Azhi-ss
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academic-figure-architecture-extractor
Extract semantic structure and transferable style grammar from academic figures, PDFs, and paper or figure URLs for analysis, redraws, or reference-conditioned generation. Do not use it for paper-text-only figure planning.
Overview
Extract semantic structure and transferable style grammar from academic figures, PDFs, and paper or figure URLs for analysis, redraws, or reference-conditioned generation. Do not use it for paper-text-only figure planning.
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Academic Figure Architecture Extractor
Turn supplied figures into grounded structure and reference-style evidence. Keep scientific content separate from transferable visual grammar.
Load only when relevant:
- missing evidence →
references/missing-info-policy.md - local PDF helper →
scripts/extract_pdf_figures.py
Scope and inputs
Accept one or more local images, PDFs, public paper/figure URLs, or an existing render that needs revision. Prefer the original figure image over a screenshot when both exist. PDF extraction finds embedded images or rasterized pages; it is not an architecture detector, and figure classification remains evidence-based judgment.
For an article URL, inspect the page with an available web or browser tool, locate the requested figure and caption, and resolve its direct image or PDF source. Do not classify a paper URL as a repository. If a renderer will need the reference, retain or download it to an absolute local path without overwriting user files.
Obtain candidates
- Local image: inspect it with an available image-viewing tool at original detail.
- Remote figure: open it, preserve its source URL and caption, then inspect the localized image when possible.
- PDF: use a PDF-capable reader for pages and captions. To extract candidates, run the bundled helper from the skill repository when available:
python3 academic-figure-architecture-extractor/scripts/extract_pdf_figures.py \
/absolute/path/to/paper.pdf -o /absolute/path/to/extraction-directory
Useful optional flags are --backend auto|pdfimages|pymupdf, --pages 1-3|all,
--dpi 150, --min-side 300, and --min-pixels 90000. Read the generated
extract-report.json. If extraction tools are unavailable, use directly viewable
pages or ask for an exported figure; never invent a local path.
Record for every candidate: source URL/path, page or figure number, caption, pixel size, local absolute path when available, and keep/drop/uncertain with a reason.
Analyze semantic structure
For each kept figure, identify only what the image and caption support:
- components and their core/auxiliary roles;
- groups, hierarchy, reading direction, and dominant focal point;
- directed edges, branches, loops, annotations, and legend semantics;
- figure type and uncertain or illegible content.
Do not import the reference's labels, claims, metrics, or topology into a new method figure. Those are reusable only when the user explicitly requests a faithful redraw.
Extract the style grammar
Describe observable design decisions rather than reducing the figure to a palette name. Capture:
composition: storyboard|pipeline|loop|modular collage|nested mechanism|other
regions: panel geometry, grouping, asymmetry, whitespace and density
marks: vector, hand-drawn, editorial illustration, geometric, line-art, mixed
strokes: weight, curvature, joins, arrow and connector language
fills: flat/tinted/wash/white, border treatment, shadow and texture
color_roles: background, regions, primary structure, accents, exceptions
typography: family character, hierarchy, placement and approximate density
motifs: agents, scientific objects, tokens, icons, mini-plots, callouts
emphasis: focal scale, contrast, saturation and annotation strategy
avoid: visual traits absent from or conflicting with the reference
Do not force classic versus pastel, Nature Blue, white-fill boxes, or a venue
stereotype. A reference may define a third style family such as a hand-drawn
editorial scientific infographic. Treat exact hex values as observations, not as a
substitute for composition and hierarchy.
ReferenceAnalysis v1 handoff
Emit one JSON-compatible object per kept figure:
schema: academic-figure/ReferenceAnalysis@1
reference_id
source: {url_or_absolute_path, local_absolute_path, page_or_figure, caption, size}
semantic_structure: {components[], groups[], edges[], reading_order, uncertainties[]}
style_grammar: {composition, regions, marks, strokes, fills, color_roles,
typography, motifs, emphasis, avoid[]}
transfer_policy: {reuse[], do_not_copy[]}
confidence: high|partial|sparse
Downstream FigurePlan v1 carries reference_id and style_grammar; FigureSpec v1
carries the reference's absolute local path whenever it can be materialized. If
the asset exists only in the current conversation, it may use the workflow's
transient recent-conversation descriptor for the immediate render, but should be
materialized before a reproducible rerun. A capable backend conditions on the
image itself. A later RenderAudit v1 compares observable style fidelity without
requiring content imitation.
Stop after the inventory if that is all the user requested. Otherwise stop when ReferenceAnalysis v1 is complete or when a missing source image blocks honest analysis.
File metadata
name: academic-figure-architecture-extractor description: Extract semantic structure and transferable style grammar from academic figures, PDFs, and paper or figure URLs for analysis, redraws, or reference-conditioned generation. Do not use it for paper-text-only figure planning. metadata: version: "1.3.0"
View original text
---
name: academic-figure-architecture-extractor
description: Extract semantic structure and transferable style grammar from academic figures, PDFs, and paper or figure URLs for analysis, redraws, or reference-conditioned generation. Do not use it for paper-text-only figure planning.
metadata:
version: "1.3.0"
---
# Academic Figure Architecture Extractor
Turn supplied figures into grounded structure and reference-style evidence. Keep
scientific content separate from transferable visual grammar.
