Claude Skill

academic-figure-draft-analyzer

Plan evidence-backed figures for a paper draft, markdown notes, outline, manuscript, PDF, or paper webpage. Supports Draft-to-Figure fast-track for Markdown notes as well as comprehensive multi-figure planning for full manuscripts.

LLM Mart · 0 points · 15 0 0

Virus-scanned Reviewed automatically before listing.

Full trust report

Download Azhi-ss-academic-figure-skills-academic-figure-draft-analyzer-3e38b08.zip · 12 KB

azhi-ss/academic-figure-skills 121 13 forks MIT Updated 11d ago

Part of azhi-ss/academic-figure-skills — 5 skills

Install

skills CLI npx skills add https://github.com/Azhi-ss/academic-figure-skills/tree/main/academic-figure-draft-analyzer

Claude Code

claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install azhi-ss-academic-figure-skills@llmmart

Git git clone https://github.com/Azhi-ss/academic-figure-skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole azhi-ss/academic-figure-skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Academic Draft and Paper Analyzer (Figure Planner)

Produce a human-readable figure strategy and a machine-readable Figure Plan v1. Plan figures around the paper's claims and reader questions, not around a fixed count or a generic pipeline template.

Read references/missing-info-policy.md when the paper is incomplete. If a repository handoff or extracted reference-style profile exists, carry it forward without renaming fields.

Dual-Track Planning Workflow

The analyzer operates in one of two modes depending on the input:

Draft-to-Figure Fast-Track (草稿敏捷直出):
- Trigger: Input is notes, an outline, or an early draft (Markdown, text, or draft sections) without complete experimental/analysis results.
- Target: Focus exclusively on Figure 1: Overall Framework / Methodology Overview.
- Policy: Do NOT force a multi-figure plan (ablation/data behavior). Do NOT warn about missing experimental/analysis sections.
Camera-Ready Multi-Figure Plan (完整定稿多图规划):
- Trigger: Input is a complete manuscript with experimental results (Markdown, LaTeX, PDF, or full text).
- Target: Systematically plan the multi-figure suite with claim verification and publication constraints.

Input contract

Prefer: manuscript text or source, abstract, method, experiments, target venue/page limit, semantic architecture handoff, and any reference figures.
Accept: Markdown (.md) draft or notes, outline, title plus abstract, local PDF, paper URL/HTML, Word/LaTeX.
A URL is a paper source only after its content is inspected; do not classify every URL as a code repository.
If a PDF or webpage cannot be read in the current environment, report that limitation rather than inventing paper structure.

Output contract

Include:

Paper overview: question, contribution, evidence, and intended venue constraints.
Completeness statement: sections and artifacts actually inspected.
Per-figure recommendation with a controlled type and must, strong, or nice priority (in Draft mode, emit exactly one primary Figure 1).
A one-sentence communication goal: what the reader should understand after viewing the figure.
Required nodes, edges, authority boundaries, and forbidden implications.
Aspect ratio and final publication width hint.
Figure Plan v1 JSON.

Controlled figure types: Overall Framework, Network Architecture, Module Detail, Comparison/Ablation, Data Behavior, Concept/Motivation, Protocol/Sequence, Timeline/Lifecycle.

Workflow

1. Identify Input Mode and Structure

In Draft Mode (.md notes / outline / unstructured prose / draft):
- Structured Markdown:
  - Headers (#, ##): Primary semantic container zones (e.g. Input Context, Core Framework, Optimization Objective).
  - Lists (1. 2. 3., - ) and arrows (->): Sequential stages, data flow, and pipeline steps.
  - Bold terms (**Name**): Canonical node/module labels (visible_text).
  - Inline code (`B x C x H x W`): Tensor dimensions or data descriptions.
- Unstructured / Plain Prose Notes:
  - Extract entities, transforms, and dataflow by tracing the grammatical subject-verb-object sequence (e.g. "Input X is encoded by Y and supervised by loss Z").
  - Group into 3 canonical panels: Input / Context -> Core Method / Interaction -> Output / Supervision.
- Skip formal claim verification and proceed straight to narrative topology for Figure 1.
In Full Paper Mode:
- Map Introduction, Method, Experiments, Analysis, and Limitations. For each claimed contribution, record the source span and the evidence that could support a visual statement.

