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Agent skill

Nwb Conversion

by K-Dense-AI in K-Dense-AI/scientific-agent-skills

Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema…

MITAuto-check passedDocuments & Office

Install Nwb Conversion

skills CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill nwb-conversion -a claude-code

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

GitHub CLI
$ gh skill install K-Dense-AI/scientific-agent-skills nwb-conversion --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/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nwb-conversion .claude/skills/nwb-conversion && 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
nwb-conversion
GitHub stars
48k
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
790 words
Files
5 (incl. scripts, references, assets)
Skills in repo
153
Repo updated
First seen
Licence
MIT

At a glance

Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema…

  • Works in 6 steps: Inventory the actual inputs and… → Copy assets/session-template.json beside… → Establish the common timebase from… → …
  • NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV
  • SKILL.md covers Supported streams, Install the tested environment, Workflow and Execute, plus 1 more section
  • Runs Python scripts from its folder; calls uv and python

What it does

Nwb Conversion is an agent skill from K-Dense-AI/scientific-agent-skills. Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks. Use for NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV; this skill does not perform spike sorting or claim tested support for arbitrary acquisition formats.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts, reference files and assets (for example `assets/session-template.json`, `references/input-contract.md` and `references/upstream-review.md`). Compatibility notes: Requires Python 3.12 with neuroconv[tiff] 0.10.2, PyNWB 4.2.0, NWB Inspector 0.7.2, roiextractors 0.10.0, tifffile 2026.9.20, zarr 2.18.7 and hdmf-zarr…

It sits in Documents & Office, covering Forms and validation and CSV and tabular files. It works with Zarr. The repository describes itself as: Turn any AI agent into an AI Scientist. The 1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific… The licence is MIT.

When your agent uses it

  • NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV
  • This skill does not perform spike sorting
  • Claim tested support for arbitrary acquisition formats

Example prompts

  • “Use the nwb-conversion skill to convert neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves…”
  • “/nwb-conversion”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Requires Python 3.12 with neuroconv[tiff] 0.10.2, PyNWB 4.2.0, NWB Inspector 0.7.2, roiextractors 0.10.0, tifffile 2026.9.20, zarr 2.18.7 and hdmf-zarr 0.11.3. Local HDF5 file access is required. Network is needed only for installation; no credentials.

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Inventory the actual inputs and acquisition metadata. Identify image plane/channel, optical
  2. Copy assets/session-template.json beside the raw data and
  3. Establish the common timebase from acquisition evidence. Frame timestamps must already be
  4. Execute the converter. Inputs must have finite, strictly increasing timestamps and matching
  5. Read the .validation.json alongside the NWB file. Schema compliance, Inspector findings and
  6. Deliver the NWB, validation JSON, original conversion config and an explanation of remaining

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. 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.

    Shell commands in SKILL.md call:

    • uv
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • neuroconv.readthedocs.io
    • github.com

    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.

  • Compatibility

    Requires Python 3.12 with neuroconv[tiff] 0.10.2, PyNWB 4.2.0, NWB Inspector 0.7.2, roiextractors 0.10.0, tifffile 2026.9.20, zarr 2.18.7 and hdmf-zarr 0.11.3. Local HDF5 file access is required. Network is needed only for installation; no credentials.

    From compatibility in the SKILL.md frontmatter.

Context cost

Nwb Conversion loads about 1.9k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 125 tokens; SKILL.md has 790 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~125
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.1k

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 K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 790 words, ~1,896 tokens.

Download SKILL.mdSave it as .claude/skills/nwb-conversion/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
nwb-conversion
description
Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks. Use for NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV; this skill does not perform spike sorting or claim tested support for arbitrary acquisition formats.
compatibility
Requires Python 3.12 with neuroconv[tiff] 0.10.2, PyNWB 4.2.0, NWB Inspector 0.7.2, roiextractors 0.10.0, tifffile 2026.9.20, zarr 2.18.7 and hdmf-zarr 0.11.3. Local HDF5 file access is required. Network is needed only for installation; no credentials.
license
MIT
metadata.version
1.1
metadata.skill-author
K-Dense Inc.
metadata.last-reviewed
2026-10-01
metadata.upstream-neuroconv
0.10.2
metadata.upstream-pynwb
4.2.0
metadata.upstream-nwbinspector
0.7.2

