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 DataSift-Ty-Personal/SiftStack --skill first-market-county-datagit clone --depth 1 https://github.com/DataSift-Ty-Personal/SiftStackWrote 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/datasift-ty-personal/siftstack/first-market-county-data)<a href="https://agentmods.dev/skills/datasift-ty-personal/siftstack/first-market-county-data"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/first-market-county-data/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/datasift-ty-personal/siftstack/first-market-county-data"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/first-market-county-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00216 | $0.04148 |
| Opus 5.5 | $0.00086 | $0.01659 |
| Sonnet 5.5 | $0.00043 | $0.00830 |
| Haiku 4.5 | $0.00022 | $0.00415 |
Grade A, and why
first-market-county-data 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 2mo 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
First-to-Market County Data Research
Find the exact offices, portals, and processes to pull first-to-market distress lists for any U.S. county, then extract, filter, normalize, and hand the data into the marketing pipeline.
Table of Contents
Router sections:
- The Harder to Acquire Equals More Money Principle
- Moat Math: One Deal Pays For Everything
- Data Priority Pyramid
- When to Use This Skill
- Canonical Notice Types (7)
- Core Workflow
- Reference Files
- Foreclosure Filtering Rules (Critical)
- Difficulty and Freshness Framing
- Compliance and Legal Guardrails
- Handoff: Where This Data Goes Next
- Important Notes
The Harder to Acquire Equals More Money Principle
The harder a data source is to acquire, the more money it makes you. CAPTCHAs, FOIA requests, courthouse visits, portal quirks, and data normalization are barriers, and every barrier is a moat. The investors who will not solve them never reach the seller. Pulling data directly from the county puts you 30-90 days ahead of the buyers who wait for that same record to show up in PropStream or BatchLeads. Reach the motivated seller first and you negotiate against far less competition.
Moat Math: One Deal Pays For Everything
Frame these as illustrative ranges to verify, not fixed prices. Vendor pricing and competition shift over time.
| Source | Cost per Lead | Competition Level |
|---|---|---|
| Self-scraped first-to-market county data | $0.50-$2.00 (plus $0.10-$0.15 skip trace per record) | Very low |
| Nationwide aggregated data (DataSift, PropStream, BatchLeads) | $4.00-$8.00 | High |
| AI-enhanced / predictive data | $8.00-$15.00 | Moderate |
Worked break-even: pulling and skip tracing 1,000 first-to-market records costs roughly ($0.50-$2.00 x 1,000) plus ($0.10-$0.15 x 1,000), about $600-$2,150 all-in. A single wholesale assignment of $8,000-$15,000 repays the whole campaign many times over. Roughly one deal per 1,000 records makes the pipeline profitable. Tier-scored dialing then compounds the edge: dialing best numbers first lifts connect rates roughly 4-5x (from about 2-3% to about 9.5%) per the phone-validator skill, so reaching the seller first AND dialing the best number multiplies the advantage into more closed deals per dollar.
What ships with it
5 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.
- 2mo ago First seen · 209 lines · 216 tokens per session scan A 9bfcdde6ec77
first-market-county-data is a skill published in the GitHub repository DataSift-Ty-Personal/SiftStack (23 stars, last pushed 23d ago), licensed MIT. It adds 216 tokens to every session and 4,148 once invoked, about $0.0009 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 2026-08-30.
first-market-county-data compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| first-market-county-dataDataSift-Ty-Personal/SiftStack | Skill | 23 | 23d ago | MIT | A | 216 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |