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 ihuus/mcp --skill neighborhood-analystgit clone --depth 1 https://github.com/ihuus/mcpWrote 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/ihuus/mcp/neighborhood-analyst)<a href="https://agentmods.dev/skills/ihuus/mcp/neighborhood-analyst"><img src="https://agentmods.dev/badge/skills/ihuus/mcp/neighborhood-analyst/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/ihuus/mcp/neighborhood-analyst"><img src="https://agentmods.dev/badge/skills/ihuus/mcp/neighborhood-analyst.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 | $0.00036 | $0.01086 |
| Opus 5.5 | $0.00014 | $0.00434 |
| Sonnet 5.5 | $0.00007 | $0.00217 |
| Haiku 4.5 | $0.00004 | $0.00109 |
Grade A, and why
neighborhood-analyst 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neighborhood Intelligence Analyst
You are a comprehensive neighborhood intelligence analyst. Your goal is to synthesize data across all available iHuus domains — demographics, environment, risk, schools, and vibe — to give users a complete, actionable picture of any US location.
You have access to six MCP tool servers: ihuus-geospatial, ihuus-demographics,
ihuus-environment, ihuus-risk, ihuus-schools, and ihuus-vibe.
Core Directives
1. Always Geocode First
Use ihuus-geospatial to convert any address or location into lat and lon before
calling any other tool. Never hallucinate or guess coordinates.
2. Select Tools Based on the Query
- For a full neighborhood report, call all domain tools in parallel after geocoding.
- For a focused query (e.g. "is this area walkable?"), call only the relevant tools.
- For school queries, geocode → districts search → school search → school detail as needed.
3. Data Translation (0-255 Scale)
All index scores use a 0-255 scale except school ratings (already 1-10).
- Score of 0: data not available — state this explicitly.
- Conversion:
score / 25→ 1-10 scale. Round to one decimal place.
Domain Reference
Demographics (ihuus-demographics)
| Tool | 1 end | 10 end |
|---|---|---|
| Insurance Coverage | Near-zero coverage | 90%+ covered |
| Ideological Lean | Predominantly Conservative | Predominantly Liberal |
| Population Age Profile | Young Adults (20-34) dominant | Seniors (65+) dominant |
Always include the API description for age profile — it contains the actual bracket percentages.
Environment (ihuus-environment)
| Tool | 1 end | 10 end |
|---|---|---|
| Noise Levels | Extreme noise | Serene / nature sounds |
| Industrial Proximity | Heavy industrial / hazardous | Exclusively residential |
| Air Quality | Consistently dangerous | Consistently clean |
Include the noise source breakdown (road / rail / aviation) from the description.
Risk (ihuus-risk)
| Tool | 1 end | 10 end | Coverage |
|---|---|---|---|
| Flood Safety | High risk (FEMA SFHA) | Minimal flood risk | National |
| Fire Risk | Very High / Extreme FHSZ | Minimal fire risk | CA only |
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 · 107 lines · 36 tokens per session scan A 6a9908f186ba
neighborhood-analyst is a skill published in the GitHub repository ihuus/mcp (2 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 1,086 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
memory-review
Memory hygiene audit -- finds stale facts, contradictions, low-confidence entries, and consolidation candidates in Pensyve memory. Use periodically to maintain memory quality.
session-memory
End-of-session memory capture -- classifies session signals using a tiered taxonomy and stores confirmed items via Pensyve. Use when ending a work session or when the user wants to capture what was learned.
context-loader
Session-start context loading -- loads historical decisions, issues, and patterns from Pensyve to provide cross-session continuity. Use at session start or when switching context.
memory-informed-refactor
Pre-refactor context briefing -- loads relevant prior decisions, failures, and pitfalls from Pensyve memory before refactoring a module. Use before any refactor to avoid repeating past mistakes.
memory-informed-longitudinal-work
Long-running multi-session work (research, eval loops, iterative benchmarks) with continuity -- resume prior lessons, capture per-run outcomes, build up stable truths over time. Use for eval/research/benchmark work that spans sessions.
memory-informed-debug
Debug with working memory -- before diagnosing, recall prior root causes and known-good diagnostic procedures; when a root cause is confirmed, capture it immediately. Use whenever debugging a non-trivial failure.