parallel-findall

FindAll: Entity Discovery

Find: $ARGUMENTS

Requires parallel-cli ≥ 0.6.0 (the findall entity-search command was added in 0.6.0; the broader findall command was added in 0.3.0). If either errors with no such command or similar, tell the user to run parallel-cli update (or pipx upgrade parallel-web-tools if installed via pipx), then retry.

When to use this skill

Use FindAll when the user wants a structured list of entities matching a description, not webpages or a narrative answer.

If the user already has a list and just wants to add fields, this is the wrong skill — use parallel-data-enrichment.

FindAll has two paths: the comprehensive, asynchronous findall run (Steps 1–2) and the fast, synchronous entity-search (final section).

• entity-search — very fast (few seconds), only supports people or company search. Supports a more limited set of query arguments. Optimized for recall over precision; results are not individually verified.
• findall run — Provides comprehensive coverage, complex, match conditions, exclusions, enrichment, citations, or a type other than people/companies.

If it's ambiguous, ask the user which they'd prefer and offer a default. Remember entity search limits: companies/people only, no exclusions/generator/enrichment, and entity_set_id can't be used with enrich/extend (re-run via findall run if needed).

Switch to entity-search only when the user explicitly signals they want a fast, throwaway list. entity-search is also strictly more limited: it only supports companies or people entity types, no exclusions, no generator choice, no enrichment, and the returned entity_set_id is not usable with findall enrich/extend. If you start there and the user later asks to enrich or extend, you'll have to re-run via findall run.

Step 1: Start the run

parallel-cli findall run "$ARGUMENTS" --no-wait --json

Defaults: generator core, match limit 10. Stick with core unless the user has a reason to escalate:

• -g pro — most thorough generator (slower, costlier). Use when the user asks for "comprehensive" coverage or matches are sparse on core
• -g base — fastest, but markedly lower quality. Often returns query-echo entities (e.g., directory pages, the literal query string), entries with no URL, or category placeholders. Only use if the user explicitly asks for a quick scan and accepts noise; otherwise prefer core
• -n 50 — return up to 50 matched entities (5–1000 allowed)

If the user wants to exclude known entities (e.g., "find competitors but not Google or OpenAI"):

parallel-cli findall run "$ARGUMENTS" --no-wait --json \
    --exclude '[{"name":"Google","url":"google.com"},{"name":"OpenAI","url":"openai.com"}]'

Tip — preview the schema first if the objective is ambiguous: parallel-cli findall ingest "$ARGUMENTS" --json shows the entity type and match conditions the API inferred, so you can refine wording before paying for a run.

Parse the JSON output to extract the findall_id and any monitoring URL. Tell the user:

• A FindAll run has been started
• Approximate cadence (minutes for core, longer for pro)
• They can keep working while it runs

Step 2: Poll for results

Choose a descriptive filename (e.g., series-a-ai-2026, charlotte-roofers). Use lowercase with hyphens, no spaces.

parallel-cli

Important:

• Use --timeout 540 (9 minutes) to stay within tool execution limits
• Do NOT pass --json for large result sets — it will flood context. -o saves the full results to disk

If the poll times out

Re-run the same parallel-cli findall poll command to continue waiting. Server-side the run continues regardless.

Response format

Before presenting matches, filter the results for obvious noise:

• Drop entries with empty/missing url
• Drop entries whose name echoes the user's query (e.g., literal "YC W25 batch companies in developer tools") — those are search-result placeholders, not real entities
• Drop entries whose url is a third-party directory or profile page rather than the entity's own domain. The URL should be something the entity itself owns (its product site, docs, or marketing site)

If filtering removes a meaningful share of matches, mention this to the user and suggest re-running with -g pro or a higher -n.

Sanity-check -g base results. The base generator can hallucinate categorical attributes (e.g., return a YC S22 company as a YC W25 match). The filter rules above only catch URL/name shape, not factual correctness. If the user's query has a falsifiable attribute (a specific batch, year, geography, etc.), spot-check the kept entries against the source URL and flag any that don't fit. Recommend re-running with -g core (or higher) if either multiple kept entries fail the spot-check or noise filtering dropped a meaningful share of the matched set (say, ≥40%) — both indicate base isn't producing reliable results for this query.

Present the remaining (real) entities as a markdown table or list. Lead with the count, then list each entity with its name, URL, and a one-line description if available. Cite each entity with its source URL.

Tell the user:

• How many entities were matched (and how many were filtered as noise, if any)
• The full results path (/tmp/$FILENAME.json)
• That they can:

  • Add fields to these results, e.g.:

    parallel-cli findall enrich $FINDALL_ID '{"properties":{"ceo":{"type":"string"},"employee_count":{"type":"number"}}}'

    The schema is a JSON Schema-style object with properties mapping field names → {type, description?}.

  • Get more matches: parallel-cli findall extend $FINDALL_ID 50

Fast entity search

Use this path only when the user explicitly signals they want a quick/rough/preview list — do not pick it just because the entity type happens to be companies or people.

Synchronous call. No polling, no findall_id. Pick a descriptive $FILENAME (lowercase, hyphens, no spaces), as in Step 2.

parallel-cli findall entity-search "$ARGUMENTS" -t companies -n 100 -o "/tmp/$FILENAME.json"

Flags:

• -t companies|people — entity type (required). The endpoint only supports these two; for anything else, use findall run
• -n 5..1000 — match limit (default 10). When possible, request more than the user needs (e.g. -n 100) and select after filtering — results are ranked but not individually verified, and a low limit can omit relevant entities
• Do NOT pass --json for large result sets — it will flood context. -o saves the full results to disk

Avoid highly restrictive objectives on this path: the API fills toward the limit, so relevance declines toward the tail. Keep the core criterion in the objective and filter the rest downstream, or use findall run.

Response shape:

{ "entity_set_id": "entity_set_…", "entities": [ {"name": "...", "url": "...", "description": "..."},
… ] }

Unlike the full path, the url returned by entity-search is usually a directory/profile link — expected, not noise. Don't drop them; only filter out entries with an empty url or a name that echoes the query.

Present the kept entities as a markdown table or list, lead with the count, and cite each with its source URL. Tell the user:

• How many entities came back (and how many were filtered as noise)
• The full results path (/tmp/$FILENAME.json) if -o was used

Setup

Requires parallel-cli (installed and authenticated). If parallel-cli --version fails, or if a later command fails with an authentication error, tell the user to see https://docs.parallel.ai/integrations/cli and stop.