Wayfinding Physical Spatial Metaphors
hashgraph-online/awesome-codex-plugins
A skill your agent uses when deciding whether (and how) to borrow physical-world wayfinding metaphors for software — rooms, doors, paths, maps, landmarks.
A skill your agent uses when the marathon plan needs a 26.2-mile physical route generated from road-network GeoJSON, or when the request mentions traffic impact, route variety via seed, finishing…
Apache-2.0Auto-check passed
Install Gis Spatial Engineering
$ npx skills add GoogleCloudPlatform/race-condition --skill gis-spatial-engineering -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GoogleCloudPlatform/race-condition gis-spatial-engineering --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/race-condition.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agents/planner/skills/gis-spatial-engineering .claude/skills/gis-spatial-engineering && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "gis-spatial-engineering" agent skill from https://github.com/GoogleCloudPlatform/race-condition/tree/main/agents/planner/skills/gis-spatial-engineering into .claude/skills/gis-spatial-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gis-spatial-engineering", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GoogleCloudPlatform/race-condition/tree/main/agents/planner/skills/gis-spatial-engineeringType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GoogleCloudPlatform/race-condition --skill gis-spatial-engineering -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GoogleCloudPlatform/race-condition gis-spatial-engineering --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/race-condition.git skills-src && mkdir -p .agents/skills && cp -r skills-src/agents/planner/skills/gis-spatial-engineering .agents/skills/gis-spatial-engineering && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gis-spatial-engineering" agent skill from https://github.com/GoogleCloudPlatform/race-condition/tree/main/agents/planner/skills/gis-spatial-engineering into .agents/skills/gis-spatial-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gis-spatial-engineering", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GoogleCloudPlatform/race-condition --skill gis-spatial-engineering -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GoogleCloudPlatform/race-condition gis-spatial-engineering --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/race-condition.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/agents/planner/skills/gis-spatial-engineering .cursor/skills/gis-spatial-engineering && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gis-spatial-engineering" agent skill from https://github.com/GoogleCloudPlatform/race-condition/tree/main/agents/planner/skills/gis-spatial-engineering into .cursor/skills/gis-spatial-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gis-spatial-engineering", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GoogleCloudPlatform/race-condition.git --path agents/planner/skills/gis-spatial-engineering--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GoogleCloudPlatform/race-condition --skill gis-spatial-engineering -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GoogleCloudPlatform/race-condition gis-spatial-engineering --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/race-condition.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/agents/planner/skills/gis-spatial-engineering .gemini/skills/gis-spatial-engineering && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gis-spatial-engineering" agent skill from https://github.com/GoogleCloudPlatform/race-condition/tree/main/agents/planner/skills/gis-spatial-engineering into .gemini/skills/gis-spatial-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gis-spatial-engineering", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GoogleCloudPlatform/race-condition gis-spatial-engineeringInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GoogleCloudPlatform/race-condition --skill gis-spatial-engineering -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/race-condition.git skills-src && mkdir -p .github/skills && cp -r skills-src/agents/planner/skills/gis-spatial-engineering .github/skills/gis-spatial-engineering && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gis-spatial-engineering" agent skill from https://github.com/GoogleCloudPlatform/race-condition/tree/main/agents/planner/skills/gis-spatial-engineering into .github/skills/gis-spatial-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gis-spatial-engineering", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GoogleCloudPlatform/race-condition --skill gis-spatial-engineering -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GoogleCloudPlatform/race-condition gis-spatial-engineering --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/race-condition.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/agents/planner/skills/gis-spatial-engineering .opencode/skills/gis-spatial-engineering && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gis-spatial-engineering" agent skill from https://github.com/GoogleCloudPlatform/race-condition/tree/main/agents/planner/skills/gis-spatial-engineering into .opencode/skills/gis-spatial-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gis-spatial-engineering", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
gis-spatial-engineering- GitHub stars
- 234
- Token cost
- ~672 tokens
- SKILL.md length
- 282 words
- Files
- 4 (incl. scripts, assets)
- Skills in repo
- 16
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when the marathon plan needs a 26.2-mile physical route generated from road-network GeoJSON, or when the request mentions traffic impact, route variety via seed, finishing…
- Works in 3 steps: Just call it: plan_marathon_route() with… → Precision: The tool uses interpolation… → GeoJSON: Input must be valid road…
- The marathon plan needs a 26.2-mile physical route generated from road-network GeoJSON
- SKILL.md covers Geographic Context (Built-in…, Algorithm, Instructions and Tools
- Runs Python scripts from its folder
What it does
Gis Spatial Engineering is an agent skill from GoogleCloudPlatform/race-condition. Use when the marathon plan needs a 26.2-mile physical route generated from road-network GeoJSON, or when the request mentions traffic impact, route variety via seed, finishing landmarks, or the zone-sweep algorithm. Triggered by route-planning or traffic-analysis language.
