Agent skill

Sceneeye Maintenance

by isetbio in isetbio/isetbio

Repair, promote, reorganize, or evaluate sceneEye tutorials and examples.

MITAuto-check passedTesting & QA

Install Sceneeye Maintenance

skills CLI
$ npx skills add isetbio/isetbio --skill sceneeye-maintenance -a claude-code

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

GitHub CLI
$ gh skill install isetbio/isetbio sceneeye-maintenance --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/isetbio/isetbio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/sceneeye-maintenance .claude/skills/sceneeye-maintenance && 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
sceneeye-maintenance
GitHub stars
115
Token cost
~579 tokens
SKILL.md length
234 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Repair, promote, reorganize, or evaluate sceneEye tutorials and examples.

  • Works in 5 steps: Identify obsolete helpers, unavailable… → Replace legacy dependency checks such as… → Reduce repeated parameter sweeps to one… → …
  • Editing tutorials/sceneEye
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Examples/sceneEye

What it does

Sceneeye Maintenance is an agent skill from isetbio/isetbio. Repair, promote, reorganize, or evaluate sceneEye tutorials and examples. Use when editing tutorials/sceneEye, examples/sceneEye, sceneEye underDevelopment paths, PBRT or Docker/cloud-dependent scripts, or deciding whether a sceneEye workflow belongs in a routine tutorial smoke test or a skipped example.

Its SKILL.md is about 580 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering QA and bug reports and Containers. It works with Docker. The repository describes itself as: Tools for modeling image systems engineering in the human visual system front end. The licence is MIT.

When your agent uses it

  • Editing tutorials/sceneEye
  • Examples/sceneEye
  • SceneEye underDevelopment paths
  • Docker/cloud-dependent scripts

Example prompts

  • “/sceneeye-maintenance”

Requirements

  • Docker

Workflow steps

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

  1. Identify obsolete helpers, unavailable assets, external dependencies, and
  2. Replace legacy dependency checks such as piCamBio, piDockerExists,
  3. Reduce repeated parameter sweeps to one representative runnable condition.
  4. If a PBRT scene is not in the repository or standard data-download path,
  5. Test a promoted file with its selected tutorial or example runner.

What it can do on your machine

Read from SKILL.md and the folder at commit d2924c2. 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

Sceneeye Maintenance loads about 579 tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 234 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
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 isetbio/isetbio at commit d2924c2, republished under its MIT licence (© isetbio). 234 words, ~579 tokens.

Download SKILL.mdSave it as .claude/skills/sceneeye-maintenance/SKILL.md (or your agent's skills folder).
name
sceneeye-maintenance
description
Repair, promote, reorganize, or evaluate sceneEye tutorials and examples. Use when editing tutorials/sceneEye, examples/sceneEye, sceneEye underDevelopment paths, PBRT or Docker/cloud-dependent scripts, or deciding whether a sceneEye workflow belongs in a routine tutorial smoke test or a skipped example.

sceneEye Maintenance

Instructions & Guidelines

Placement Rule

Keep a sceneEye file in tutorials/sceneEye only when it is short, linear, and teaches one core object/API concept using a compact render or mocked/precomputed result. Avoid cloud execution, manual Docker setup, large PBRT sweeps, and high-resolution comparison grids.

Place applied workflows in examples/sceneEye: multiple eye models or render conditions, crop-window rendering, depth-of-field or accommodation sweeps, retina-shape experiments, stereo, cloud rendering, PSF/MTF analysis, and publication-style figures.

Keep tutorials/sceneEye/analysis/underDevelopment/ and tutorials/sceneEye/cloud/underDevelopment/ skipped unless a deliberate promotion makes one script routine, local, and reliable.

Repair and Promotion Workflow
  1. Identify obsolete helpers, unavailable assets, external dependencies, and repeated high-resolution work.
  2. Replace legacy dependency checks such as piCamBio, piDockerExists, piDockerConfig, mcDockerExists, and mcDockerConfig with a current documented setup check or an explicit skip reason.
  3. Reduce repeated parameter sweeps to one representative runnable condition.
  4. If a PBRT scene is not in the repository or standard data-download path, document the asset requirement clearly or retain the script as a skipped example.
  5. Test a promoted file with its selected tutorial or example runner.
Acceptance Criteria
  • isetbioTutorialTest has zero failures.
  • Under-development paths remain skipped.
  • A restored tutorial passes isetbioTutorialTest('selection','<name>') and generally completes in under 10 seconds on a normal local MATLAB session.
  • A moved example passes isetbioExampleTest('selection','<name>'), or keeps % SkipFile with a specific documented reason.
  • Retain one clear example per workflow family; merge, remove, or skip obsolete, overly narrow, or duplicated scripts.

