Import
asgeirtj/system_prompts_leaks
Handle explicit /import requests for read-only transcript recovery and a resume checkpoint, or continue work from other coding agents and unnamed artifacts.
Construct, inspect, import, and mutate GemPy input tables and structural frames; use this for surface points, orientations, elements, groups, IDs, colors, and input validation.
$ npx skills add VectorSpaceLab/AREX-Skill --skill data-and-structure -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill data-and-structure --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure .claude/skills/data-and-structure && 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 "data-and-structure" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure into .claude/skills/data-and-structure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-structure", 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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/gempy/sub-skills/data-and-structureType 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 VectorSpaceLab/AREX-Skill --skill data-and-structure -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill data-and-structure --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure .agents/skills/data-and-structure && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-and-structure" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure into .agents/skills/data-and-structure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-structure", 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 VectorSpaceLab/AREX-Skill --skill data-and-structure -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill data-and-structure --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure .cursor/skills/data-and-structure && 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 "data-and-structure" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure into .cursor/skills/data-and-structure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-structure", 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/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/gempy/sub-skills/data-and-structure--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 VectorSpaceLab/AREX-Skill --skill data-and-structure -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill data-and-structure --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure .gemini/skills/data-and-structure && 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 "data-and-structure" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure into .gemini/skills/data-and-structure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-structure", 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 VectorSpaceLab/AREX-Skill data-and-structureInstalls 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 VectorSpaceLab/AREX-Skill --skill data-and-structure -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure .github/skills/data-and-structure && 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 "data-and-structure" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure into .github/skills/data-and-structure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-structure", 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 VectorSpaceLab/AREX-Skill --skill data-and-structure -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill data-and-structure --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure .opencode/skills/data-and-structure && 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 "data-and-structure" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/gempy/sub-skills/data-and-structure into .opencode/skills/data-and-structure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-structure", 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.
data-and-structureConstruct, inspect, import, and mutate GemPy input tables and structural frames; use this for surface points, orientations, elements, groups, IDs, colors, and input validation.
Data And Structure is an agent skill from VectorSpaceLab/AREX-Skill. Construct, inspect, import, and mutate GemPy input tables and structural frames; use this for surface points, orientations, elements, groups, IDs, colors, and input validation.
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md`, `references/data-formats.md` and `references/troubleshooting.md`).
The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit ac3fe1a. 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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom 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.
Data And Structure loads about 2.2k tokens when it runs, and up to ~8.1k if it reads all its reference files. Until then it costs about 49 tokens; SKILL.md has 815 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); the scripts in this folder are not scanned.
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its Apache-2.0 licence (© VectorSpaceLab). 815 words, ~2,154 tokens.
.claude/skills/data-and-structure/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Use this sub-skill when a request concerns input data or geological organization, before model interpolation. A GemPy model stores input in this hierarchy:
GeoModel -> StructuralFrame -> StructuralGroup -> StructuralElement -> SurfacePointsTable + OrientationsTable.
A group is an ordered series/stack (or a fault group); an element is one named surface or fault. The frame's group order is geological structure, not merely a container order. Keep the same element names/IDs in surface points and orientations, and validate before computing.
modeling.grids-and-visualization..gempy, or use advanced mesh/plugin workflows: use
serialization-and-advanced.environment-and-troubleshooting.The public data API is available through gempy as gp and gp.data. The
recipes below use only caller-provided arrays, DataFrames, or file paths; they
do not require GemPy's source checkout or example data.
SurfacePointsTable and OrientationsTable from equal-length numeric
arrays, usually with from_arrays.name_id_map for both tables when IDs must be controlled;
otherwise let GemPy generate an opaque ID per distinct name.StructuralFrame.from_data_tables(surface_points, orientations) or start with StructuralFrame.initialize_default_structure()
for incremental GeoModel construction.frame.elements_names, element_id_name_map, counts, and the table
.df/.data before mapping or computing.gp.add_*/gp.modify_*, or replace a frame table through the
model setter. Re-run model.validate() and then route computation to
modeling.A minimal direct construction is:
import numpy as np
import gempy as gp
sp = gp.data.SurfacePointsTable.from_arrays(
x=np.array([0., 1., 0.]), y=np.array([0., 0., 1.]), z=np.array([0., 0., 0.]),
names="Layer",
)
ori = gp.data.OrientationsTable.from_arrays(
x=np.array([0.5]), y=np.array([0.5]), z=np.array([0.]),
G_x=np.array([0.]), G_y=np.array([0.]), G_z=np.array([1.]),
names=["Layer"], name_id_map=sp.name_id_map,
)
frame = gp.data.StructuralFrame.from_data_tables(sp, ori)from_data_tables creates one default erosional group and an element for each
surface-point ID. An element with no matching orientation receives an empty
orientation table; this is allowed as a data structure, but may be insufficient
for later model validation/computation.