Its SKILL.md is about 670 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and assets (for example `__init__.py`, `assets/network.json` and `scripts/tools.py`).
The repository describes itself as: The open source multi-agent simulation from the Developer Keynote at Google Cloud Next '26. A deployable reference architecture for autonomous AI agents using Gemini and ADK. The licence is Apache-2.0.
When your agent uses it
- The marathon plan needs a 26.2-mile physical route generated from road-network GeoJSON
- The request mentions traffic impact
- Route variety via seed
- Finishing landmarks
Example prompts
- “/gis-spatial-engineering”
Requirements
- Python 3
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Just call it: plan_marathon_route() with no arguments produces a valid
- Precision: The tool uses interpolation to guarantee exactly 26.2 miles.
- GeoJSON: Input must be valid road network GeoJSON.
What it can do on your machine
Read from SKILL.md and the folder at commit 26efc1b. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Gis Spatial Engineering loads about 672 tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 282 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from GoogleCloudPlatform/race-condition at commit 26efc1b, republished under its Apache-2.0 licence (© GoogleCloudPlatform). 282 words, ~672 tokens.
Download SKILL.mdSave it as
.claude/skills/gis-spatial-engineering/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- gis-spatial-engineering
- description
- Use when the marathon plan needs a 26.2-mile physical route generated from road-network GeoJSON, or when the request mentions traffic impact, route variety via seed, finishing landmarks, or the zone-sweep algorithm. Triggered by route-planning or traffic-analysis language.
- license
- Apache-2.0
- metadata.adk_additional_tools
- plan_marathon_route, report_marathon_route, assess_traffic_impact
GIS Spatial Engineering
You use this skill to generate the physical path of the marathon.
Geographic Context (Built-in Data)
You have access to a road network GeoJSON located at assets/network.json.
Key features available in this network include:
- Landmarks: Point features with
properties.name(e.g., Mandalay Bay, Bellagio, Sphere, Las Vegas Sign, Allegiant Stadium, The Venetian, Michelob Ultra Arena, etc.). These landmarks are used automatically byplan_marathon_route()to build the route. - Named Roads: All 34 LineString features have
properties.name(e.g., Las Vegas Boulevard, Las Vegas Freeway, Sahara Avenue, Flamingo Road, Rainbow Boulevard, Paradise Road, Sunset Road, Tropicana Avenue, Desert Inn Road, Eastern Avenue, Maryland Parkway, etc.).
Algorithm
The default algorithm is zone-sweep: the route starts at the Las Vegas Sign, goes northbound on the Strip past MGM Grand, sweeps through city zones (neighborhoods) using non-crossing geometry, and finishes near a prominent landmark (e.g., Michelob Ultra Arena). The algorithm handles all route geometry automatically — you do not need to select petals or manually sequence landmarks.
Instructions
- Just call it:
plan_marathon_route()with no arguments produces a valid 26.2-mile zone-sweep route. Usefinish_landmarkandseedfor variety. - Precision: The tool uses interpolation to guarantee exactly 26.2 miles.
- GeoJSON: Input must be valid road network GeoJSON.
Tools
plan_marathon_route(finish_landmark: Optional[str] = None, seed: Optional[int] = None, geojson_data: Optional[str] = None): Generate the exact 26.2-mile path.finish_landmark: Name of a landmark to finish near (e.g.,"Michelob Ultra Arena").seed: Integer seed for route variety. Different seeds produce different routes.
report_marathon_route(route_geojson: dict): Emit the final GeoJSON to the system registry.
Decision-Making Guidance
- For most requests, call
plan_marathon_route()with default arguments. - Use
seedto generate alternative routes when the user wants variety. - Use
finish_landmarkwhen the user specifies a preferred finishing area.
© GoogleCloudPlatform, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (scripts, assets) in agents/planner/skills/gis-spatial-engineering of GoogleCloudPlatform/race-condition.
- SKILL.md
- __init__.py
- assets/network.json
- scripts/tools.py
Open the folder on GitHubat commit 26efc1b
Compare with similar skills
Gis Spatial Engineering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Gis Spatial Engineering this skillGoogleCloudPlatform/race-condition | 234 | — | ~672 | Automated safety check: Pass | Apache-2.0 | |
| Wayfinding Physical Spatial Metaphorshashgraph-online/awesome-codex-plugins | 1.2k | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Physical Addressthedaviddias/Front-End-Checklist | 74k | — | ~574 | Automated safety check: Pass | MIT | |
| Bio Spatial Transcriptomics Spatial MultiomicsFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | 1 repos | ~1.6k | Automated safety check: Pass | None | |
| Bio Spatial Transcriptomics Spatial ProteomicsFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | 1 repos | ~976 | Automated safety check: Pass | None | |
| Spatial Designsickn33/agentic-awesome-skills | 47k | 1 repos | ~2.6k | Automated safety check: Pass | MIT |
Similar skills
Physical Address
thedaviddias/Front-End-Checklist
A skill your agent uses when auditing local business websites, e-commerce sites, or any site where a physical presence affects trust or local search visibility.
