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 spiralcrew-ou/profilespider-agent-skills --skill b2b-lead-qualificationgit clone --depth 1 https://github.com/spiralcrew-ou/profilespider-agent-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/spiralcrew-ou/profilespider-agent-skills/b2b-lead-qualification)<a href="https://agentmods.dev/skills/spiralcrew-ou/profilespider-agent-skills/b2b-lead-qualification"><img src="https://agentmods.dev/badge/skills/spiralcrew-ou/profilespider-agent-skills/b2b-lead-qualification/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/spiralcrew-ou/profilespider-agent-skills/b2b-lead-qualification"><img src="https://agentmods.dev/badge/skills/spiralcrew-ou/profilespider-agent-skills/b2b-lead-qualification.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 | $0.00054 | $0.00527 |
| Opus 5 | $0.00027 | $0.00264 |
| Sonnet 5 | $0.00011 | $0.00105 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
b2b-lead-qualification 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 12d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
B2B Lead Qualification
Purpose
Score and classify B2B prospects against a defined ideal customer profile, with reasoning and a recommended next action.
When to use this skill
- Qualifying exported prospect lists before outreach
- Prioritizing accounts when you have more leads than capacity
- Separating high-fit and low-fit companies
- Documenting why a prospect matches an ICP
When not to use this skill
- You have no defined ICP or qualification criteria yet
- You need verified contact details rather than fit scoring
- The dataset has no company or firmographic fields to score against
Required inputs
- An ICP definition
- A prospect or company dataset
- Qualification criteria
Optional inputs
- Exclusion criteria
- Weighted scoring rules
- Target locations
- Company size range
- Required technologies or industries
Rules
- Use only information present in the supplied dataset or explicitly provided by the user.
- Do not invent missing company, contact, revenue, employee, technology, or location information.
- Clearly distinguish known facts from assumptions.
- Flag missing information that materially affects the score.
- Apply the same scoring framework consistently to every prospect.
Process
- Parse the ideal customer profile.
- Extract positive qualification criteria.
- Extract disqualifying criteria.
- Evaluate each prospect against both sets.
- Assign a score from 0 to 100.
- Explain the score using available evidence.
- Recommend the next action.
Output format
Return one record per prospect with the following fields:
- fit_score
- fit_level
- qualification_reason
- supporting_evidence
- missing_information
- recommended_next_action
Validation
- Confirm every score is supported by evidence from the input.
- Confirm missing fields are listed rather than guessed.
- Confirm the same criteria were applied to every record.
Limitations
- Scores reflect fit, not intent or buying readiness.
- A high score is not a guarantee of a sale; verify before investing heavily.
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.
- 12d ago First seen · 88 lines · 54 tokens per session scan A 1fbc379b372e
b2b-lead-qualification is a skill published in the GitHub repository spiralcrew-ou/profilespider-agent-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 527 once invoked, about $0.0003 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.
Other skills, from other repositories
inbound-lead-enrichment
Fills in missing data for inbound leads — researches the company, identifies the person's role and seniority, finds other stakeholders at the company, checks for existing CRM relationships, and updates the lead record. Produces enriched lead data ready for qualification or outreach. Tool-agnostic.
inbound-lead-qualification
Qualifies inbound leads against full ICP criteria — company size, industry, use case fit, role/seniority of the person. Checks CRM and existing customer base for duplicates and existing relationships. Outputs a scored CSV with qualification status, reasoning, and pipeline overlap flags. Tool-agnostic — works with any…
inbound-lead-triage
Triages all inbound leads from a given period — demo requests, free trial signups, content downloads, webinar registrations, chatbot conversations. Classifies by urgency, qualifies against ICP, enriches with context, and produces a prioritized action queue with recommended response for each lead. Tool-agnostic — works…
company-contact-finder
Find decision-makers at a specific company using Apollo, Crustdata, Fiber, and PDL people search via Gooseworks MCP. Given a company name and target titles, returns a list of contacts with name, title, LinkedIn URL, and location.
funding-signal-monitor
Monitor web sources for Series A-C funding announcements. Aggregates signals from TechCrunch, Crunchbase (via web search), Twitter, Hacker News, and LinkedIn. Filters by stage, amount, and industry. Returns qualified recently-funded companies ready for outreach.
kol-engager-icp
Find ICP-fit leads from KOL audiences on LinkedIn. Given a list of KOLs, scrapes their most relevant high-engagement post from the last 30 days, extracts engagers (reactors + commenters), pre-filters by position, enriches top profiles, and ICP-classifies. Cost-controlled: 1 post per KOL. Use when someone wants to…