Load only when relevant:
- missing evidence → `references/missing-info-policy.md`
- local PDF helper → `scripts/extract_pdf_figures.py`
## Scope and inputs
Accept one or more local images, PDFs, public paper/figure URLs, or an existing
render that needs revision. Prefer the original figure image over a screenshot when
both exist. PDF extraction finds embedded images or rasterized pages; it is not an
architecture detector, and figure classification remains evidence-based judgment.
For an article URL, inspect the page with an available web or browser tool, locate
the requested figure and caption, and resolve its direct image or PDF source. Do not
classify a paper URL as a repository. If a renderer will need the reference, retain
or download it to an absolute local path without overwriting user files.
## Obtain candidates
- **Local image:** inspect it with an available image-viewing tool at original detail.
- **Remote figure:** open it, preserve its source URL and caption, then inspect the
localized image when possible.
- **PDF:** use a PDF-capable reader for pages and captions. To extract candidates,
run the bundled helper from the skill repository when available:
```bash
python3 academic-figure-architecture-extractor/scripts/extract_pdf_figures.py \
/absolute/path/to/paper.pdf -o /absolute/path/to/extraction-directory
```
Useful optional flags are `--backend auto|pdfimages|pymupdf`, `--pages 1-3|all`,
`--dpi 150`, `--min-side 300`, and `--min-pixels 90000`. Read the generated
`extract-report.json`. If extraction tools are unavailable, use directly viewable
pages or ask for an exported figure; never invent a local path.
Record for every candidate: source URL/path, page or figure number, caption, pixel
size, local absolute path when available, and keep/drop/uncertain with a reason.
## Analyze semantic structure
For each kept figure, identify only what the image and caption support:
- components and their core/auxiliary roles;
- groups, hierarchy, reading direction, and dominant focal point;
- directed edges, branches, loops, annotations, and legend semantics;
- figure type and uncertain or illegible content.
Do not import the reference's labels, claims, metrics, or topology into a new method
figure. Those are reusable only when the user explicitly requests a faithful redraw.
## Extract the style grammar
Describe observable design decisions rather than reducing the figure to a palette
name. Capture:
```text
composition: storyboard|pipeline|loop|modular collage|nested mechanism|other
regions: panel geometry, grouping, asymmetry, whitespace and density
marks: vector, hand-drawn, editorial illustration, geometric, line-art, mixed
strokes: weight, curvature, joins, arrow and connector language
fills: flat/tinted/wash/white, border treatment, shadow and texture
color_roles: background, regions, primary structure, accents, exceptions
typography: family character, hierarchy, placement and approximate density
motifs: agents, scientific objects, tokens, icons, mini-plots, callouts
emphasis: focal scale, contrast, saturation and annotation strategy
avoid: visual traits absent from or conflicting with the reference
```
Do not force `classic` versus `pastel`, Nature Blue, white-fill boxes, or a venue
stereotype. A reference may define a third style family such as a hand-drawn
editorial scientific infographic. Treat exact hex values as observations, not as a
substitute for composition and hierarchy.
## ReferenceAnalysis v1 handoff
Emit one JSON-compatible object per kept figure:
```text
schema: academic-figure/ReferenceAnalysis@1
reference_id
source: {url_or_absolute_path, local_absolute_path, page_or_figure, caption, size}
semantic_structure: {components[], groups[], edges[], reading_order, uncertainties[]}
style_grammar: {composition, regions, marks, strokes, fills, color_roles,
typography, motifs, emphasis, avoid[]}
transfer_policy: {reuse[], do_not_copy[]}
confidence: high|partial|sparse
```
Downstream FigurePlan v1 carries `reference_id` and `style_grammar`; FigureSpec v1
carries the reference's absolute local path whenever it can be materialized. If
the asset exists only in the current conversation, it may use the workflow's
transient recent-conversation descriptor for the immediate render, but should be
materialized before a reproducible rerun. A capable backend conditions on the
image itself. A later RenderAudit v1 compares observable style fidelity without
requiring content imitation.
Stop after the inventory if that is all the user requested. Otherwise stop when
ReferenceAnalysis v1 is complete or when a missing source image blocks honest
analysis.
Review the source
Price & running costs
- Get the skill
- Price unconfirmed
- Run it
- Requirements have not been confirmed. Check the source for agent, API and service charges.
- License
- MIT
- Price unconfirmed
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Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 99 GitHub stars
- Stars/forks activity: 99 stars, 10 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
Indexed
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- Azhi-ss/academic-figure-skills
- License
- MIT
- Version
- 1.0.0
- Last GitHub push
- Sep 3, 2026
- Registry updated
- Sep 7, 2026
- Instruction path
- academic-figure-architecture-extractor/SKILL.md @ 3e38b08f562c
Version reported in registry metadata; check source releases before relying on it.
Quality
67/100
Promising
Trust
61/100
Sandbox only
Audit
76/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 99 GitHub stars
- Stars/forks activity: 99 stars, 10 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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- Azhi-ss
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