2. Assign Visual Jobs

Recommend a figure only when a visual materially improves understanding:

Reader question / Objective	Common type	Typical priority
What is the end-to-end idea and authority/data flow?	Overall Framework	must (Default for Drafts)
What is the conceptual motivation or problem setting?	Concept/Motivation	strong or must
What is the internal executable structure?	Network Architecture	must or strong
How does the central mechanism work?	Module Detail	must or strong
Which choices matter empirically?	Comparison/Ablation	strong
How does behavior change over data, time, or conditions?	Data Behavior	strong or nice
How do components interact over time or protocol?	Protocol/Sequence / Timeline	strong

Do not use venue stereotypes or fixed counts as requirements. For draft notes, emit a single high-impact Figure 1.

3. Design the narrative topology

For each figure specify:

communication_goal and claim_scope;
hero_element: the dominant visual story, not merely the largest box;
required_nodes and required_connections from source evidence or draft structure;
secondary_context that may be dropped under space pressure;
forbidden_claims and forbidden_connections;
authority_boundaries when agents, tools, or external oracles are involved;
text_budget: short labels, with detail reserved for the caption;
reference_style_profile if the user supplied a reference image.

An Overall Framework need not be a left-to-right chain. Choose among a loop, storyboard, asymmetric modular collage, layered authority diagram, central mechanism with satellites, or pipeline according to the scientific story.

4. Set publication geometry

Overall Framework: usually 16:9 or 3:2 at double-column width (183 mm).
Network Architecture / Pipeline: 16:9, 3:2, or tall layout when topology requires it.
Module Detail / Concept: commonly 4:3 or 1:1.
Comparison/Ablation: match the number and reading order of panels.
Data Behavior / Timeline: let axes and sequence determine geometry.

5. Emit Figure Plan v1

{
  "schema": "academic-figure/FigurePlan@1",
  "source_revision": "<paper-or-repo-revision>",
  "venue": null,
  "sources": [],
  "figures": [
    {
      "figure_id": "fig1",
      "figure_type": "Overall Framework",
      "priority": "must",
      "communication_goal": "<one sentence>",
      "claim_scope": ["<evidence-backed claim or draft objective>"],
      "hero_element": "<loop|mechanism|modular collage|pipeline|other>",
      "required_nodes": ["<component-id>"],
      "required_connections": ["<from-id> -> <to-id>: <kind>"],
      "authority_boundaries": [],
      "secondary_context": [],
      "forbidden_claims": [],
      "forbidden_connections": [],
      "aspect_ratio": "16:9",
      "final_width_mm": 183,
      "style_profile_hint": null,
      "reference_assets": [],
      "open_questions": [],
      "confidence": "high|partial|sparse",
      "review_status": "pending|confirmed|waived"
    }
  ]
}

The example defines fields only. Replace every placeholder with sourced content or an explicit null/empty value.

Sparse input & Draft Handling

Single .md draft or notes: Extract pipeline directly from headers and bullet points; plan Figure 1 immediately.
Title and abstract only: plan high-level visual jobs; do not invent submodules.
Method without experiments: plan method framework; do not block or raise spurious errors.
Repository handoff only: produce a system-centric plan and flag narrative review.
Reference image only: analyze visual grammar, but request or locate the target system content before planning its topology.

Stop

Stop after delivering the strategy and Figure Plan v1. Do not generate prompts or images unless the user requested the downstream workflow.

Files (academic-figure-skills)

references

missing-info-policy.md 1 KB

# Missing-Info Policy

Shared by all academic-figure skills. Domain skills add only their own cases.

## Rule

When evidence is incomplete: ship a **conservative, useful** partial result. Label every claim beyond evidence as `推断` or `待确认`. Prefer placeholders over invention.

## Completeness block (every deliverable)

```
- 已分析材料: ...
- 当前输出类型: 完整 / 阶段性 / 局部 / 骨架
- 高置信信息: ...
- 待确认信息: ...
- 建议补充材料: 1–3 highest-value items
```

## Stop vs continue

| Situation | Action |
|-----------|--------|
| Core deliverable possible with placeholders | continue |
| Zero usable source (no paper, repo, figure type, or image) | stop; list minimum materials |
| User asked only this stage | stop after that stage |
| Next stage needs material user has not provided | stop; do not invent |

## Invention ban

Do not invent modules, losses, dimensions, experiment results, or architecture layers that never appear in the source. Rewrite unknowns as explicit placeholders (`[module_name]`, `R^(?×?)`).

palettes.md 20.7 KB

# Academic Palettes — Single Source of Truth

This file defines **12 classic presets**, three pastel-airy schemes, and paired illustrated semantic-zone tokens. Skills that need hex values or style routing should load this file rather than maintaining private copies.