© isetbio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .github/skills/sceneeye-maintenance of isetbio/isetbio.

Open the folder on GitHubat commit d2924c2

Compare with similar skills

Sceneeye Maintenance 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.

Sceneeye Maintenance compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Sceneeye Maintenance this skillisetbio/isetbio115—~579Automated safety check: PassMIT
DeerFlow Smoke Testbytedance/deer-flow84k—~2.5kAutomated safety check: NotesMIT
Lvc Run Appopen-edge-platform/edge-ai-suites140—~796Automated safety check: PassApache-2.0
PR TestElite588/AUTOGPT103—~9.4kAutomated safety check: NotesCustom licence
PgjevrealZachi/pg-jev1.1k—~2.9kAutomated safety check: PassCustom licence
Onboarding Validationopen-edge-platform/edge-ai-suites140—~3.3kAutomated safety check: PassApache-2.0

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More from isetbio/isetbio

All 9 skills in this repo
  • Matlab Environment

    isetbio/isetbio

    Configure or troubleshoot a local MATLAB and VS Code environment for ISETBio.

    115 GitHub stars~558 tokensUpdated 15 days ago
    Auto-check passed
  • Matlab Evaluation

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    Run MATLAB non-interactively from a shell on this machine — locating the MATLAB binary, setting ISETCam/ISETBio paths for a batch session, and invoking -batch evaluation.

    115 GitHub stars~568 tokensUpdated 15 days ago
    Auto-check passed
  • Matlab Testing

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    Develop, run, diagnose, or report ISETBio MATLAB tests. An agent skill from isetbio/isetbio.

    115 GitHub stars~884 tokensUpdated 15 days ago
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  • Retinal Mosaic Data

    isetbio/isetbio

    Work with ISETBio cone and midget-RGC mosaic data, including loading, generating, migrating, caching, or reviewing lattices, serialized cMosaics, pre-baked mRGCMosaics, and compute-ready mosaics.

    115 GitHub stars~1.5k tokensUpdated 15 days ago
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  • Create, move, review, publish, or test ISETBio tutorials, examples, and data-generation scripts.

    115 GitHub stars~560 tokensUpdated 15 days ago
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  • Sdr Mosaic Data

    isetbio/isetbio

    Create, publish, organize, or fetch the ISETBio retinal mosaic data deposit in the Stanford Digital Repository (SDR).

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Works with

Questions about Sceneeye Maintenance

What does Sceneeye Maintenance do?

Repair, promote, reorganize, or evaluate sceneEye tutorials and examples. Sceneeye Maintenance is an agent skill from isetbio/isetbio. Repair, promote, reorganize, or evaluate sceneEye tutorials and examples.

When should I use Sceneeye Maintenance?

Sceneeye Maintenance fits situations like: editing tutorials/sceneEye; examples/sceneEye; sceneEye underDevelopment paths; Docker/cloud-dependent scripts.

How do I install Sceneeye Maintenance in Claude Code?

Run `npx skills add isetbio/isetbio --skill sceneeye-maintenance -a claude-code`. Or copy the skill folder (.github/skills/sceneeye-maintenance in isetbio/isetbio) into .claude/skills/sceneeye-maintenance in your project. Claude Code loads it when a task matches its description.

How do I install Sceneeye Maintenance in Codex?

Run `npx skills add isetbio/isetbio --skill sceneeye-maintenance -a codex`. Or copy the skill folder (.github/skills/sceneeye-maintenance in isetbio/isetbio) into .agents/skills/sceneeye-maintenance in your project. Codex loads it when a task matches its description.

Can I use Sceneeye Maintenance 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 isetbio/isetbio --skill sceneeye-maintenance -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sceneeye-maintenance, .gemini/skills/sceneeye-maintenance, .github/skills/sceneeye-maintenance and .opencode/skills/sceneeye-maintenance in your project.

What does Sceneeye Maintenance need to run?

SKILL.md names no scripts, command-line tools or credentials: Sceneeye Maintenance is instructions for the agent only. Our summary lists: Docker.

Does Sceneeye Maintenance 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 Sceneeye Maintenance 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 Sceneeye Maintenance use?

Sceneeye Maintenance is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sceneeye Maintenance 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 Sceneeye Maintenance?

Skills that share tags, products or a category with Sceneeye Maintenance: DeerFlow Smoke Test (bytedance/deer-flow, 84k stars), Lvc Run App (open-edge-platform/edge-ai-suites, 140 stars), PR Test (Elite588/AUTOGPT, 103 stars) and Pgjev (realZachi/pg-jev, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sceneeye Maintenance?

isetbio (a GitHub organization) maintains it in isetbio/isetbio, which has 115 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on September 25, 2026.

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