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 AlpacaLabsLLC/skills-for-architects --skill zoning-analysis-nycgit clone --depth 1 https://github.com/AlpacaLabsLLC/skills-for-architectsWrote 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/alpacalabsllc/skills-for-architects/zoning-analysis-nyc)<a href="https://agentmods.dev/skills/alpacalabsllc/skills-for-architects/zoning-analysis-nyc"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/zoning-analysis-nyc/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/alpacalabsllc/skills-for-architects/zoning-analysis-nyc"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/zoning-analysis-nyc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 17 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Data Exfiltration · line 52 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 56 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 61 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00057 | $0.04157 |
| Opus 5 | $0.00028 | $0.02079 |
| Sonnet 5 | $0.00011 | $0.00831 |
| Haiku 4.5 | $0.00006 | $0.00416 |
Grade A, and why
zoning-analysis-nyc scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/as:zoning-analysis-nyc — Zoning Envelope Analysis (New York City)
Harness note: use
/as:<skill>on Claude Code and$<skill>on Codex. Resolve<skill-root>as the directory containing this loadedSKILL.mdand<plugin-root>as the plugin root that containsskills/, and use equivalent native tools when host tool names differ.
Analyze building envelope rules for any lot in New York City using the PLUTO database (NYC Open Data) and the NYC Zoning Resolution.
Project context
If PROJECT.md exists in the working directory, read it before fetching — the BBL, district, and prior envelope results may already be on file. After completing, offer the zoning district, FAR set, envelope results, overlays/special districts, and landmark status to /as:project update for its Zoning section, each with a source and date. If the analysis surfaces a choice between development paths, propose /as:project record-decision. No PROJECT.md? Skip silently — or mention /as:project init if the user is clearly starting a project.
Workflow
Step 1: Parse Input
Accept one of the following identifiers:
- Address + Borough/Zip — e.g., "123 Main St, Brooklyn 11201"
- BBL — 10-digit Borough-Block-Lot (e.g., 3012340056 = Brooklyn, Block 1234, Lot 56)
- BIN — Building Identification Number
Normalize to BBL format: [borough 1 digit][block 5 digits][lot 4 digits]
Borough codes:
| Code | Borough |
|---|---|
| 1 | Manhattan |
| 2 | Bronx |
| 3 | Brooklyn |
| 4 | Queens |
| 5 | Staten Island |
Step 2: Query PLUTO (tabular + polygon)
Fetch lot data from two NYC APIs in parallel:
2a. Tabular data (Socrata PLUTO)
Endpoint: https://data.cityofnewyork.us/resource/64uk-42ks.json
Query by BBL:
https://data.cityofnewyork.us/resource/64uk-42ks.json?bbl=XXXXXXXXXX
Query by address (fallback):
https://data.cityofnewyork.us/resource/64uk-42ks.json?$where=address='123 MAIN STREET' AND zipcode='10001'
What ships with it
11 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.
- README.md 5.1 KB
- zoning-rules/city-of-yes.md 5.2 KB
- zoning-rules/commercial.md 6.8 KB
- zoning-rules/contextual-districts.md 7.7 KB
- zoning-rules/manufacturing.md 5.2 KB
- zoning-rules/overview.md 6.0 KB
- zoning-rules/parking.md 4.5 KB
- zoning-rules/pluto-fields.md 6.2 KB
- zoning-rules/residential.md 7.6 KB
- zoning-rules/special-districts.md 6.2 KB
- zoning-rules/use-groups.md 5.2 KB
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 · 405 lines · 57 tokens per session scan A 16a01d933096
zoning-analysis-nyc is a skill published in the GitHub repository AlpacaLabsLLC/skills-for-architects (353 stars, last pushed 8d ago), licensed MIT. It adds 57 tokens to every session and 4,157 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 2026-08-30.
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