Validated NWB conversion

Supported streams

The executable workflow covers two explicit input streams in one session:

InputNWB representationTested constraints
Two-photon grayscale multi-page TIFF + frame timestamps CSVAcquisition TwoPhotonSeries named Imaging through NeuroConvOne channel, one plane, one 2D image per page, fixed shape and dtype
Calibrated position CSV (time_s,x,y)Behavior Position / SpatialSeries through PyNWBCoordinates in m, cm or mm; converted to meters without temporal resampling

Other acquisition readers require their own format-specific tests. In particular, this helper does not decode SpikeGLX, Open Ephys, multichannel TIFF, volumetric TIFF, compressed video, or pixel-to-world calibration. Do not rename an arbitrary numeric table to a supported stream.

Install the tested environment

bash
uv venv --python 3.12 nwb-env
uv pip install --python nwb-env/bin/python 'neuroconv[tiff]==0.10.2' pynwb==4.2.0 \
  nwbinspector==0.7.2 roiextractors==0.10.0 tifffile==2026.9.20 \
  zarr==2.18.7 hdmf-zarr==0.11.3

Keep both Zarr pins even for an HDF5-only conversion: NeuroConv 0.10.2 imports its backend configuration modules at startup, and the tested unconstrained Zarr 3.4.0 installation failed on zarr.codec_registry. The pinned environment ran the real conversion, PyNWB validation and Inspector successfully on macOS ARM64. The dependency resolver supplies NumPy and HDF5 support. These are compatibility pins, not claims that Zarr 2 and hdmf-zarr 0.11.3 are the latest releases. Current interface checks and the tested dependency exception are recorded in references/upstream-review.md.

Workflow

  1. Inventory the actual inputs and acquisition metadata. Identify image plane/channel, optical settings, subject/session identifiers, timezone, behavior coordinate system, units and the timestamp clock for every stream. Preserve originals. Do not replace missing metadata with plausible defaults from a sample config.
  2. Copy assets/session-template.json beside the raw data and replace the explicitly synthetic values. Paths resolve from that JSON file. Read references/input-contract.md for the exact CSV and metadata contract and the pulse-pair variant. TIFF pixels are retained as acquired; a raw arbitrary-unit intensity does not become a photon count merely by changing its unit label.
  3. Establish the common timebase from acquisition evidence. Frame timestamps must already be reference-clock seconds since the timezone-aware session start. For position, provide either a documented shared clock or matched synchronization pulses. The helper fits one affine clock transform, checks its residual against a specified tolerance, and refuses extrapolation beyond the pulse range. It never estimates synchronization from coincident-looking neural/behavioral signals. Clock resets or nonlinear drift require an explicitly validated piecewise mapping.
  4. Execute the converter. Inputs must have finite, strictly increasing timestamps and matching image/timestamp counts. Explicitly declare one channel and one plane; known TIFF channel/plane metadata must agree. Grayscale pages alone cannot exclude undocumented interleaving. The acquisition samples stay intact; only coordinate units and, when evidenced, behavior timestamps are transformed.
  5. Read the .validation.json alongside the NWB file. Schema compliance, Inspector findings and data equality answer different questions. The script exits with an error for schema failures and flags critical Inspector findings for review in the report. Review all findings in context; successful validation cannot establish that anatomical labels, pulse pairing or calibration supplied by the user are correct.
  6. Deliver the NWB, validation JSON, original conversion config and an explanation of remaining metadata gaps or Inspector findings. No upload or archive submission is part of this workflow.
Show full SKILL.md (295 more words)Show less

Execute

Run the following from the skill directory, with paths to the actual analysis files:

bash
nwb-env/bin/python scripts/convert_session.py /path/to/session.json --output /path/to/session.nwb

nwb-env must point to the environment created above; the absolute example input paths are illustrative. The command requires a .nwb output and refuses to overwrite an existing NWB or validation report. Output contains source and converter checksums, package versions, full supplied metadata, units and clock-fit provenance in both a scratch record and the validation report. When adapting this command for large data, TIFF writes are iterative and equality checking loads one frame at a time; position CSV currently loads into memory. Round-trip checks also verify dtype, unit scaling, optical-channel links, subject metadata, position reference frame, common time origin and embedded provenance. Inspector findings requiring review appear in the CLI summary; exit zero alone does not mean the file is scientifically correct. An exception during writing or round-trip checks can leave an incomplete NWB without a report; retain the error and use a fresh output path after correcting the cause.

