AgentFigureGallery

作者 Dsadd4已验证

Drop-in scientific plotting skill for Claude Code, Codex, Cursor, and other coding agents.

134
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1
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Python
语言
2026/8/23
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⚠️ 第三方软件声明

本 Skill 为第三方开源软件,独立托管于 GitHub。SkillTip 仅为信息目录,不控制或维护底层仓库。所显示的安全检查为自动化且范围有限,安装前请自行审查源码。

阅读服务条款

安装

添加到你的 Claude Code skills 目录:

# Add to your Claude Code skills
git clone https://github.com/Dsadd4/AgentFigureGallery

快速入门

使用 AgentFigureGallery 等 Skills 的指南。

安全报告

已验证

上次扫描:—

{
  "status": "PASSED",
  "issues": []
}

README.md

AgentFigureGallery

English 简体中文

License: MIT Python 3.10+ Full KB Hugging Face Dataset

AgentFigureGallery is a scientific plotting reference gallery for Claude Code, Codex, Cursor, and other coding agents. It lets an agent search real figure references, lets you mark examples as liked, rejected, or selected in a browser gallery, and exports those choices as a reference bundle for plotting code.

Quick install for Codex:

curl -fsSL https://raw.githubusercontent.com/Dsadd4/AgentFigureGallery/main/scripts/install.sh | bash

The script clones or updates the repository at $HOME/AgentFigureGallery, creates a Python virtual environment, installs the package, and installs the Codex skill wrapper. After this bootstrap install, run the CLI as ~/AgentFigureGallery/.venv/bin/agentfiguregallery, or activate the environment first:

source ~/AgentFigureGallery/.venv/bin/activate

For an editable manual install, see Manual Install.

AgentFigureGallery dynamic demo

agent query -> browser gallery -> human like/reject/select -> reference bundle -> plotting code

Before generating plotting code, the agent queries visual references, the user selects preferred examples in the browser, and AgentFigureGallery exports the selected references for the final plotting task.

AgentFigureGallery candidate counts by plot type

Quick Paths

GoalCommand or link
First successful local run after install~/AgentFigureGallery/.venv/bin/agentfiguregallery first-run --open
Bootstrap the Codex skillcurl -fsSL https://raw.githubusercontent.com/Dsadd4/AgentFigureGallery/main/scripts/install.sh | bash
Install through Awesome Skillsnpx add-skill Dsadd4/AgentFigureGallery
Review install behavior firstTrust and Install Notes
Open the Hugging Face showcaseAgentFigureGallery Space
Contribute a reference packCommunity Packs

Manual Install

git clone https://github.com/Dsadd4/AgentFigureGallery.git
cd AgentFigureGallery
python -m venv .venv
source .venv/bin/activate
pip install -e .
agentfiguregallery doctor
agentfiguregallery install-skill --target codex

The default install is enough for smoke tests and the small built-in reference pack. To use the full 16k+ public reference pool, run the setup command below.

Full Public Reference Pool

Install the complete full-public pack from Hugging Face:

agentfiguregallery setup --pack full-public --manifest-url https://huggingface.co/datasets/dsadd4/AgentFigureGallery/resolve/main/resource_manifest.json

If Hugging Face is blocked, use the GitHub API manifest fallback:

agentfiguregallery setup --pack full-public --manifest manifests/resource_manifest.github-api.json

You can also download the full pack during bootstrap:

curl -fsSL https://raw.githubusercontent.com/Dsadd4/AgentFigureGallery/main/scripts/install.sh | env AFG_INSTALL_FULL_PUBLIC=1 bash

Browser Gallery Workflow

Create a reference session and open the browser gallery:

agentfiguregallery gallery --plot-type embedding_plot --limit 50 --serve
# Then open http://127.0.0.1:8765/

The command prints a session id before starting the local server. In the browser, mark references as liked, rejected, or selected. Those saved preferences are reused by later sessions.

After selecting references, export the bundle for the coding agent:

agentfiguregallery bundle --session <session_id>

The bundle is written to:

outputs/reference_sessions/<session_id>/export_bundle/reference_bundle.json

To reopen the frontend later without creating a new reference session:

agentfiguregallery serve --host 127.0.0.1 --port 8765

Verify Codex Skill

After installing the Codex skill, Codex can discover AgentFigureGallery as a local skill.

Codex discovered Agent Figure Gallery

Then ask your coding agent to run a plot-type smoke test:

Use AgentFigureGallery to test your installed plotting skill. Generate one Nature-style example for each supported plot type, then export PNG/PDF/SVG and a combined preview.

The result should look like this: one Nature-style example for every supported plot type.

AgentFigureGallery plot-type smoke examples

See examples/plot_type_examples/ for the runnable script, source data, and PNG/PDF/SVG outputs.

For Coding Agents

After pip install -e . finishes, tell your Codex, Claude Code, Cursor, or other coding agent:

Read skills/agent-figure-gallery/SKILL.md, then use AgentFigureGallery before writing publication figure code.