Bio Spatial Transcriptomics Spatial Multiomics
FreedomIntelligence/OpenClaw-Medical-Skills
Analyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD.
Bio Spatial Transcriptomics Spatial Proteomics
FreedomIntelligence/OpenClaw-Medical-Skills
Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization.
Spatial Design
sickn33/agentic-awesome-skills
Web and App implementation guide for Spatial Design. An agent skill from sickn33/agentic-awesome-skills.
Bio Spatial Transcriptomics Spatial Statistics
GPTomics/bioSkills
Detects spatially variable genes, spatial autocorrelation, and cell-type colocalization for spatial transcriptomics using Squidpy with PySAL/esda for local statistics.
More from GoogleCloudPlatform/race-condition
All 16 skills in this repoA2ui Rendering
GoogleCloudPlatform/race-condition
A skill your agent uses when an agent renders rich UI back to a client surface (cards, dashboards, forms, modals) using the A2UI v0.8.0 declarative protocol.
Contributing
GoogleCloudPlatform/race-condition
Guides the developer workflow for contributing to Race Condition.
Deploying
GoogleCloudPlatform/race-condition
Guides deployment of Race Condition to a GCP project. An agent skill from GoogleCloudPlatform/race-condition.
Exploring The Codebase
GoogleCloudPlatform/race-condition
Explains the Race Condition architecture, the design decisions behind it, and where to read code first.
Getting Started
GoogleCloudPlatform/race-condition
Guides setup of the Race Condition project from clone to running simulation.
Preparing The Race
GoogleCloudPlatform/race-condition
A skill your agent uses when the simulator receives a validated plan from the planner and must initialize the race: parsing the plan, spawning runner agents, starting the telemetry collector, and…
Questions about Gis Spatial Engineering
What does Gis Spatial Engineering do?
A skill your agent uses when the marathon plan needs a 26.2-mile physical route generated from road-network GeoJSON, or when the request mentions traffic impact, route variety via seed, finishing…. Gis Spatial Engineering is an agent skill from GoogleCloudPlatform/race-condition.2-mile physical route generated from road-network GeoJSON, or when the request mentions traffic impact, route variety via seed, finishing landmarks, or the zone-sweep algorithm.
When should I use Gis Spatial Engineering?
Gis Spatial Engineering fits situations like: the marathon plan needs a 26.2-mile physical route generated from road-network GeoJSON; the request mentions traffic impact; route variety via seed; finishing landmarks.
How do I install Gis Spatial Engineering in Claude Code?
Run `npx skills add GoogleCloudPlatform/race-condition --skill gis-spatial-engineering -a claude-code`. Or copy the skill folder (agents/planner/skills/gis-spatial-engineering in GoogleCloudPlatform/race-condition) into .claude/skills/gis-spatial-engineering in your project. Claude Code loads it when a task matches its description.
How do I install Gis Spatial Engineering in Codex?
Run `npx skills add GoogleCloudPlatform/race-condition --skill gis-spatial-engineering -a codex`. Or copy the skill folder (agents/planner/skills/gis-spatial-engineering in GoogleCloudPlatform/race-condition) into .agents/skills/gis-spatial-engineering in your project. Codex loads it when a task matches its description.
Can I use Gis Spatial Engineering in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GoogleCloudPlatform/race-condition --skill gis-spatial-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gis-spatial-engineering, .gemini/skills/gis-spatial-engineering, .github/skills/gis-spatial-engineering and .opencode/skills/gis-spatial-engineering in your project.
What does Gis Spatial Engineering need to run?
Going by SKILL.md and its folder, Gis Spatial Engineering needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Gis Spatial Engineering access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Gis Spatial Engineering safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Gis Spatial Engineering use?
Gis Spatial Engineering is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Gis Spatial Engineering use?
About 672 tokens (SKILL.md is roughly 2.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Gis Spatial Engineering?
Skills that share tags, products or a category with Gis Spatial Engineering: Wayfinding Physical Spatial Metaphors (hashgraph-online/awesome-codex-plugins, 1.2k stars), Physical Address (thedaviddias/Front-End-Checklist, 74k stars), Bio Spatial Transcriptomics Spatial Multiomics (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Bio Spatial Transcriptomics Spatial Proteomics (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Gis Spatial Engineering?
GoogleCloudPlatform (a GitHub organization) maintains it in GoogleCloudPlatform/race-condition, which has 234 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 5, 2026.
Source: GoogleCloudPlatform/race-condition on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.