# model must already contain an element named "Layer"
gp.add_surface_points(
model, x=[2., 3.], y=[0., 0.], z=[1., 1.],
elements_names=["Layer", "Layer"],
)
gp.add_orientations(
model, x=[2.], y=[0.], z=[1.], elements_names=["Layer"],
pole_vector=np.array([[0., 0., 1.]]),
)add_surface_points and add_orientations append rows to the named element
and return the model's StructuralFrame. All coordinate/name/pole/nugget
sequences must have the same length. An unknown element name raises
ValueError; neither add_* creates a new element. Create an element and put
it in a group first when adding a new surface.
add_orientations accepts a representation in one of these forms:
pole_vector: an (n, 3) array of gradient components [G_x, G_y, G_z].orientation: an (n, 3) array [azimuth_degrees, dip_degrees, polarity].Pass one representation. If both are supplied, the current implementation
converts orientation and ignores the supplied pole vector. A missing
representation raises ValueError. A missing nugget uses GemPy's
current defaults (0.01 for orientations and 0.00002 for surface points).
# Target by element name, or use a global row index/slice.
gp.modify_surface_points(model, elements_names=["Layer"], Z=np.array([1., 1., 2.]))
gp.modify_surface_points(model, slice=0, X=0.25, nugget=0.00002)
gp.modify_orientations(
model, slice=slice(0, 1),
G_x=np.array([0.0]), G_y=np.array([0.0]), G_z=np.array([1.0]),
)modify_surface_points accepts X, Y, Z, and nugget;
modify_orientations accepts those coordinates plus G_x, G_y, G_z, and
nugget. Scalars broadcast through NumPy structured-array assignment; arrays
must match the selected row count. Surface-point selection cannot specify both
elements_names and slice. These functions update the model through the
frame table setters; returned value is the updated StructuralFrame.
The current inspected GemPy release exposes angular keyword handling in
modify_orientations, but its implementation raises an unpacking ValueError
when azimuth, dip, or polarity is used. Treat angular modification as a
known compatibility gap: convert angles yourself and write G_x/G_y/G_z, or
recreate the orientation rows with add_orientations. Do not claim angular
modification works unless a later package version is verified.
gp.delete_surface_points() and gp.delete_orientations() are exported but
currently are zero-argument stubs that raise NotImplementedError; they do not
provide a selector. For a controlled row deletion, filter a copy and assign it
back, preserving the structured dtype:
sp = model.surface_points_copy
keep = sp.data["Z"] >= 0.0
sp.data = sp.data[keep]
model.surface_points = sp
ori = model.orientations_copy
keep_ori = ori.data["X"] != 2.0
ori.data = ori.data[keep_ori]
model.orientations = ori
model.validate()This is a low-level workaround: filter by the table's numeric fields or IDs, never by an assumed row order, and validate immediately. Removing the only observations can make the model empty or underdetermined.
Before handing off to modeling, check model.validate(). It raises
gempy.data.ModelValidationError, whose useful attributes are field,
reason, message, and context. Current semantic checks include:
empty_model);empty_non_fault_group or empty_fault_group);underdetermined_input);BASEMENT group before the final group (basement_relation_on_non_last_group).For fault groups, accessing the frame's fault descriptor also requires a square
fault-relation matrix with one row/column per structural group. For a failure,
inspect model.structural_frame.structural_groups, elements_names, table
lengths, IDs, and model.structural_frame.fault_relations; repair the structure
before calling compute. A successful data-table construction is not proof that
interpolation is determined.
Use the bundled table inspector for a caller-owned CSV pair:
python sub-skills/data-and-structure/scripts/inspect_tables.py \
--surface-points points.csv --orientations orientations.csvIt reports canonical columns, row counts, IDs, names, and finite-value checks
without modifying files. For numeric/shape errors, first convert inputs with
np.asarray(..., dtype=float), assert all row counts agree, and print
table.data.dtype/table.data.shape. For name/ID errors, pass the same
name_id_map to both constructors and compare it with
frame.element_name_id_map. For sparse inputs, add non-collinear surface
points and at least one orientation per interpolated element, then call
model.validate() again.
For more column-level contracts and structural relationships, read
references/data-formats.md and
references/api-reference.md. For failures,
read references/troubleshooting.md. For
persistence, route the already validated model to
serialization-and-advanced; do not serialize ad hoc table internals as a
replacement for model persistence.
