Agent skill

Create Data Inventory

by NeuroAIHub in NeuroAIHub/BrainPilot

Create or update the canonical Markdown inventory of task-relevant research data.

AGPL-3.0Auto-check passedData & Analytics

Install Create Data Inventory

skills CLI
$ npx skills add NeuroAIHub/BrainPilot --skill create-data-inventory -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NeuroAIHub/BrainPilot create-data-inventory --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/plugin-research/skills/create-data-inventory .claude/skills/create-data-inventory && rm -rf skills-src

Use ~/.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/

Facts

Skill name
create-data-inventory
GitHub stars
1.1k
Token cost
~579 tokens
SKILL.md length
271 words
Files
2
Skills in repo
59
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Create or update the canonical Markdown inventory of task-relevant research data.

  • Tasks that involve Data governance
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Create Data Inventory is an agent skill from NeuroAIHub/BrainPilot. Create or update the canonical Markdown inventory of task-relevant research data. Engineer must invoke this skill before creating or updating any data inventory, data contract, or dataset-coverage summary that downstream agents will use.

Its SKILL.md is about 580 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Data & Analytics, covering Data governance. The repository describes itself as: BrainPilot: Automating Brain Discovery with Agentic Research. The licence is AGPL-3.0.

When your agent uses it

  • Tasks that involve Data governance

Example prompts

  • “/create-data-inventory”

What it can do on your machine

Read from SKILL.md and the folder at commit 93f6855. 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

    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.

  • 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

Create Data Inventory loads about 579 tokens when it runs. Until then it costs about 65 tokens; SKILL.md has 271 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~579

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from NeuroAIHub/BrainPilot at commit 93f6855, republished under its AGPL-3.0 licence (© NeuroAIHub). 271 words, ~579 tokens.

Download SKILL.mdSave it as .claude/skills/create-data-inventory/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
create-data-inventory
description
Create or update the canonical Markdown inventory of task-relevant research data. Engineer must invoke this skill before creating or updating any data inventory, data contract, or dataset-coverage summary that downstream agents will use.

Create Data Inventory

Inspect the task's authorized data locations before formal analysis, modelling, training, or evaluation. Use bounded metadata and representative-content checks to identify every task-relevant dataset, subject, session, run, partition, and label source that is visible within the assigned scope. Reconcile counts across filenames, metadata, labels, and loaded shapes; state unresolved discrepancies instead of guessing.

Write one canonical Markdown document at docs/specs/data-inventory.md. Create the parent directory when needed. If the document already exists, read it first and revise it in place so downstream agents retain one authoritative path.

Include:

  • the task scope and every location inspected;
  • a table of discovered data sources with paths, formats, sizes or record counts, subjects, sessions, runs, trials or other natural grouping units;
  • loaded structure where relevant: shapes, axes, dtypes, value domains, feature ordering, labels and label mappings;
  • documented or observed split relationships and grouping boundaries;
  • reconciled totals and the checks used to obtain them;
  • missing metadata, ambiguous mappings, unreadable inputs, discrepancies, and any parts of the assigned scope that could not be inspected;
  • concise implications that Experimentalist, Librarian, Engineer, and Auditor must preserve when designing or reviewing later work.

Keep the inventory descriptive. Do not train models, choose methods, freeze hyperparameters, or treat inventory inspection as empirical validation. Link supporting metadata or inspection outputs when they are useful, but keep the inventory self-contained enough that another agent can use it without repeating the discovery work.

Before handoff, verify that every discovered task-relevant grouping is accounted for, totals agree or are explicitly unresolved, and every referenced path exists. Return docs/specs/data-inventory.md as the primary artifact and direct dependent agents to read it before using the data.

© NeuroAIHub, AGPL-3.0. 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 1 other file in packages/plugin-research/skills/create-data-inventory of NeuroAIHub/BrainPilot.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 93f6855

Compare with similar skills

Create Data Inventory 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.

Create Data Inventory compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Create Data Inventory this skillNeuroAIHub/BrainPilot1.1k—~579Automated safety check: PassAGPL-3.0
Jeecg Systemjeecgboot/skills239—~3.2kAutomated safety check: PassApache-2.0
Openalgo Chart Indicatormarketcalls/openalgo-charts140—~3.4kAutomated safety check: NotesApache-2.0
Data Quality Frameworkswshobson/agents40k11 repos~1.1kAutomated safety check: PassMIT
Tracing Downstream Lineageastronomer/agents4511 repos~1.2kAutomated safety check: PassApache-2.0
Tracing Upstream Lineageastronomer/agents4511 repos~1.1kAutomated safety check: PassApache-2.0

Similar skills

  • Jeecg System

    jeecgboot/skills

    JeecgBoot 系统主数据查询与管理。Use when user asks to query/create/manage system master data, or says "查询角色", "查询用户", "查询部门", "查询字典", "创建字典", "创建角色", "查岗位", "查职务", "查租户", "查数据源", "查定时任务", "系统主数据", "query…

    239 GitHub stars~3.2k tokensUpdated 21 days ago
    Data & AnalyticsAuto-check passed
  • Openalgo Chart Indicator

    marketcalls/openalgo-charts

    Add a built-in openalgo-charts indicator, restyle it, build a settings UI from its descriptor, or author a custom indicator with registerIndicator or the Tier-2 external-data contract.

