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 muend/geoai-skills --skill network-accessibility-analysisgit clone --depth 1 https://github.com/muend/geoai-skillsWrote 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/muend/geoai-skills/network-accessibility-analysis)<a href="https://agentmods.dev/skills/muend/geoai-skills/network-accessibility-analysis"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/network-accessibility-analysis/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/muend/geoai-skills/network-accessibility-analysis"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/network-accessibility-analysis.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 | $0.00099 | $0.01508 |
| Opus 5 | $0.00049 | $0.00754 |
| Sonnet 5 | $0.00020 | $0.00302 |
| Haiku 4.5 | $0.00010 | $0.00151 |
Grade A, and why
network-accessibility-analysis 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 10d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Network & Accessibility Analysis
Purpose: replace as-the-crow-flies guesswork with network-true travel costs, at the right scale and with honest assumptions about speeds and modes. First decision on every task: Euclidean distance is only acceptable as a declared approximation — flag it whenever you see it standing in for access.
Tool selection by scale
| Scale | Tool |
|---|---|
| Neighborhood-city, research flexibility | OSMnx + NetworkX |
| City-region, many-to-many OD (>10⁴×10⁴) | r5py (multimodal + transit w/ GTFS) or pandana (contraction-hierarchy speed) |
| Production routing service | Valhalla / OSRM / OpenRouteService API |
| Proprietary stacks | ArcGIS Network Analyst (script it headlessly) |
NetworkX chokes on metro-scale many-to-many — don't loop shortest_path
over thousands of origins; switch tools instead.
Graph construction (OSMnx)
import osmnx as ox
G = ox.graph_from_place("City, Country", network_type="drive") # walk/bike/all
G = ox.add_edge_speeds(G) # imputes from highway tags where maxspeed missing
G = ox.add_edge_travel_times(G) # edge attr: travel_time (s)
G = ox.project_graph(G) # metric CRS before any distance work
- network_type matters: pedestrian analysis on a
drivegraph misses paths, stairs, plazas; driving onalluses footpaths. Match mode. - Imputed speeds are averages by road class — a systematic bias, not noise. State it; calibrate against known trips when stakes are high.
- Keep the strongly connected component for routing
(
ox.truncate.largest_component(G, strongly=True)); orphan islands cause spurious infinities. - Snapping: origins/destinations map to nearest nodes/edges
(
ox.distance.nearest_nodes). Report the snap-distance distribution; a facility snapped 2 km away (riverside, gated area) silently corrupts results.
Core products
- Isochrones / service areas: ego-graph by travel_time cutoff → alpha shape or buffered edge union around reached edges. Node-based convex hulls overstate coverage across rivers/highways — prefer edge-based polygons. Always label the assumptions: mode, speed model, cutoff.
- OD matrix: many-to-many travel costs; the substrate for accessibility and location-allocation. For big matrices use pandana/r5py; store as Parquet with origin/destination IDs.
- Closest facility: k-nearest by network cost (not Euclidean); report both the assigned facility and the cost.
- Centrality: betweenness on travel_time (sampled
kfor big graphs — exact is O(nm)); edge betweenness ≈ through-traffic potential. Interpret as network structure, not observed traffic.
What ships with it
2 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.
- 10d ago First seen · 131 lines · 99 tokens per session scan A 07553d4b6be7
network-accessibility-analysis is a skill published in the GitHub repository muend/geoai-skills (15 stars, last pushed 6d ago), licensed MIT. It adds 99 tokens to every session and 1,508 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
read-memories
Search past Claude Code session logs to recover context from previous conversations. Finds past decisions, data paths, CRS info, model configurations, and unresolved work. Works across all projects or scoped to the current one.
detect-objects
Run pre-trained AI models on geospatial imagery. Detect buildings, cars, ships, solar panels, agriculture fields, or use text-prompted segmentation with GroundedSAM. Requires GPU for best performance.
inspect-geo
Inspect any raster or vector geospatial file. Returns CRS, bounds, bands, resolution, dtype, attribute summaries, and band statistics. Supports GeoTIFF, Shapefile, GeoJSON, GeoPackage, GeoParquet, and more.
process-raster
Process raster data: clip by bounding box, stack multiple bands, mosaic GeoTIFFs, or convert between raster and vector formats.
download-data
Download NAIP aerial imagery for a bounding box. Specify coordinates as minx,miny,maxx,maxy in WGS84 and optionally a year.
overture-data
Download Overture Maps data (buildings, places, roads, land use, water, etc.) for a bounding box. Returns a GeoDataFrame saved as GeoJSON or GeoPackage.