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qualify-leads

Business
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Run the GTM-OS 7-gate qualification pipeline over a batch of leads from a CSV, JSON, Notion DB, profile-visitors export, post-engagers export, or an existing SQLite result set. Writes per-lead pass/fail data back into SQLite and returns a result-set id you can hand to the campaign launcher. Use when the user says 'qualify these leads', 'score this lead list', 'run the qualification pipeline', 'check if these leads are a fit', or 'qualify the engagers'. Side-effecting — writes to the local SQLite db.

QUICK START

How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/Othmane-Khadri/YALC-the-GTM-operating-system/blob/HEAD/.claude/skills/qualify-leads/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/qualify-leads/. Do not write files or run scripts until I approve.

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Qualify Leads

I'll wrap leads:qualify. I'll ask for the input source and path, validate locally, run the CLI, parse the per-gate counts, and surface the result-set id so you can hand it to launch-linkedin-campaign or personalize-message.

When This Skill Applies

Use this skill when the user says:

  • "qualify these leads"
  • "score this lead list"
  • "run the qualification pipeline"
  • "check if these leads are a fit"
  • "qualify the engagers"

NOT this skill (use personalize-message instead):

  • "personalize a message for this lead" — that wraps the personalize CLI and writes a single LinkedIn DM, it doesn't run the gate pipeline.
  • "draft a DM for [lead]" — same.

NOT this skill (use scrape-post-engagers instead):

  • "scrape engagers off this LinkedIn post" — that wraps leads:scrape-post. Run that first to produce an engagers JSON, then come back here to qualify it.

What This Skill Does

  1. Asks where the leads live (CSV path, JSON path, Notion DB id, visitors export, engagers export, or an existing result-set id).
  2. Validates the input locally — file exists, Notion id has the right shape, result-set id format looks plausible.
  3. Shells out to npx tsx src/cli/index.ts leads:qualify <args> from ~/Desktop/gtm-os/.
  4. Parses the CLI's stdout — the pipeline emits per-gate counters and the resultSetId.
  5. Renders a clean per-gate summary, the result-set id, and the top hot leads.
  6. Offers two follow-up moves: launch a campaign or personalize messages.

What This Skill Does NOT

  • Send any outbound messages. That's launch-linkedin-campaign (LinkedIn) or send-cold-email (email).
  • Author personalized message bodies. That's personalize-message.
  • Re-import data that's already a result set. If the user already has a resultSetId, pass --result-set <id> and skip --source / --input.
  • Modify any .env file. If a key is missing, the CLI raises and we surface its stderr verbatim.

Pre-flight (do this before step 1)

  1. Onboarding interruption guard. Run:

    test -f ~/.gtm-os/.in-flight-setup && echo "BLOCKED" || echo "OK"

    If BLOCKED, stop. Tell the user: "Setup is mid-flight. Finish yalc-gtm start first, then re-invoke me." Exit cleanly.

  2. Confirm cwd. All shell-outs assume ~/Desktop/gtm-os/.

Workflow

Step 0 — Ask for input

One question at a time:

  1. Where do the leads live? Five options:

    • CSV path (--source csv --input <path>)
    • JSON path (--source json --input <path>)
    • Notion DB id (--source notion --input <db-id>)
    • Profile-visitors export (--source visitors --input <path>)
    • Post-engagers export (--source engagers --input <path>)
    • Or "use the most recent result set" / a specific id (--result-set <id>, no --source / --input)

  2. Any toggles? Ask only if volunteered:

    • --dry-run — preview without writing
    • --no-dedup — skip dedup gate
    • --slack-confirm — Slack for ambiguous dedup matches
    • --enrich-signals (+ --signals-types <jobs,funding,tech,news>) — pull PredictLeads after qualify

Step 1 — Validate the input locally

  • For csv / json / visitors / engagers: test -f <path> returns 0.
  • For notion: 32-char hex (with or without dashes). Extract id from URL if needed.
  • For --result-set <id>: format looks plausible.

Step 2 — Shell out

cd ~/Desktop/gtm-os && set -a && source .env.local && set +a && \
  npx tsx src/cli/index.ts leads:qualify --source <type> --input <path>

…or for an existing result set:

cd ~/Desktop/gtm-os && set -a && source .env.local && set +a && \
  npx tsx src/cli/index.ts leads:qualify --result-set <id>

Per the merged 0.13.0 benchmark in docs/skills-architecture.md, single-command side-effecting skills shell out unconditionally. The CLI is the source of truth for env loading, tenant resolution, and the withDiagnostics() wrapper.

Step 3 — Parse the output

leads:qualify writes [qualify] log lines to stdout, one per gate, plus the result-set id and survivor count. On non-zero exit, surface stderr verbatim — no summarising.

Step 4 — Render

Show the per-gate funnel, result-set id, and top 5 hot leads. See references/example-output.md.

Step 5 — Offer follow-ups

"Want me to also: (a) launch a LinkedIn campaign for the qualified leads via launch-linkedin-campaign, or (b) personalize messages for the top N via personalize-message?"

Don't run anything unless the user says yes.

Failure surfacing — verbatim, never summarised

When leads:qualify exits non-zero, paste its stderr unchanged. The CLI's messages are tested and stable.

Notes

  • Pipeline writes to ~/.gtm-os/gtm-os.db. Side-effecting → Pattern A (shell-out) per the architecture doc.
  • 7 gates: dedup → prequal → exclusion → company-fit → role-fit → ICP scoring → optional signal enrichment. See src/lib/qualification/pipeline.ts.
  • --enrich-signals ≈ 1 PredictLeads credit per surviving domain (cached 7 days).