linkedin-growth-import

Use the linkedin-growth skill to run a Phase A import. Arguments: $ARGUMENTS

Drive the full import flow:

1. Determine the inputs. If I gave a search URL, detect the type (URL containing /sales/ → nv, otherwise st). If I gave filters instead, use them. Ask me for anything missing: which account should run the search (--searcher), and a list name (--list). If I have more than one account, confirm the searcher with me.
2. Always ask me the limit — how many of the found leads to take. I can give a number or say "max" for the maximum (Sales Navigator caps at 2500, standard search at 1000). Do not assume a default; this question is required every time.
3. Run node scripts/import.mjs prepare --searcher <acct> --list "<name>" --type <nv|st> --limit <N|max> with either --search-url "<url>" or the filter flags. (--limit is required; a number above the cap is clamped and reported as limit_capped_to_max.)
4. Settle the ICP before qualifying (this is the filtering step — make it explicit, not silent). The prepare output tells you whether an ICP is configured:
   • If none is configured, interview me: which roles/seniority to target, which industries/company types fit, company size/stage if relevant, locations to include or exclude, and any hard exclusions. Summarize what you heard, then save it with node scripts/settings.mjs set icp_definition --stdin (pipe the text via a heredoc — it is stored in the database, not a file; do not leave stray ICP files in the repo or tmp).
   • If one is configured, show it to me in plain language and ask whether to use it as-is or adjust it for this list (re-save if I change it).
5. Read the returned candidate file and qualify every candidate against my ICP, using the output contract in config/qualification-prompt.md. For more than ~25 candidates, chunk the work and delegate each chunk to a sub-agent. Use a cheap/fast model for these qualification sub-agents — it's simple classification, not deep reasoning (in Claude Code, spawn them with model: "haiku"; in other hosts use their small/fast model). Keep orchestration on the main model. Produce a JSON array [{hashed_url, suitable, reasoning}] covering every candidate (reasoning must cite the ICP criterion that drove the decision), and write it to the expected result file path.
6. Run node scripts/import.mjs commit --batch <id> --results <file>.
7. Report transparently: how many were found, skipped as duplicates, kept vs filtered, a few concrete sample reasons from both sides, and how the new leads were distributed across accounts.

Never send invites here — import only stores leads as not_connected.