© VectorSpaceLab, 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
SKILL.md and 4 other files (scripts, references) in skills/repositories/repo-skills/gempy/sub-skills/data-and-structure of VectorSpaceLab/AREX-Skill.
Open the folder on GitHubat commit ac3fe1a
Data And Structure 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 |
|---|---|---|---|---|---|---|
| Data And Structure this skillVectorSpaceLab/AREX-Skill | 328 | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Importasgeirtj/system_prompts_leaks | 69k | — | ~3.5k | Automated safety check: Pass | CC0-1.0 | |
| Node Inspect Debuggeropenclaw/openclaw | 392k | 1 repos | ~894 | Automated safety check: Pass | MIT | |
| Structured Datathedaviddias/Front-End-Checklist | 74k | — | ~420 | Automated safety check: Pass | MIT | |
| Table Fitasgeirtj/system_prompts_leaks | 69k | — | ~772 | Automated safety check: Pass | CC0-1.0 | |
| Bio Structural Biology Modern Structure PredictionFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | 1 repos | ~2.5k | Automated safety check: Pass | None |
asgeirtj/system_prompts_leaks
Handle explicit /import requests for read-only transcript recovery and a resume checkpoint, or continue work from other coding agents and unnamed artifacts.
openclaw/openclaw
Debug Node.js with node inspect, --inspect, breakpoints, CDP, heap, and CPU profiles.
thedaviddias/Front-End-Checklist
A skill your agent uses when auditing metadata, crawlability, structured data, or indexability related to Add structured data markup.
asgeirtj/system_prompts_leaks
Keep a Markdown table readable in a narrow terminal of about 100 display columns - a wide table or one carrying prose in its cells wraps into unreadable ragged rows.
FreedomIntelligence/OpenClaw-Medical-Skills
Predict protein structures using modern ML models including AlphaFold3, ESMFold, Chai-1, and Boltz-1.
nexu-io/open-design
Read an existing repository's structure into the project cwd as a normalised snapshot the agent can analyse without re-walking the tree on every turn.
VectorSpaceLab/AREX-Skill
Use this repo skill for Agent Lightning package tasks: authoring trainable agents, tracing rewards and spans, running LightningStore/Trainer loops, using agl CLI services, choosing examples, and…
VectorSpaceLab/AREX-Skill
A skill your agent uses when configuring LiteLLM for MCP tools, A2A agents, Claude Code/Cursor agent gateway traffic, MCP auth/OAuth, tool permissions, semantic filtering, or agent-specific proxy…
VectorSpaceLab/AREX-Skill
Build and debug DB-GPT agents, tools, skills, teams, and AWEL workflows, including deterministic local DAG runs and HTTP-trigger topology without assuming an LLM, credential, or external service.
VectorSpaceLab/AREX-Skill
Work on the actively maintained LangChain v1 agent package: initchatmodel, createagent, structured output, tools, middleware, embeddings initialization, provider routing, and agent runtime…
VectorSpaceLab/AREX-Skill
A skill your agent uses for giskard.agents async chat workflows, tools, prompt templates, structured outputs, retries, rate limiting, embeddings, and optional LiteLLM backend.
VectorSpaceLab/AREX-Skill
A skill your agent uses for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection.
Construct, inspect, import, and mutate GemPy input tables and structural frames; use this for surface points, orientations, elements, groups, IDs, colors, and input validation. Data And Structure is an agent skill from VectorSpaceLab/AREX-Skill. Construct, inspect, import, and mutate GemPy input tables and structural frames; use this for surface points, orientations, elements, groups, IDs, colors, and input validation.
Run `npx skills add VectorSpaceLab/AREX-Skill --skill data-and-structure -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/gempy/sub-skills/data-and-structure in VectorSpaceLab/AREX-Skill) into .claude/skills/data-and-structure in your project. Claude Code loads it when a task matches its description.
Run `npx skills add VectorSpaceLab/AREX-Skill --skill data-and-structure -a codex`. Or copy the skill folder (skills/repositories/repo-skills/gempy/sub-skills/data-and-structure in VectorSpaceLab/AREX-Skill) into .agents/skills/data-and-structure 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 VectorSpaceLab/AREX-Skill --skill data-and-structure -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-and-structure, .gemini/skills/data-and-structure, .github/skills/data-and-structure and .opencode/skills/data-and-structure in your project.
Going by SKILL.md and its folder, Data And Structure needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Data And Structure is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.2k tokens (SKILL.md is roughly 8.6k 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 5.9k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Data And Structure: Import (asgeirtj/system_prompts_leaks, 69k stars), Node Inspect Debugger (openclaw/openclaw, 392k stars), Structured Data (thedaviddias/Front-End-Checklist, 74k stars) and Table Fit (asgeirtj/system_prompts_leaks, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 157 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.