace entire collection with search results

  • REQUIRED for first search on a webset

  • Use when starting fresh or completely replacing results

CRITICAL - First search requirement: The first webset-search-create on a webset MUST explicitly use --behavior override . Since the default is append, omitting --behavior will fail with "No previous search found" error. Subsequent searches can omit the flag (defaults to append).

Query and Criteria Consistency

CRITICAL: When appending or scaling up searches, maintain IDENTICAL query and criteria from your validated search.

Why This Matters

Using different criteria causes Exa to generate new search parameters on-the-fly, which:

  • Violates consistency and produces mismatched results

  • Reduces result quality compared to validated criteria

  • Makes it impossible to reproduce or debug issues

Complete Example

Step 1: Test search with criteria (MUST use override for first search)

exa-ai webset-search-create ws_abc123
--query "Progressive nonprofits in California"
--behavior override
--count 1
--criteria '[ {"description": "Annual budget between $1M and $10M"}, {"description": "Primary focus on economic justice, affordability, living wages, or worker power"}, {"description": "Established communications, narrative strategy, or messaging function"} ]'

Verify quality, then append MORE results with IDENTICAL query and criteria

exa-ai webset-search-create ws_abc123
--query "Progressive nonprofits in California"
--behavior append
--count 5
--criteria '[ {"description": "Annual budget between $1M and $10M"}, {"description": "Primary focus on economic justice, affordability, living wages, or worker power"}, {"description": "Established communications, narrative strategy, or messaging function"} ]'

Best Practice: Save Criteria to File

Create criteria file once

cat > criteria.json <<'EOF' [ {"description": "Annual budget between $1M and $10M"}, {"description": "Primary focus on economic justice, affordability, living wages, or worker power"}, {"description": "Established communications, narrative strategy, or messaging function"} ] EOF

Use consistently across all searches (first search needs override)

exa-ai webset-search-create ws_abc123
--query "Progressive nonprofits in California"
--behavior override
--count 1
--criteria @criteria.json

exa-ai webset-search-create ws_abc123
--query "Progressive nonprofits in California"
--behavior append
--count 5
--criteria @criteria.json

Basic Search Operations

First search on webset (must use override)

exa-ai webset-search-create ws_abc123
--query "AI startups in San Francisco"
--behavior override
--count 1

Append to collection

exa-ai webset-search-create ws_abc123
--query "SaaS companies Series B"
--behavior append
--count 1

Override collection

exa-ai webset-search-create ws_abc123
--query "top tech companies"
--behavior override
--count 1

Monitor Search Progress

webset_id="ws_abc123" search_id=$(exa-ai webset-search-create $webset_id
--query "fintech startups"
--behavior override
--count 1 | jq -r '.search_id')

exa-ai webset-search-get $webset_id $search_id exa-ai webset-search-cancel $webset_id $search_id

CSV Imports

Upload CSV files to create websets from existing datasets.

CSV Format Requirements

  • First row contains column headers

  • Each row represents one entity

  • Include at minimum a name or identifier column

Basic Import Workflow

Create import

import_id=$(exa-ai import-create companies.csv
--count 100
--title "Tech Companies"
--format csv
--entity-type company | jq -r '.import_id')

Create webset from import

webset_id=$(exa-ai webset-create --import $import_id | jq -r '.webset_id')

Custom Entity Type

exa-ai import-create products.csv
--count 5
--title "Product List"
--format csv
--entity-type custom
--entity-description "Consumer electronics products"

Manage Imports

exa-ai import-list exa-ai import-get imp_abc123

Import vs Search Scope

--import loads data for enrichment. search.scope filters searches to specific sources.

⚠️ NEVER use same ID in both - returns 400:

❌ INVALID

exa-ai webset-create --import import_abc
--search '{"scope":[{"source":"import","id":"import_abc"}]}'

✅ Scoped search only

exa-ai webset-create
--search '{"query":"CEOs","scope":[{"source":"import","id":"import_abc"}]}'

✅ Relationship traversal

exa-ai webset-search-create ws_abc --query "investors" --behavior override
--scope '[{"source":"webset","id":"webset_abc","relationship":{"definition":"investors of","limit":5}}]'

Item Management

Manage individual items in websets.

