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 buyer-prospectorgit 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/buyer-prospector)<a href="https://agentmods.dev/skills/datasift-ty-personal/siftstack/buyer-prospector"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/buyer-prospector/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/buyer-prospector"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/buyer-prospector.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 | $0.00190 | $0.03697 |
| Opus 5 | $0.00095 | $0.01849 |
| Sonnet 5 | $0.00038 | $0.00739 |
| Haiku 4.5 | $0.00019 | $0.00370 |
Grade A, and why
buyer-prospector 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 9d 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 — 335 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Buyer Prospector
Pull active real estate buyer data for any US county from a nationwide database of 84,000+ buyer records across 1,471 counties. The skill filters buyers by the user's target county, categorizes entities, and walks through a structured research workflow to identify the actual decision-makers behind LLCs, trusts, and corporations — turning raw transaction data into a skip-traceable buyers list.
When to Use
- User wants to find active buyers in a specific county/state
- Building a buyers list for a new market
- Need to identify who's buying properties in an area
- Prospecting cash buyers for wholesaling deals
- Researching investor activity in a target county
What the User Needs to Provide
At minimum, the user needs to tell you:
- County name (e.g., "Knox", "Harris", "Maricopa")
- State (e.g., "TN", "TX", "AZ")
Optional preferences:
- Minimum purchase threshold (default: 2 purchases in 6 months)
- How many records to research (batch size)
- Whether they want the full research workflow or just the analysis
Workflow Overview
1. Filter nationwide data for the target county
2. Run entity analysis (categorize + prioritize)
3. Review results with user
4. Research decision-makers for High priority entities
5. Update the Excel workbook with findings
6. Deliver completed buyer analysis
Step 1: Filter and Analyze
Run the analysis script on the bundled nationwide dataset. The script path is relative to this skill's directory:
python <skill-path>/scripts/analyze_buyers.py <skill-path>/data/nationwide_buyers.csv \
--county "<county_name>" \
--state "<state_abbrev>" \
--output "<output_path>"
Example:
python <skill-path>/scripts/analyze_buyers.py <skill-path>/data/nationwide_buyers.csv \
--county "Knox" \
--state "TN" \
--output "Knox_TN_Buyer_Analysis.xlsx"
The --min-purchases flag defaults to 2. If the user wants a broader or narrower list, adjust accordingly.
Save the output Excel file to the user's workspace so they can access it.
What ships with it
3 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.
- 9d ago First seen · 335 lines · 190 tokens per session scan A d43974ea547d
buyer-prospector is a skill published in the GitHub repository DataSift-Ty-Personal/SiftStack (21 stars, last pushed 5d ago), licensed MIT. It adds 190 tokens to every session and 3,697 once invoked, about $0.0010 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 2026-08-30.
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