version: "1.0.1" name: parallel-findall description: "Discover entities (companies, people, products, etc.) matching a natural-language description. Use when the user asks to 'find all X' or 'list every Y that…' — e.g., 'Find AI startups that raised Series A in 2026', 'List roofing companies in Charlotte NC', 'Show me YC W24 dev tools companies'. Different from web-search (which returns webpages) and deep-research (which returns a narrative report). Use this when the user wants a structured list of entities." user-invocable: true argument-hint: <objective describing entities to find> compatibility: Requires parallel-cli >= 0.3.0 and internet access. allowed-tools: Bash(parallel-cli:*) metadata: author: parallel

FindAll: Entity Discovery

Find: $ARGUMENTS

Requires parallel-cli ≥ 0.3.0 (the findall command was added in 0.3.0). If parallel-cli findall 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.

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 findall poll "$FINDALL_ID" -o "/tmp/$FILENAME.json" --timeout 540

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. Concretely: drop URLs on linkedin.com, ycombinator.com/companies/..., crunchbase.com, pitchbook.com, generic news/blog posts about the entity, etc. 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.:

``bash 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

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.