lead-qualification
Lead qualification engine with conversational intake. Asks structured questions to understand your qualification criteria, generates a reusable qualification prompt, then batch-enriches leads via Apify LinkedIn scraping and scores them with parallel processing. Outputs qualified/disqualified verdicts with confidence scores and reasoning to CSV or whatever output format the user prefers. Supports calibration mode for prompt refinement.
npx skills add gooseworks-ai/goose-skills --skill lead-qualification --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Lead Qualification Engine Qualify leads against custom criteria through a structured intake process, then score lead lists in parallel with confidence ratings and reasoning. ## Three Modes of Operation ### Mode 1: Full Intake + Qualify No existing qualification prompt. Run intake to build one, save it, then qualify leads. **Trigger:** User provides no qualification prompt file. ### Mode 2: Reuse Prompt + Qualify User references an existing qualification prompt file — skip intake, go straight to scoring. **Trigger:** User tags or references a file in `skills/lead-qualification/qualification-prompts/`. ### Mode 3: Refine / Calibrate User has seen results and wants to adjust criteria. Update the saved prompt, re-run. **Trigger:** User says something like "refine", "adjust", "that's wrong", or provides feedback on qualification results. --- ## Phase 1: Intake (Mode 1 Only) The goal is to build a complete picture of who the user considers qualified vs disqualified. Present questions in bulk rounds so the user can answer efficiently. ### Round 1 — Core Questions (Present All at Once) Present these questions as a numbered list. Tell the user: *"Answer what's relevant, skip what's not. I
- Three Modes of Operation
- Mode 1: Full Intake + Qualify
- Mode 2: Reuse Prompt + Qualify
- Mode 3: Refine / Calibrate
- Phase 1: Intake (Mode 1 Only)
- Round 1 — Core Questions (Present All at Once)
- Round 2 — Follow-Up Probes
- Round 3 — Edge Case Scenarios (Optional but Recommended)
- Generate & Save Qualification Prompt
- Phase 2: Lead Qualification
- Step 1 — Parse Input
- Step 1.5 — Batch Enrichment via Apify
- Step 2 — Calibration Batch
- Step 3 — Full Run (Parallelized)
python3 skills/lead-qualification/scripts/enrich_leads.py INPUT_CSV \
What does the lead-qualification skill do?
Lead qualification engine with conversational intake. Asks structured questions to understand your qualification criteria, generates a reusable qualification prompt, then batch-enriches leads via Apify LinkedIn scraping and scores them with parallel processing. Outputs qualified/disqualified verdicts with confidence scores and reasoning to CSV or whatever output format the user prefers. Supports calibration mode for prompt refinement.
How do I install it?
Run `npx skills add gooseworks-ai/goose-skills --skill lead-qualification --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.