Exploratory Data Analysis
spacering-net/codeg
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.
Select an appropriate projected coordinate system for geographic data analysis or cartographic tasks.
$ npx skills add zzhonglei/GeoCode-Release --skill projection-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zzhonglei/GeoCode-Release projection-selection --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/zzhonglei/GeoCode-Release.git skills-src && mkdir -p .claude/skills && cp -r skills-src/contributions/projection-selection/skill .claude/skills/projection-selection && 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 "projection-selection" agent skill from https://github.com/zzhonglei/GeoCode-Release/tree/main/contributions/projection-selection/skill into .claude/skills/projection-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "projection-selection", 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/zzhonglei/GeoCode-Release/tree/main/contributions/projection-selection/skillType 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 zzhonglei/GeoCode-Release --skill projection-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zzhonglei/GeoCode-Release projection-selection --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zzhonglei/GeoCode-Release.git skills-src && mkdir -p .agents/skills && cp -r skills-src/contributions/projection-selection/skill .agents/skills/projection-selection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "projection-selection" agent skill from https://github.com/zzhonglei/GeoCode-Release/tree/main/contributions/projection-selection/skill into .agents/skills/projection-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "projection-selection", 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 zzhonglei/GeoCode-Release --skill projection-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zzhonglei/GeoCode-Release projection-selection --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zzhonglei/GeoCode-Release.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/contributions/projection-selection/skill .cursor/skills/projection-selection && 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 "projection-selection" agent skill from https://github.com/zzhonglei/GeoCode-Release/tree/main/contributions/projection-selection/skill into .cursor/skills/projection-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "projection-selection", 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/zzhonglei/GeoCode-Release.git --path contributions/projection-selection/skill--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 zzhonglei/GeoCode-Release --skill projection-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zzhonglei/GeoCode-Release projection-selection --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zzhonglei/GeoCode-Release.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/contributions/projection-selection/skill .gemini/skills/projection-selection && 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 "projection-selection" agent skill from https://github.com/zzhonglei/GeoCode-Release/tree/main/contributions/projection-selection/skill into .gemini/skills/projection-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "projection-selection", 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 zzhonglei/GeoCode-Release projection-selectionInstalls 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 zzhonglei/GeoCode-Release --skill projection-selection -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zzhonglei/GeoCode-Release.git skills-src && mkdir -p .github/skills && cp -r skills-src/contributions/projection-selection/skill .github/skills/projection-selection && 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 "projection-selection" agent skill from https://github.com/zzhonglei/GeoCode-Release/tree/main/contributions/projection-selection/skill into .github/skills/projection-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "projection-selection", 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 zzhonglei/GeoCode-Release --skill projection-selection -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zzhonglei/GeoCode-Release projection-selection --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zzhonglei/GeoCode-Release.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/contributions/projection-selection/skill .opencode/skills/projection-selection && 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 "projection-selection" agent skill from https://github.com/zzhonglei/GeoCode-Release/tree/main/contributions/projection-selection/skill into .opencode/skills/projection-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "projection-selection", 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.
projection-selectionSelect an appropriate projected coordinate system for geographic data analysis or cartographic tasks.
Projection Selection is an agent skill from zzhonglei/GeoCode-Release. Select an appropriate projected coordinate system for geographic data analysis or cartographic tasks. Consult this skill when you need to determine which projection to use. Before using this skill, you MUST first identify the latitude/longitude extent of the study area, as key parameters — such as zone numbers, standard parallels, and central meridians — all depend on its location and extent. To obtain the precise extent, you can search for relevant reference materials or create a boundary vector file (e.g…
Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/中国制图投影坐标系规范.md`).
It sits in Data & Analytics, covering Data analysis. The repository describes itself as: A desktop AI assistant for geoscience data processing. The licence is MIT.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 6e3534f. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Projection Selection loads about 4.1k tokens when it runs, and up to ~6.4k if it reads all its reference files. Until then it costs about 143 tokens; SKILL.md has 1,409 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.
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); files beside SKILL.md are not scanned.
The full file from zzhonglei/GeoCode-Release at commit 6e3534f, republished under its MIT licence (© zzhonglei). 1,409 words, ~4,058 tokens.
.claude/skills/projection-selection/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.This skill is a built-in skill of GeoAgent. Please read all the following content carefully and strictly follow the guidelines during task execution.
This skill provides a comprehensive knowledge framework for selecting projected coordinate systems, helping you make sound projection choices when faced with geographic data analysis or cartographic tasks.
Prerequisite: You can only begin selecting a projection once the study area's latitude/longitude extent is known. Many projection parameters (such as UTM/Gauss-Krüger zone numbers, conic projection standard parallels, central meridians, etc.) depend on the specific location and extent of the study area — without this information, a correct projection choice cannot be made.