The real-library test converts eight non-square uint16 images with irregular frame timing plus four position samples, asserts exact pixel and timestamp round trips, and checks centimeter-to-meter conversion. A second integration test recovers a known 1000-ppm clock drift and 50-ms offset from three matched pulses. Duplicate timestamps, mismatched frame counts, absent clock evidence, nonlinear pulse disagreement and missing timezone are rejection cases. The mapping is TIFF (time,y,x) to NWB (time,x,y), explicitly checked against every transposed source page. NWB Inspector flags the short fixture with a critical orientation heuristic because width exceeds frame count; the report retains that finding and adds the exact frame/timestamp equality evidence. No transpose is performed merely to satisfy a longest-axis heuristic.

Primary references

© K-Dense-AI, MIT. 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 4 other files (scripts, references, assets) in skills/nwb-conversion of K-Dense-AI/scientific-agent-skills.

  • SKILL.md
  • assets/session-template.json
  • references/input-contract.md
  • references/upstream-review.md
  • scripts/convert_session.py

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Nwb Conversion 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.

Nwb Conversion compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Nwb Conversion this skillK-Dense-AI/scientific-agent-skills48k1 repos~1.9kAutomated safety check: PassMIT
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Vdjdb Extractantigenomics/vdjdb-db157—~1.5kAutomated safety check: PassCustom licence
Generate CodebookAperivue/medsci-skills333—~1.1kAutomated safety check: PassMIT
Auditing Part11 Trailsmaziyarpanahi/openmed5.5k—~2.2kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Nwb Conversion

What does Nwb Conversion do?

Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema…. Nwb Conversion is an agent skill from K-Dense-AI/scientific-agent-skills. Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks.

When should I use Nwb Conversion?

Nwb Conversion fits situations like: NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV; this skill does not perform spike sorting; claim tested support for arbitrary acquisition formats.

How do I install Nwb Conversion in Claude Code?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill nwb-conversion -a claude-code`. Or copy the skill folder (skills/nwb-conversion in K-Dense-AI/scientific-agent-skills) into .claude/skills/nwb-conversion in your project. Claude Code loads it when a task matches its description.

How do I install Nwb Conversion in Codex?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill nwb-conversion -a codex`. Or copy the skill folder (skills/nwb-conversion in K-Dense-AI/scientific-agent-skills) into .agents/skills/nwb-conversion in your project. Codex loads it when a task matches its description.

Can I use Nwb Conversion 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 K-Dense-AI/scientific-agent-skills --skill nwb-conversion -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nwb-conversion, .gemini/skills/nwb-conversion, .github/skills/nwb-conversion and .opencode/skills/nwb-conversion in your project.

What does Nwb Conversion need to run?

Going by SKILL.md and its folder, Nwb Conversion needs Python for the scripts in its folder and the command-line tools its instructions call (uv and python). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires Python 3.12 with neuroconv[tiff] 0.10.2, PyNWB 4.2.0, NWB Inspector 0.7.2, roiextractors 0.10.0, tifffile 2026.9.20, zarr 2.18.7 and hdmf-zarr 0.11.3. Local HDF5 file access is required. Network is needed only for installation; no credentials..

Does Nwb Conversion access the network?

SKILL.md names 2 domains. As links in the text: neuroconv.readthedocs.io and github.com. This is read from the text; nothing was executed.

Is Nwb Conversion 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 Nwb Conversion use?

Nwb Conversion is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nwb Conversion use?

About 1.9k tokens (SKILL.md is roughly 7.6k 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 2.2k tokens, read only when the agent opens those files.

What are the alternatives to Nwb Conversion?

Skills that share tags, products or a category with Nwb Conversion: Dataset Conversion (open-h/open-h-embodiment, 149 stars), Module Authoring (dna-seq/just-dna-lite, 141 stars), Vdjdb Extract (antigenomics/vdjdb-db, 157 stars) and Generate Codebook (Aperivue/medsci-skills, 333 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nwb Conversion?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/scientific-agent-skills, which has 48,215 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.

Source: K-Dense-AI/scientific-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.