parallel-findall

作者: parallel-web

發現符合自然語言描述的實體(公司、人物、產品等)。當使用者要求「找出所有X」或「列出每個Y,其…」時使用。

npx skills add https://github.com/parallel-web/parallel-cursor-plugin --skill parallel-findall

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.

User asks for…Use
"Find all X that…" / "List every Y…"parallel-findall (this skill)
Webpage results / quick answers / current infoparallel-web-search
Narrative report / analysis / "research X"parallel-deep-research
Add fields to a list you already haveparallel-data-enrichment

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.:

      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

If the parallel-cli binary is not installed

If the shell reports command not found: parallel-cli (i.e. the binary itself is missing — distinct from a No such command error from a stale CLI, which the in-body guidance above covers), stop immediately. Do NOT search the web yourself, do NOT use any built-in search tools, and do NOT try to answer the query from your own knowledge. Instead, tell the user:

  1. parallel-cli is not installed
  2. Run /parallel-setup to install it
  3. Then retry their request

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