This is the first decision dimension for projection selection — what geometric property does your task need to preserve?
| Distortion Property | What It Preserves | What It Sacrifices | Typical Use Cases |
|---|---|---|---|
| Conformal | Local shapes and angles | Area distortion (high-latitude regions are enlarged) | Navigation, weather maps, ocean current maps, wind field maps, any direction-critical scenario |
| Equal-area | Correct area proportions for any region | Shape distortion (regions far from standard lines are compressed or stretched) | Population density, land use, precipitation distribution, any statistical thematic map requiring area comparison |
| Equidistant | Distances from a given point or line | Both angles and areas are distorted | Distance analysis from a city, communication coverage, airline distance maps |
| Compromise | Nothing strictly, but overall visual balance | All properties have slight distortion | General-purpose display maps, educational maps, world maps in publications |
This is the second decision dimension for projection selection — how large is your map's coverage, what shape is the region, and at what latitude is it located?
Projections are classified into families by the type of geometric surface, each naturally suited to different regional shapes:
Project the earth onto a cylinder wrapped around it, then unroll.
Common projections:
| Projection Name | Distortion Property | Use Cases |
|---|---|---|
| Mercator | Conformal | Marine navigation, web maps (not suitable for global area comparison) |
| Transverse Mercator | Conformal | North-south elongated small regions (basis of UTM, Gauss-Krüger) |
| Equal-Area Cylindrical | Equal-area | Area statistics near the equator |
| Plate Carrée (Equidistant Cylindrical) | Equidistant (along meridians) | Quick display, default coordinates for data exchange |
Project the earth onto a cone placed over it, then unroll.
Common projections:
| Projection Name | Distortion Property | Use Cases |
|---|---|---|
| Lambert Conformal Conic | Conformal | Mid-latitude country/continent mapping (weather, aviation) |
| Albers Equal-Area Conic | Equal-area | Statistical thematic maps for mid-latitude countries/continents |
| Equidistant Conic | Equidistant | Distance measurement in mid-latitude regions |
Project the earth onto a plane tangent to a single point.
Common projections:
| Projection Name | Distortion Property | Use Cases |
|---|---|---|
| Stereographic | Conformal | Polar mapping, precise local area mapping |
| Lambert Azimuthal Equal-Area | Equal-area | Area statistics centered on a point (e.g., continental maps) |
| Azimuthal Equidistant | Equidistant (from center point) | Distance display from a city, airline route maps |
Variants of cylindrical projections where parallels remain straight lines but meridians curve.
Common projections:
| Projection Name | Distortion Property | Use Cases |
|---|---|---|
| Mollweide | Equal-area | Global distribution area statistics thematic maps |
| Robinson | Compromise | General-purpose global display maps |
| Sinusoidal | Equal-area | Global area statistics for low-latitude regions |
| Natural Earth | Compromise | Aesthetically pleasing global display maps |
| Equal Earth | Equal-area | Aesthetically balanced global equal-area thematic maps (recommended) |
Projection selection is not purely a technical issue — it also depends on the purpose of what you're doing. GIS analysis and cartographic mapping have different logics for projection requirements.
Important: A single task can use multiple projections. Within the same project, the GIS analysis phase and the final cartographic phase can use entirely different projections. For example: use an equal-area projection for area statistical analysis, then use a projection conforming to local cartographic standards for the final map output. Don't try to use a single projection for all stages — instead, choose the most suitable projection for each stage based on its purpose.
In GIS spatial analysis, projection selection should fully serve the analytical task's requirements:
| Analysis Task | Property to Preserve | Projection Type to Use | Example |
|---|---|---|---|
| Area calculation, density analysis | Area | Equal-area projection | Calculate forest cover area by country |
| Distance/buffer analysis | Distance | Equidistant projection or UTM/Gauss-Krüger | Calculate distance from city to coastline |
| Direction/angle analysis | Angles | Conformal projection | Analyze wind directions, ocean currents |
| Shape analysis | Local shape | Conformal projection | Terrain feature identification |
| Small-area comprehensive analysis | Balanced properties | UTM / Gauss-Krüger | Multi-dimensional spatial analysis at city level |
Principle: Accuracy of analytical results comes first; visual aesthetics don't matter.
Projection selection in cartographic mapping is more complex, requiring simultaneous consideration of the following factors:
1. Local Cartographic Standards
Many countries and regions have official cartographic projection standards. When mapping a specific area, local standards should take priority:
2. Thematic Map Subject Characteristics
Projection selection should serve thematic expression:
3. Comprehensive Trade-offs
When standard requirements and thematic needs conflict, cartographic standards generally take priority — because standards ensure data comparability and interoperability. When there are no explicit standard constraints, thematic expression needs take precedence.
Note: When standards and thematic needs are hard to reconcile, proactively ask the user: Do they need to follow specific cartographic standards? Do they prioritize accurate thematic data expression or overall visual aesthetics? Use this as the basis for projection selection decisions.
