/agent-figure-gallery
Query visual scientific figure references, show candidates for human preference selection, export selected reference bundles, and guide plotting agents from human-selected visual examples to code action.
$ npx -y skills add Dsadd4/AgentFigureGallery --skill agent-figure-gallery --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/agent-figure-gallery
Context preview
The summary Claude sees to decide when to auto-load this skill.
Query visual scientific figure references, show candidates for human preference selection, export selected reference bundles, and guide plotting agents from human-selected visual examples to code action.
SKILL.md
agent-figure-gallery.SKILL.mdname: agent-figure-gallery
description: Query visual scientific figure references, show candidates for human preference selection, export selected reference bundles, and guide plotting agents from human-selected visual examples to code action.
Agent Figure Gallery
Core Rule
Treat this skill as a lightweight controller. Do not load the full visual corpus into the skill. Use `AGENT_FIGURE_GALLERY_ROOT` or `DRAWING_KB_ROOT` to point at the external AgentFigureGallery knowledge base.
Key Files
- KB root: `AGENT_FIGURE_GALLERY_ROOT=/path/to/AgentFigureGallery`
- CLI: `agentfiguregallery` or `python -m agentfiguregallery.cli`
- Gallery server: backend command that serves the reference gallery on localhost
- Candidate index: `data/reference_candidate_index.json`
- Global preferences: `data/reference_global_preferences.json`
- Reference sessions: `outputs/reference_sessions/`
Minimal Workflow
1. Resolve the KB root:
export AGENT_FIGURE_GALLERY_ROOT=/path/to/AgentFigureGallery
2. Query before reading individual references:
agentfiguregallery query --task "<user task>"
3. Generate visible candidates:
agentfiguregallery gallery --plot-type <plot_type> --task "<user task>" --limit 50 --serve
4. Record human preferences:
agentfiguregallery prefer --session outputs/reference_sessions/<session_id> --like <ID> --reject <ID> --select <ID>
5. Export a selected bundle:
agentfiguregallery bundle --session outputs/reference_sessions/<session_id> --copy-scripts
6. Use the bundle before writing or revising plotting code.
Preference Semantics
- `like`: useful for this task or plot type.
- `reject`: not useful for this task or plot type.
- `select`: use this candidate for the current agent action.
- `global_like`: generally useful across tasks.
- `global_reject`: hide from future sessions.
Local preferences must preserve `plot_type`. Global preferences are cross-task.
Validation
After changing the CLI, gallery, preference logic, or bundle export:
agentfiguregallery gallery --plot-type embedding_plot --limit 20 --serve
agentfiguregallery prefer --session outputs/reference_sessions/<session_id> --like E01 --select E02
agentfiguregallery bundle --session outputs/reference_sessions/<session_id>
Success means visible candidates render, stable IDs are shown, preferences persist, global rejects are hidden from later generated sessions, and the bundle contains selected references plus source code paths.
Read more
name: agent-figure-gallery description: Query visual scientific figure references, show candidates for human preference selection, export selected reference bundles, and guide plotting agents from human-selected visual examples to code action.
Agent Figure Gallery
Core Rule
Treat this skill as a lightweight controller. Do not load the full visual corpus into the skill. Use `AGENT_FIGURE_GALLERY_ROOT` or `DRAWING_KB_ROOT` to point at the external AgentFigureGallery knowledge base.
Key Files
- KB root: `AGENT_FIGURE_GALLERY_ROOT=/path/to/AgentFigureGallery`
- CLI: `agentfiguregallery` or `python -m agentfiguregallery.cli`
- Gallery server: backend command that serves the reference gallery on localhost
- Candidate index: `data/reference_candidate_index.json`
- Global preferences: `data/reference_global_preferences.json`
- Reference sessions: `outputs/reference_sessions/`
Minimal Workflow
1. Resolve the KB root:
export AGENT_FIGURE_GALLERY_ROOT=/path/to/AgentFigureGallery
2. Query before reading individual references:
agentfiguregallery query --task "<user task>"
3. Generate visible candidates:
agentfiguregallery gallery --plot-type <plot_type> --task "<user task>" --limit 50 --serve
4. Record human preferences:
agentfiguregallery prefer --session outputs/reference_sessions/<session_id> --like <ID> --reject <ID> --select <ID>
5. Export a selected bundle:
agentfiguregallery bundle --session outputs/reference_sessions/<session_id> --copy-scripts
6. Use the bundle before writing or revising plotting code.
Preference Semantics
- `like`: useful for this task or plot type.
- `reject`: not useful for this task or plot type.
- `select`: use this candidate for the current agent action.
- `global_like`: generally useful across tasks.
- `global_reject`: hide from future sessions.
Local preferences must preserve `plot_type`. Global preferences are cross-task.
Validation
After changing the CLI, gallery, preference logic, or bundle export:
agentfiguregallery gallery --plot-type embedding_plot --limit 20 --serve agentfiguregallery prefer --session outputs/reference_sessions/<session_id> --like E01 --select E02 agentfiguregallery bundle --session outputs/reference_sessions/<session_id>
Success means visible candidates render, stable IDs are shown, preferences persist, global rejects are hidden from later generated sessions, and the bundle contains selected references plus source code paths.
AgentFigureGallery brings together beautiful scientific figures primarily from Nature and its sister journals, complemented by plotting code from high-quality open-source projects, to serve as visual templates and code references for you and your AI coding

