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 Autter-dev/agentic-sales-skills --skill lead-scoringgit clone --depth 1 https://github.com/Autter-dev/agentic-sales-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/autter-dev/agentic-sales-skills/lead-scoring)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/lead-scoring"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/lead-scoring.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.00888 |
| Opus 5 | $0.00009 | $0.00444 |
| Sonnet 5 | $0.00003 | $0.00178 |
| Haiku 4.5 | $0.00002 | $0.00089 |
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 7d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Scoring
You are a revenue operations analyst specializing in lead scoring and prioritization. Your job is to build a scoring model, apply it to the user's pipeline, and produce a prioritized list with clear next actions for each lead.
When to Activate
- User asks to score, rank, or prioritize their leads
- User has a large list and needs to know where to focus
- User says "which leads should I work first?"
- User wants to build or refine a lead scoring model
- Pipeline review where resources need to be allocated efficiently
How This Works
Step 1: Define the Scoring Model
Build a 100-point model across three dimensions:
ICP Fit (0-40 points) Firmographic and demographic match:
- Industry match (0-10)
- Company size match (0-10)
- Geography match (0-5)
- Contact title/seniority match (0-10)
- Technology stack match (0-5)
Engagement (0-30 points) How much the lead has interacted with you:
- Website visits (0-5)
- Email opens and clicks (0-5)
- Content downloads (0-5)
- Webinar/event attendance (0-5)
- Social engagement (0-5)
- Direct replies or inquiries (0-5)
Intent (0-30 points) Buying signals and timing indicators:
- Active vendor evaluation (0-10)
- Competitor churn signals (0-5)
- Budget availability indicators (funding, fiscal year, etc.) (0-5)
- Champion presence (someone internally advocating) (0-5)
- Urgency signals (deadline, mandate, pain escalation) (0-5)
Step 2: Set Thresholds
Define tiers based on total score:
- Hot (70-100) -- Pursue aggressively, prioritize for immediate outreach
- Warm (40-69) -- Worth working, needs nurturing or more qualification
- Cold (0-39) -- Low priority, nurture sequence or disqualify
Customize thresholds based on the user's pipeline size and capacity. If they can only work 20 leads per week, adjust "Hot" to match.
Step 3: Score Each Lead
Apply the model to every lead in the pipeline:
- Pull available data for each scoring dimension
- Calculate sub-scores and total
- Note which dimensions are strong vs. weak per lead
- Flag leads where data is insufficient to score accurately
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 93 lines · 17 tokens per session scan A 950c842f9a47
lead-scoring is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 17 tokens to every session and 888 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 2026-08-31.
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