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 librefang/librefang-registry --skill leadgit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/lead)<a href="https://agentmods.dev/skills/librefang/librefang-registry/lead"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/lead/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/librefang/librefang-registry/lead"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/lead.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 | $0.00026 | $0.06729 |
| Opus 5.5 | $0.00010 | $0.02692 |
| Sonnet 5.5 | $0.00005 | $0.01346 |
| Haiku 4.5 | $0.00003 | $0.00673 |
Grade A, and why
lead-hand-skill 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 1mo 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.
This is a copy
100% identical to lead-hand-skill — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 673 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Generation Expert Knowledge
Ideal Customer Profile (ICP) Construction
A good ICP answers these questions:
- Industry: What vertical does your ideal customer operate in?
- Company size: How many employees? What revenue range?
- Geography: Where are they located?
- Technology: What tech stack do they use?
- Budget signals: Are they funded? Growing? Hiring?
- Decision-maker: Who has buying authority? (title, seniority)
- Pain points: What problems does your product solve for them?
Company Size Categories
| Category | Employees | Typical Budget | Sales Cycle |
|---|---|---|---|
| Startup | 1-50 | $1K-$25K/yr | 1-4 weeks |
| SMB | 50-500 | $25K-$250K/yr | 1-3 months |
| Enterprise | 500+ | $250K+/yr | 3-12 months |
ICP Refinement Loop
The ICP should not be static. After every 3 report cycles, refine it:
- Analyze top performers: Look at leads scored 80+ — what industry sub-segments, company sizes, and role patterns appear most often?
- Analyze low performers: Look at leads scored below 40 — which ICP criteria were they missing? Were there false positives from overly broad keywords?
- Tighten criteria: Narrow industry keywords (e.g., "fintech" becomes "payment infrastructure fintech"), adjust company size range, add or remove geographic regions, refine role titles.
- Track revisions: Log each ICP revision with date, changes made, and rationale. This creates an audit trail showing how targeting improved over time.
- Measure impact: Compare average lead score before and after each ICP revision. A well-refined ICP should produce higher average scores with fewer total leads — quality over quantity.
Web Research Techniques for Lead Discovery
Search Query Patterns
# Find companies in a vertical
"[industry] companies" site:crunchbase.com
"top [industry] startups [year]"
"[industry] companies [city/region]"
# Find decision-makers
"[title]" "[company]" site:linkedin.com
"[company] team" OR "[company] about us" OR "[company] leadership"
# Growth signals (high-intent leads)
"[company] hiring [role]" — indicates budget and growth
"[company] series [A/B/C]" — recently funded
"[company] expansion" OR "[company] new office"
"[company] product launch [year]"
# Technology signals
"[company] uses [technology]" OR "[company] built with [technology]"
site:stackshare.io "[company]"
site:builtwith.com "[company]"
What ships with it
2 files 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.
- 1mo ago First seen · 673 lines · 26 tokens per session scan A 443f4464e196
lead-hand-skill is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 6,729 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to lead-hand-skill, differing in 0 lines, and is treated as a copy.
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