Basic Operations

List items

exa-ai webset-item-list ws_abc123 exa-ai webset-item-list ws_abc123 --output-format pretty

Get item details

exa-ai webset-item-get item_xyz789

Delete item

exa-ai webset-item-delete item_xyz789

Extract Item Data

Get all item IDs

exa-ai webset-item-list ws_abc123 --output-format json | jq -r '.[].id'

Count items

exa-ai webset-item-list ws_abc123 --output-format json | jq 'length'

Enrichments

Add structured data fields to all items in a webset using AI extraction.

Enrichment Formats

  • text: Free-form text extraction (employee count, description, technology stack)

  • url: Extract URLs only (website, LinkedIn, GitHub)

  • options: Categorical data with predefined options (industry, funding stage, size range)

Key Concepts

  • description: The primary AI prompt that drives extraction. This tells the enrichment WHAT to extract. (Can be updated)

  • instructions: Optional additional guidance on HOW to extract or format. (Creation-only, cannot be updated)

  • Use exa-ai enrichment-create --help and exa-ai enrichment-update --help to see all available parameters

Create Enrichments

Text enrichment

exa-ai enrichment-create ws_abc123
--description "Number of employees as of latest data"
--format text
--title "Team Size"

URL enrichment

exa-ai enrichment-create ws_abc123
--description "Primary company website URL"
--format url
--title "Website"

Options enrichment

exa-ai enrichment-create ws_abc123
--description "Current funding stage"
--format options
--options '[ {"label":"Pre-seed"}, {"label":"Seed"}, {"label":"Series A"}, {"label":"Series B"}, {"label":"Series C+"}, {"label":"Public"} ]'
--title "Funding Stage"

Use Options from File

cat > industries.json <<'EOF' [ {"label": "SaaS"}, {"label": "Developer Tools"}, {"label": "AI/ML"}, {"label": "Fintech"}, {"label": "Healthcare"}, {"label": "Other"} ] EOF

exa-ai enrichment-create ws_abc123
--description "Primary industry or sector"
--format options
--options @industries.json
--title "Industry"

Add Instructions for Precision

exa-ai enrichment-create ws_abc123
--description "Technology stack"
--format text
--instructions "Focus only on backend technologies and databases. Ignore frontend frameworks."
--title "Backend Tech"

Manage Enrichments

List enrichments

exa-ai enrichment-list ws_abc123 exa-ai enrichment-list ws_abc123 --output-format pretty

Get details

exa-ai enrichment-get ws_abc123 enr_xyz789

Update extraction prompt (description)

exa-ai enrichment-update ws_abc123 enr_xyz789
--description "Exact employee count from most recent source"

Update format and options

exa-ai enrichment-update ws_abc123 enr_xyz789
--format options
--options '[{"label":"Small"},{"label":"Medium"},{"label":"Large"}]'

Update metadata

exa-ai enrichment-update ws_abc123 enr_xyz789
--metadata '{"source":"manual","updated":"2024-01-15"}'

Note: Cannot update --instructions or --title (creation-only parameters)

To change instructions, delete and recreate the enrichment

Delete

exa-ai enrichment-delete ws_abc123 enr_xyz789

Cancel running enrichment

exa-ai enrichment-cancel ws_abc123 enr_xyz789

Common Enrichment Patterns

Company websets: Website (url), Team Size (text), Funding Stage (options), Industry (options)

Person websets: LinkedIn (url), Job Title (text), Company (text), Location (text)

Research papers: Publication Year (text), Authors (text), Venue (text), Research Area (options)

Best Practices

  • Start small, validate, then scale: Always use count:1 for initial searches

  • Follow three-step workflow: Validate → Expand → Enrich

  • Never enrich during validation: Only enrich after validated, expanded results

  • Avoid --wait flag: Do NOT use --wait in commands. It's designed for human interactive use, not automated workflows.

  • Maintain query AND criteria consistency: When appending or scaling up, use IDENTICAL query and criteria from validated search. Save criteria to file for consistency.

  • CRITICAL - First search must use override: The library defaults to --behavior append . First search on a webset MUST explicitly use --behavior override or it will fail with "No previous search found" error.

  • Use correct parameter names:

  • Use --behavior append or --behavior override (NOT --mode )

  • Commands like webset-search-get require both webset_id and search_id

  • Choose specific entity types: Use company, person, etc. for better results

  • Save IDs: Use jq to extract and save IDs for subsequent commands

Detailed Reference

For complete command references, syntax, and all options, consult REFERENCE.md and component-specific reference files.