You can install personal skill wrappers for multiple agents:

curl -fsSL https://raw.githubusercontent.com/Dsadd4/AgentFigureGallery/main/scripts/install.sh | env AFG_AGENT_TARGETS="codex claude-code cursor" bash

Cursor project rules need a project path, so pass it explicitly:

curl -fsSL https://raw.githubusercontent.com/Dsadd4/AgentFigureGallery/main/scripts/install.sh | env AFG_AGENT_TARGETS="cursor" AFG_CURSOR_PROJECT=/path/to/your-cursor-project bash

Or install wrappers manually:

agentfiguregallery install-skill --target codex
agentfiguregallery install-skill --target claude-code
agentfiguregallery install-skill --target cursor
agentfiguregallery install-cursor-rule --project /path/to/your-cursor-project

Codex installs to ~/.codex/skills, Claude Code installs to ~/.claude/skills, Cursor-compatible skill installs write to ~/.cursor/skills, and Cursor project rules write to .cursor/rules/agent-figure-gallery.mdc. See docs/AGENT_QUICKSTART.md and examples/agent_prompt.md.

End-to-end examples:

  • examples/end_to_end_embedding.md
  • examples/generated_embedding_plot/README.md
  • examples/before_after_benchmark/README.md

Dynamic Gallery

Use the browser gallery to browse candidates by plot type, reject unsuitable references, save plot-type preferences, and export selected examples for the agent. With the full-public pack installed, the gallery can draw from 16,341 public visual candidates across common scientific plot types.

agentfiguregallery query --task "Nature-style embedding map for cell atlas"
agentfiguregallery gallery --plot-type embedding_plot --limit 100 --serve

Extend Your Gallery

AgentFigureGallery can grow after install. You can ask an agent to follow the expansion guide, or add a small local reference pack yourself, then inspect the new candidates in the browser gallery.

Tell your coding agent:

Read ExtendAgent/README.md, then expand AgentFigureGallery for <plot type or style>. Discover high-quality public scientific plotting sources, render every useful reference as a visible preview, preserve stable candidate IDs and source license metadata, rebuild the candidate index, and report candidate counts plus private-path scan results.

For manual expansion, the important rules are:

  1. Add only references with visible preview PNGs.
  2. Preserve stable candidate_id, plot_type, preview path, source metadata, and license attribution when available.
  3. Keep large preview packs, raw upstream repositories, private paths, and tokens out of Git.

See ExtendAgent/README.md for the full expansion contract and quality gates.

Community Packs

Community packs are the public contribution path for reusable plotting references. The base full-public pack remains the canonical 16k+ pool maintained by Dsadd4; community contributions land first in community_pool/, then accepted material is periodically released as installable asset packs.

Contribution routes:

  • Open a Community Pack issue to propose public sources, plot types, or a pack idea.
  • Open a PR under community_pool/packs/<pack_name>/ using the documented schema.
  • Keep large assets out of Git; accepted packs are distributed through resource manifests.

After a community release manifest is published, users can selectively install a community pack:

agentfiguregallery setup --pack community-latest --manifest-url <community_resource_manifest_url>
agentfiguregallery gallery --plot-type embedding_plot --limit 50 --serve

See docs/COMMUNITY_PACKS.md and community_pool/README.md for contribution rules, schemas, review gates, and install patterns.

What Is Inside

  • 16,341 full-public visual candidates across 10 scientific plot types.
  • Browser-gallery feedback for personal or lab-specific figure preferences.
  • A small curated minimal pack committed for instant smoke tests.
  • Codex-equipped plot-type smoke examples with PNG/PDF/SVG outputs.
  • Backend CLI, browser gallery, Codex skill wrapper, and agent expansion guide.
  • Stable candidate IDs, saved preferences, and export bundles for agent handoff.
  • Community pack contribution path for user-submitted plotting references and periodic asset releases.

Roadmap

Completed:

Docs

User docs:

  • docs/AGENT_QUICKSTART.md: minimal instructions for coding agents.
  • docs/COMMUNITY_PACKS.md: community contribution rules and release model.
  • community_pool/: staging area and schema examples for community packs.
  • ExtendAgent/: instructions for agents that expand the gallery.
  • docs/REMOTE_FULL_VALIDATION.md: first remote full-public validation and current mirror-speed caveat.

Maintainer docs:

  • docs/DISCOVERY_PLAYBOOK.md: launch and star-growth checklist.
  • docs/releases/v0.1.0.md: first public release notes.
  • docs/HF_SYNC.md: Hugging Face dataset card and asset sync commands.
  • docs/PYPI_RELEASE.md: Python package release path.
  • docs/HF_DATASET_CARD.md: Hugging Face dataset card draft.
  • docs/LAUNCH.md: public launch copy and channels.
  • docs/FULL_KB_DISTRIBUTION.md: public asset-pack strategy.

常见问题

What is AgentFigureGallery?

AgentFigureGallery is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Dsadd4. Drop-in scientific plotting skill for Claude Code, Codex, Cursor, and other coding agents. It has 134 GitHub stars.

Is AgentFigureGallery safe to use?

Yes. AgentFigureGallery passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install AgentFigureGallery?

Clone the repository with "git clone https://github.com/Dsadd4/AgentFigureGallery" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is AgentFigureGallery written in?

AgentFigureGallery is primarily written in Python. It is open-source under Dsadd4 on GitHub, so you can review or fork the full source.

Are there alternatives to AgentFigureGallery?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh AgentFigureGallery against similar tools.

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