The following lists recommended projection schemes for high-frequency mapping scenarios as a quick reference:
| Scenario | Recommended Projection | Rationale |
|---|---|---|
| Global statistical thematic maps (population, climate, etc.) | Equal Earth / Mollweide | Equal-area, ensures accurate area comparison |
| Global general display maps | Robinson / Natural Earth | Compromise, visually natural and balanced |
| Global ocean/route maps | Mercator | Conformal, directions and routes are correct |
| Scenario | Recommended Projection | Rationale |
|---|---|---|
| Continental equal-area thematic maps | Albers Equal-Area Conic / Lambert Azimuthal Equal-Area | Equal-area, suitable for large mid-latitude areas |
| Continental conformal mapping | Lambert Conformal Conic | Conformal, accurate shapes |
| Polar regions | Stereographic (conformal) / Lambert Azimuthal Equal-Area (equal-area) | Azimuthal projections are naturally suited for poles |
| Scenario | Recommended Projection | Rationale |
|---|---|---|
| Mid-latitude country thematic maps (E-W extent) | Albers Equal-Area Conic / Lambert Conformal Conic | Conic projections suit mid-latitude E-W regions |
| Equatorial country thematic maps | Mercator / Cylindrical Equal-Area | Cylindrical projections suit equatorial regions |
| North-south elongated countries (e.g., Chile) | Transverse Mercator | Transverse cylindrical suits N-S elongated regions |
| Scenario | Recommended Projection | Rationale |
|---|---|---|
| City-level precise mapping/analysis | UTM / Gauss-Krüger | All types of distortion are negligible at small scales |
| Distance display centered on a point | Azimuthal Equidistant | Accurate distances from center point |
| File | When to Read |
|---|---|
references/中国制图投影坐标系规范.md | When the task involves any part of China. — read it before selecting a projection for China. |
© zzhonglei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file (references) in contributions/projection-selection/skill of zzhonglei/GeoCode-Release.
Open the folder on GitHubat commit 6e3534f
Projection Selection 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 |
|---|---|---|---|---|---|---|
| Projection Selection this skillzzhonglei/GeoCode-Release | 189 | — | ~4.1k | Automated safety check: Pass | MIT | |
| Exploratory Data Analysisspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Excel and CSV Data Analysisbytedance/deer-flow | 84k | 4 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Exploratory Data AnalysisOleafly/Oleafly | 212 | 2 repos | ~3.4k | Automated safety check: Notes | MIT | |
| Pandas ProJeffallan/claude-skills | 12k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Python Executorcortega26/chile-hub | 113 | 2 repos | ~1.5k | Automated safety check: Pass | MIT |
spacering-net/codeg
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bytedance/deer-flow
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Oleafly/Oleafly
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cortega26/chile-hub
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh).
FrankS-IntelLab/agentic-kaggle-skill
Takes a Kaggle competition from rules and validation design through baselines, ensembling and notebook architecture to a scored submission.
zzhonglei/GeoCode-Release
Create well-designed maps that follow standard cartographic conventions.
zzhonglei/GeoCode-Release
Create, edit, analyze, or convert Excel spreadsheets (.xlsx, .xlsm) where the workbook file is the primary deliverable.
zzhonglei/GeoCode-Release
Find, download, and prepare official thematic statistics from the National Bureau of Statistics of China.
zzhonglei/GeoCode-Release
GEE scripting and remote sensing analysis workflow guide. An agent skill from zzhonglei/GeoCode-Release.
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zzhonglei/GeoCode-Release
Sample skill verifying the GeoCode skill store pipeline end-to-end.
Categories
Select an appropriate projected coordinate system for geographic data analysis or cartographic tasks. Projection Selection is an agent skill from zzhonglei/GeoCode-Release. Select an appropriate projected coordinate system for geographic data analysis or cartographic tasks.
Projection Selection fits situations like: tasks that involve Data analysis.
Run `npx skills add zzhonglei/GeoCode-Release --skill projection-selection -a claude-code`. Or copy the skill folder (contributions/projection-selection/skill in zzhonglei/GeoCode-Release) into .claude/skills/projection-selection in your project. Claude Code loads it when a task matches its description.
Run `npx skills add zzhonglei/GeoCode-Release --skill projection-selection -a codex`. Or copy the skill folder (contributions/projection-selection/skill in zzhonglei/GeoCode-Release) into .agents/skills/projection-selection in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zzhonglei/GeoCode-Release --skill projection-selection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/projection-selection, .gemini/skills/projection-selection, .github/skills/projection-selection and .opencode/skills/projection-selection in your project.
SKILL.md names no scripts, command-line tools or credentials: Projection Selection is instructions for the agent only.
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.
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. Review the folder before installing.
Projection Selection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Projection Selection: Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 84k stars), Exploratory Data Analysis (Oleafly/Oleafly, 212 stars) and Pandas Pro (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
zzhonglei (a GitHub user) maintains it in zzhonglei/GeoCode-Release, which has 189 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on October 4, 2026.
Source: zzhonglei/GeoCode-Release on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.