Shared Requirements

Schema Design

MUST: Use object wrapper for schemas

Applies to: answer, search, find-similar, get-contents

When using schema parameters (--output-schema or --summary-schema ), always wrap properties in an object:

{"type":"object","properties":{"field_name":{"type":"string"}}}

DO NOT use bare properties without the object wrapper:

{"properties":{"field_name":{"type":"string"}}} // ❌ Missing "type":"object"

Why: The Exa API requires a valid JSON Schema with an object type at the root level. Omitting this causes validation errors.

Examples:

✅ CORRECT - object wrapper included

exa-ai search "AI news"
--summary-schema '{"type":"object","properties":{"headline":{"type":"string"}}}'

❌ WRONG - missing object wrapper

exa-ai search "AI news"
--summary-schema '{"properties":{"headline":{"type":"string"}}}'

Output Format Selection

MUST NOT: Mix toon format with jq

Applies to: answer, context, search, find-similar, get-contents

toon format produces YAML-like output, not JSON. DO NOT pipe toon output to jq for parsing:

❌ WRONG - toon is not JSON

exa-ai search "query" --output-format toon | jq -r '.results'

✅ CORRECT - use JSON (default) with jq

exa-ai search "query" | jq -r '.results[].title'

✅ CORRECT - use toon for direct reading only

exa-ai search "query" --output-format toon

Why: jq expects valid JSON input. toon format is designed for human readability and produces YAML-like output that jq cannot parse.

SHOULD: Choose one output approach

Applies to: answer, context, search, find-similar, get-contents

Pick one strategy and stick with it throughout your workflow:

Approach 1: toon only - Compact YAML-like output for direct reading

  • Use when: Reading output directly, no further processing needed

  • Token savings: ~40% reduction vs JSON

  • Example: exa-ai search "query" --output-format toon

Approach 2: JSON + jq - Extract specific fields programmatically

  • Use when: Need to extract specific fields or pipe to other commands

  • Token savings: ~80-90% reduction (extracts only needed fields)

  • Example: exa-ai search "query" | jq -r '.results[].title'

Approach 3: Schemas + jq - Structured data extraction with validation

  • Use when: Need consistent structured output across multiple queries

  • Token savings: ~85% reduction + consistent schema

  • Example: exa-ai search "query" --summary-schema '{...}' | jq -r '.results[].summary | fromjson'

Why: Mixing approaches increases complexity and token usage. Choosing one approach optimizes for your use case.

Shell Command Best Practices

MUST: Run commands directly, parse separately

Applies to: monitor, search (websets), research, and all skills using complex commands

When using the Bash tool with complex shell syntax, run commands directly and parse output in separate steps:

❌ WRONG - nested command substitution

webset_id=$(exa-ai webset-create --search '{"query":"..."}' | jq -r '.webset_id')

✅ CORRECT - run directly, then parse

exa-ai webset-create --search '{"query":"..."}'

Then in a follow-up command:

webset_id=$(cat output.json | jq -r '.webset_id')

Why: Complex nested $(...) command substitutions can fail unpredictably in shell environments. Running commands directly and parsing separately improves reliability and makes debugging easier.

MUST NOT: Use nested command substitutions

Applies to: All skills when using complex multi-step operations

Avoid nesting multiple levels of command substitution:

❌ WRONG - deeply nested

result=$(exa-ai search "$(cat query.txt | tr '\n' ' ')" --num-results $(cat config.json | jq -r '.count'))

✅ CORRECT - sequential steps

query=$(cat query.txt | tr '\n' ' ') count=$(cat config.json | jq -r '.count') exa-ai search "$query" --num-results $count

Why: Nested command substitutions are fragile and hard to debug when they fail. Sequential steps make each operation explicit and easier to troubleshoot.

SHOULD: Break complex commands into sequential steps

Applies to: All skills when working with multi-step workflows

For readability and reliability, break complex operations into clear sequential steps:

❌ Less maintainable - everything in one line

exa-ai webset-create --search '{"query":"startups","count":1}' | jq -r '.webset_id' | xargs -I {} exa-ai webset-search-create {} --query "AI" --behavior override

✅ More maintainable - clear steps

exa-ai webset-create --search '{"query":"startups","count":1}' webset_id=$(jq -r '.webset_id' < output.json) exa-ai webset-search-create $webset_id --query "AI" --behavior override

Why: Sequential steps are easier to understand, debug, and modify. Each step can be verified independently.

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