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 realjaymes/marketingagentskills --skill prospect-scrapergit clone --depth 1 https://github.com/realjaymes/marketingagentskillsWrote 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/realjaymes/marketingagentskills/prospect-scraper)<a href="https://agentmods.dev/skills/realjaymes/marketingagentskills/prospect-scraper"><img src="https://agentmods.dev/badge/skills/realjaymes/marketingagentskills/prospect-scraper/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/realjaymes/marketingagentskills/prospect-scraper"><img src="https://agentmods.dev/badge/skills/realjaymes/marketingagentskills/prospect-scraper.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 | $0.00191 | $0.03506 |
| Opus 5.5 | $0.00076 | $0.01402 |
| Sonnet 5.5 | $0.00038 | $0.00701 |
| Haiku 4.5 | $0.00019 | $0.00351 |
Grade A, and why
prospect-scraper 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 2d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prospect Scraper
The data half of cold outbound. It turns any source into a clean, enriched, verified, scored prospect list.
This skill owns FIND → ENRICH → VERIFY → SCORE. Its companion skill, outbound-engine, owns PERSONALIZE → QA → WARMUP → SEND → MEASURE. The two meet at the common record schema below.
When to use
- Build or extend a scraper or source adapter.
- Produce a prospect list (funded startups or local businesses) with enrichment, verification and scoring.
- Any time you need the list itself and not a campaign: agency prospect lists, client lead lists, GTM-engineering take-home tasks.
Use a different skill for writing the cold email, running the send, deliverability or warmup (outbound-engine), or Clay-specific enrichment when you work inside Clay.
Core principles
- Free-first. Prefer official free APIs, government and open data, and open-source libraries over paid per-record tools. Pay only for the hard remainder, such as some contact enrichment or an occasional Google Maps pull through Outscraper or Apify.
- Adapters, not monoliths. Each source is a thin adapter that emits the one common record schema. Sites change, and the pipeline stays the same.
- Never lose code. Save every adapter's runnable code to a version-controlled repo the moment it's built, and document it where your team can read it. See the preservation step below.
- Latent need over active hiring. Target companies that need the capability but aren't publicly shopping for it. A company hiring for the exact role is a job lead, not a services lead.
- Adopt before you build. Use the default libraries below before hand-rolling anything.
The pipeline
Discover (source adapter) → normalize (common schema) → dedupe → enrich → verify → detect signals → score → export
The common record schema
One record shape for every source (nulls allowed): name, domain, description, category, motion (sales-led, PLG or B2C for startups), region/country, city/state, address, phone, funding stage/amount/date, employee_count, founder, contact name/role/email, email_status (from the verifier), tech_stack, live_ads flag, source(s), first_seen, signals, deliverable, personalization_seed, fit_score, and dedupe keys (domain plus normalized name, or place ID for local businesses).
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.
- 2d ago First seen · 126 lines · 191 tokens per session scan A 246f05aece77
prospect-scraper is a skill published in the GitHub repository realjaymes/marketingagentskills (68 stars, last pushed 2d ago), licensed MIT. It adds 191 tokens to every session and 3,506 once invoked, about $0.0008 per session on Opus 5.5. 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 2026-10-24.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…