# Fivos Lead Scoring > Use this skill to qualify and tier a list of accounts or contacts. Runs a 10-row sample first with per-row reasoning so the engineer can audit accuracy before committing to a full run. Trigger when the user says 'score these accounts', 'qualify the list', 'run qualification', 'tier the leads', or after fivos-account-enrichment / fivos-contact-enrichment. - **Type:** Skill - **Install:** `agentstack add skill-fivosaresti-workflows-outbound-skills-lead-scoring` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [fivosaresti](https://agentstack.voostack.com/s/fivosaresti) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [fivosaresti](https://github.com/fivosaresti) - **Source:** https://github.com/fivosaresti/workflows-outbound-skills/tree/main/lead-scoring ## Install ```sh agentstack add skill-fivosaresti-workflows-outbound-skills-lead-scoring ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Fivos Lead Scoring Qualification + tiering for accounts and contacts. Broad-then-narrow: source wide, filter down with a prompt. Scoring runs **in-session through Claude itself** - no external model needed today. ## How scoring runs (today) - Claude reads the input CSV row-by-row (using the Read tool on a sample, then processing batches). - For each row, Claude applies the validated qualification prompt and outputs a structured decision: `{tier, score, reasoning}`. - Claude writes the scored CSV back to disk using Write/Edit. - No external model wiring required. First-class, not a fallback. If you have a dedicated local qualification model (via an MCP or API), this skill's Step 2 and Step 5 can be swapped to call it. The rest of the flow - sample, audit, two gates - stays identical. ## Hard rules - **Two approval gates** per the TAM Mapping and Contact Sourcing Plan - 2026: - Gate 1: engineer approves / revises the qualification prompt after seeing 10-row sample results. - Gate 2: re-estimate full-run cost with the validated prompt, manager approval before scaling. - **Every scored row carries reasoning.** Store `tier, score, reasoning` triples for audit - not bare tiers. - **Re-scoring uses this same skill.** Never write custom scoring scripts outside this skill. If criteria change mid-campaign, re-invoke. ## Inputs - **CSV to score** - account CSV (from `fivos-account-enrichment`), contact CSV, or both. - **Qualification prompt** - engineer-provided. If missing, the skill suggests one from the template below. - **ICP matrix** - from `fivos-icp-segmentation`. Gives signal weights and tier thresholds. - **Tier thresholds** (optional) - override the default A/B/C cutoffs if the client has specific definitions. ## Outputs 1. Scored CSV with columns appended: `tier, score, reasoning, scored_at, scorer_version`. 2. Tier distribution report (counts of A/B/C/DQ, top disqualification reasons). 3. Audit trail: the prompt used, the gate approvals received. ## Prompt template If the engineer didn't provide a prompt, start here and fill in the bracketed sections from the ICP: ``` You are qualifying 's outbound list. The ICP is: - Industry: - Size: - Signals that raise fit: - Known disqualifiers: For each row you receive, decide tier A/B/C: - A = strong ICP fit + at least one high-weight signal firing. - B = ICP fit, no strong signal, or signal firing on a weaker-fit account. - C = tangential fit. Keep only if volume is needed. - DQ = disqualify (wrong geo, wrong industry, sanctioned, bankrupt, etc.). Return JSON per row: {"tier": "A"|"B"|"C"|"DQ", "score": 0-100, "reasoning": ""}. ``` Always show the engineer the fully-rendered prompt before running. ## Workflow ### Step 1 - Prompt assembly Render the template with the ICP doc's values. Show the rendered prompt to the engineer for a quick read. ### Step 2 - 10-row sample (in-session, Claude-driven) Read the first 10 rows of the input CSV. For each row, apply the prompt and produce the structured output. Present results inline: ``` Row 1: Acme Corp (domain: acme.com) Tier: A Score: 87 Reasoning: Strong industry fit; Series B within 45 days (high-weight signal); headcount in target band; no disqualifiers. Row 2: Globex (domain: globex.io) Tier: DQ Score: 10 Reasoning: Wrong industry (consumer retail); also HQ in sanctioned region. ... ``` ### Step 3 - Gate 1 (prompt validation) Ask the engineer: ``` 10-row sample complete. Distribution: A=X, B=X, C=X, DQ=X Audit the reasoning. Do any look wrong? Options: [approve] - proceed to Gate 2 [revise] - edit prompt and re-run sample [abort] - stop, do not score ``` Iterate on the prompt until the engineer signs off. Common revisions: tightening a disqualifier, re-weighting a signal, adding a geo exclusion, adding an industry keyword. ### Step 4 - Gate 2 (full-run approval) Once the prompt is validated, present the full-run plan: ``` Full qualification plan - / Rows to score: N Scorer: Claude (in-session) Estimated time: ~M minutes (typical: 2-4 seconds/row in batched reads) Manager approval required to proceed. ``` ### Step 5 - Full run Read the CSV in batches (50-200 rows per batch depending on row width), apply the prompt per row, append the scoring columns, write the result to `_scored.csv`. ``` scorer_version: "claude--" # e.g. claude-opus-4-7-2026-04-22 ``` ### Step 6 - Distribution + anomaly report ``` Qualification complete - / Total scored: N Distribution: - A: X (X%) - B: Y (Y%) - C: Z (Z%) - DQ: W (W%) Top disqualification reasons: 1. (X rows) 2. (Y rows) 3. ... Flags: - Unexpectedly high DQ rate (>25%) - review prompt tightness. - Tier-A concentration in one industry - possibly over-indexed. ``` ### Step 7 - Handoff Hand the scored CSV to `fivos-copywriter`. Tier drives copy depth: A = deep personalisation, B = signal-triggered, C = pattern-only. ## Cutover to a dedicated qualification model If you wire a local or API-hosted qualification model: 1. Replace Step 2 + Step 5 with calls to that model. 2. Update `scorer_version` in the output CSV to reflect the model. 3. Everything else (sample → Gate 1 → Gate 2 → full run → report) stays the same. No structural change to this skill is needed beyond swapping the two step bodies. ## Re-scoring If prompt criteria change mid-campaign (new disqualifier, re-weighting), re-run this skill on the existing CSV. Always bump `scorer_version` and `scored_at` so downstream analytics can see the change. ## References - `~/.agents/skills/gtm-meta-skill/writing-outreach.md` - scoring rubric examples, prompt patterns. - `~/.agents/skills/gtm-meta-skill/prompts.json` - qualification prompt templates. - TAM Mapping and Contact Sourcing Plan - 2026 (Google Doc). ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [fivosaresti](https://github.com/fivosaresti) - **Source:** [fivosaresti/workflows-outbound-skills](https://github.com/fivosaresti/workflows-outbound-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-fivosaresti-workflows-outbound-skills-lead-scoring - Seller: https://agentstack.voostack.com/s/fivosaresti - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.