    140 GitHub stars~3.4k tokensUpdated 7 days ago
    Data & AnalyticsAuto-check: notes
  • Sets up data quality checks with Great Expectations, dbt tests and data contracts, with checkpoints and pass-fail reports for pipelines.

    40k GitHub starsUsed in 11 repos~1.1k tokens
    Data & AnalyticsAuto-check passed
  • Trace downstream data lineage and impact analysis. An agent skill from astronomer/agents.

    451 GitHub starsUsed in 1 repo~1.2k tokens
    Data & AnalyticsAuto-check passed
  • Tracing Upstream Lineage

    astronomer/agents

    Trace upstream data lineage. An agent skill from astronomer/agents.

    451 GitHub starsUsed in 1 repo~1.1k tokens
    Data & AnalyticsAuto-check passed
  • Finds usable public datasets, judges whether the data can support a research idea, and checks train and test splits for leakage before results are trusted.

    641 GitHub stars~4.9k tokensUpdated 3 mo ago
    Data & AnalyticsAuto-check passed

More from NeuroAIHub/BrainPilot

All 59 skills in this repo
  • Deeplabcut

    NeuroAIHub/BrainPilot

    Toolbox for markerless animal pose estimation with DeepLabCut.

    1.1k GitHub stars~1.7k tokensUpdated 6 days ago
    Auto-check passed
  • Fmriprep

    NeuroAIHub/BrainPilot

    Preprocess task-based or resting-state fMRI data with fMRIPrep — a robust, BIDS-App preprocessing pipeline built on FSL, ANTs, FreeSurfer, AFNI, and Nilearn.

    1.1k GitHub stars~4.1k tokensUpdated 6 days ago
    Auto-check passed
  • Mne Python Guide

    NeuroAIHub/BrainPilot

    Domain-validated pipeline guidance for EEG/MEG data analysis using MNE-Python: data loading, preprocessing (filtering, ICA, re-referencing), epoching, ERP/ERF computation, time-frequency…

    1.1k GitHub stars~2.3k tokensUpdated 6 days ago
    Auto-check passed
  • Netneurotools Guide

    NeuroAIHub/BrainPilot

    Domain-validated guidance for network neuroscience analysis using netneurotools: datasets, brain network metrics, connectivity consensus, modularity, spatial statistics, null models, and cortical…

    1.1k GitHub stars~2.6k tokensUpdated 6 days ago
    Auto-check passed
  • Nature Figure

    NeuroAIHub/BrainPilot

    Submission-grade Nature/high-impact journal figure workflow for Python or R.

    1.1k GitHub starsUsed in 1 repo~1.3k tokens
    Auto-check passed
  • Pycortex Guide

    NeuroAIHub/BrainPilot

    Domain-validated guidance for cortical surface visualization and brain surface rendering of fMRI data using pycortex: data types (Volume, Vertex, Dataset), 2D cortical flatmaps, 3D WebGL brain…

    1.1k GitHub stars~1.6k tokensUpdated 6 days ago
    Auto-check passed

Questions about Create Data Inventory

What does Create Data Inventory do?

Create or update the canonical Markdown inventory of task-relevant research data. Create Data Inventory is an agent skill from NeuroAIHub/BrainPilot. Create or update the canonical Markdown inventory of task-relevant research data.

When should I use Create Data Inventory?

Create Data Inventory fits situations like: tasks that involve Data governance.

How do I install Create Data Inventory in Claude Code?

Run `npx skills add NeuroAIHub/BrainPilot --skill create-data-inventory -a claude-code`. Or copy the skill folder (packages/plugin-research/skills/create-data-inventory in NeuroAIHub/BrainPilot) into .claude/skills/create-data-inventory in your project. Claude Code loads it when a task matches its description.

How do I install Create Data Inventory in Codex?

Run `npx skills add NeuroAIHub/BrainPilot --skill create-data-inventory -a codex`. Or copy the skill folder (packages/plugin-research/skills/create-data-inventory in NeuroAIHub/BrainPilot) into .agents/skills/create-data-inventory in your project. Codex loads it when a task matches its description.

Can I use Create Data Inventory 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 NeuroAIHub/BrainPilot --skill create-data-inventory -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/create-data-inventory, .gemini/skills/create-data-inventory, .github/skills/create-data-inventory and .opencode/skills/create-data-inventory in your project.

What does Create Data Inventory need to run?

SKILL.md names no scripts, command-line tools or credentials: Create Data Inventory is instructions for the agent only.

Does Create Data Inventory 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 Create Data Inventory 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. Review the folder before installing.

What licence does Create Data Inventory use?

Create Data Inventory is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Create Data Inventory use?

About 579 tokens (SKILL.md is roughly 2.3k 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 Create Data Inventory?

Skills that share tags, products or a category with Create Data Inventory: Jeecg System (jeecgboot/skills, 239 stars), Openalgo Chart Indicator (marketcalls/openalgo-charts, 140 stars), Data Quality Frameworks (wshobson/agents, 40k stars) and Tracing Downstream Lineage (astronomer/agents, 451 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Create Data Inventory?

NeuroAIHub (a GitHub organization) maintains it in NeuroAIHub/BrainPilot, which has 1,060 GitHub stars. The repository holds 59 skills in this directory. The repository was last updated on October 2, 2026.

Source: NeuroAIHub/BrainPilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.