Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add matteotitta/genesys-skills --skill lead-scoringgit clone --depth 1 https://github.com/matteotitta/genesys-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/matteotitta/genesys-skills/lead-scoring)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/lead-scoring"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/lead-scoring/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/lead-scoring"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/lead-scoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.01940 |
| Opus 5 | $0.00014 | $0.00970 |
| Sonnet 5 | $0.00006 | $0.00388 |
| Haiku 4.5 | $0.00003 | $0.00194 |
Grade A, and why
lead-scoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead scoring
Evaluate accounts through three layers — fit (structural), signals (temporal), interpretation (synthesized) — to produce a routing recommendation per account. NOT a composite numeric score. Compressing fit, timing, and context into one number destroys the information operators need to act.
When to run
- User asks "score this lead", "is X a good fit", "should we pursue X", "prioritize these accounts", "rank these prospects"
- ABM campaign needs account tiering input
- Pre-discovery account brief, sales pipeline qualification gate
Skip when: user wants full research without assessment angle (/company-context), ICP definition (/icp-research), ABM tactics on already-tiered accounts (/abm-campaign), outreach copy (/outreach-emails).
Inputs
Required: at least one company identifier (URL, LinkedIn URL, or name).
Recommended (lift quality): client ICP doc (/icp-research), CRM engagement history, competitor list, prior /company-context output.
Mode detection: single account → deep assessment. Batch (5+ accounts) → lightweight pass + priority matrix. >15 accounts → calibration round + parallel waves (see the premium reference).
If ICP doc missing: proceed with generic B2B SaaS criteria, flag as "generic ICP" in output, suggest /icp-research upstream.
Steps
- Validate input — confirm company identifier(s), determine mode (single/batch), identify ICP reference.
- Fit assessment (Phase 1) — score firmographic, technographic, use case, negative-fit dimensions per the premium reference. Output verdict: STRONG_FIT | MODERATE_FIT | WEAK_FIT | NO_FIT with evidence + confidence per dimension.
- Signal detection (Phase 2) — catalog leadership, growth, intent, operational, engagement signals per the premium reference. Tag each with category, recency (strong/moderate/weak/expired per decay table), source URL, confidence level.
- Apply recency decay — drop expired signals from active inventory; weak-recency signals provide background only, don't drive routing. Decay table in the premium reference.
- Interpret signal clusters (Phase 3) — identify reinforcement, contradictions, dominant story. Write 2-4 sentence situation hypothesis: "Based on [cluster], [company] appears to be [situation]. This suggests [implication]. The window is [timeframe] because [decay reasoning]."
- Confidence assessment — rate HIGH (dense + fresh + diverse) | MODERATE (2 of 3) | LOW (sparse or stale).
- Routing recommendation (Phase 4) — apply fit × signals matrix in the premium reference. Output: SALES | MARKETING | MONITOR | EVALUATE | DEPRIORITIZE | DISQUALIFY + 2-3 sentence rationale + 1-3 specific next actions.
- Optional — activation score — if client wants auditable math:
signal_activation = strength × recency × fit × tier_weight, sum top-3 per account, bucket into Hot/Warm/Nurture/Cold. Formula in the premium reference. - Optional — tier mode (numeric) — if client CRM needs a
lead_scorefield or sales ops wants a single-column sort: compute weighted tier_score (0-5) and bucket Tier 1 / 2 / 3 / Disqualify. Formula + alignment-with-routing check in the premium reference. - Self-evaluation gate — every signal has source + recency tag, fit dimensions have evidence (not assumption), interpretation reads as narrative not list, routing follows fit×signals matrix, gaps marked [UNAVAILABLE], confidence levels per ontology.
- Format output — single account: full template in the premium reference. Batch: priority matrix template.
- Review gate (Level 1) — present fit verdict, signal summary, situation hypothesis, routing recommendation. Actions: [Approve] [Challenge fit] [Add signals] [Change routing].
- Suggest chain — if SALES routing →
/outreach-emails. If batch →/abm-campaign. If fit uncertain →/company-context. If no ICP →/icp-research.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 123 lines · 130 tokens per session scan A b71c3bc5d431
lead-scoring is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 1,940 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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