## Decision order

Apply evidence in this order:

1. explicit user colors, style, print, and accessibility requirements
2. supplied reference image grammar
3. hard production constraints such as grayscale output and text contrast
4. figure semantics and visual-zone relationships
5. an existing paper-wide visual system or explicit submission rule
6. conservative default

A reference image is the highest-priority inferred style source. Extract composition, panel surfaces, outline strength, shadow treatment, typography character, icon style, nesting depth, density, arrow grammar, and paired fill/outline colors. Match those properties without copying the reference's labels, branded assets, or method content.

`module_count` is a density clue only. It does not trigger monochrome. Choose hue count from the number and relationship of semantic zones shown in the figure.

| Missing evidence | Conservative default |
|---|---|
| No reference or style cue, classic technical figure | **Okabe-Ito** |
| Explicit airy UI/token figure | **P2 Cool Research** |
| Narrative framework or agent/scientific workflow | **I1 Illustrated Zones** |
| Accessibility unspecified | colorblind-aware dual encoding and validated text contrast |

Always state the branch (`user`, `reference`, `scene`, or `default`) and offer one alternate.

## Style profiles first

Palette values only make sense with a surface and line treatment. Select a profile before assigning tokens.

| Signals | Profile | Primary skill | Visual grammar |
|---|---|---|---|
| technical stack, compact network, classic vector, strict print | **`classic-technical`** | `academic-figure-designer` | restrained geometry, fine borders, white or near-white modules, compact sans labels |
| airy, token flow, interface-like, soft cards | **`pastel-airy-ui`** | `academic-figure-designer` | white cards, subtle border/shadow, floating pills and tokens, generous whitespace |
| hand-drawn academic infographic, modular narrative, agent/scientific workflow, tinted zones | **`illustrated-modular`** | `academic-figure-designer` | asymmetric hero layout, soft semantic-zone fills, strong same-hue outlines, no shadow, one-level subcards, controlled line illustrations |
| supplied reference does not fit one preset | **`reference-led`** | `academic-figure-designer` | override defaults with observed grammar; do not assume an illustrated surface |

Do not force a supplied reference into a binary classic/pastel label. A coherent figure may combine a classic flat canvas, tinted modular zones, and hand-drawn illustrations. State the observed properties so the combination is intentional rather than a style-word mixture.

### Pastel airy UI schemes

Small body text remains neutral `#24323D`. The colored values below are heading accents on white; validate them again before placing small text on a tinted token.

| Scheme | Scene | Soft fills | Accessible heading accents on white |
|---|---|---|---|
| **P1 Warm ML** | playful, teaching, human-centered | `#FFD0D0` `#BBDEFB` `#FFF3C4` `#E1BEE7` `#C8E6C9` | `#A93636` `#146C61` `#6A5ACD` `#2F7430` |
| **P2 Cool Research** | calm token-centric research figure | `#B3E5FC` `#C5CAE9` `#CFD8DC` `#B2DFDB` `#D1C4E9` | `#1565C0` `#3949AB` `#006F65` |
| **P3 Earthy Warm** | natural or embodied visual direction | `#FFE0B2` `#D7CCC8` `#C8E6C9` `#E0E0E0` `#EFEBE9` | `#6D4C41` `#827717` `#2E7D32` |
| **P4 Airy Agentic BO** | surrogate backend, agent decision, charcoal eval anchor | `#FAE8DC` (Peach) `#DBE7FB` (Periwinkle) `#4B5563` (Slate) `#E9E9EC` (Gray) | `#1E293B` `#0F172A` `#FFFFFF` `#334155` |

If a reference is present, derive its token pairs instead of snapping every soft figure to the nearest P1–P4 scheme.

---

## I1 Illustrated Zones — paired semantic tokens

Each token is a coordinated surface system rather than a standalone accent. `title_text` values meet normal-text contrast against their paired fills; small body text may use neutral `#24323D` throughout.

| Token | `soft_fill` | `dark_outline` | `title_text` | `icon_accent` |
|---|---|---|---|---|
| **I1 Blue** | `#EDF4FB` | `#194166` | `#163E64` | `#2E6B9E` |
| **I1 Green** | `#F3FBF0` | `#3B7D23` | `#2F681D` | `#4E8D36` |
| **I1 Peach** | `#FBE3D6` | `#A94417` | `#9E3F13` | `#C65A22` |
| **I1 Purple** | `#F5ECF5` | `#77206E` | `#65185E` | `#8B3B83` |
| **I1 Cyan** | `#DBF3FE` | `#236E96` | `#195876` | `#2F81A8` |
| **I1 Gold** | `#FBF1D1` | `#856B1B` | `#66500F` | `#9B7B1C` |
| **I1 Coral** | `#FDE8E5` | `#B83A2F` | `#8F2A24` | `#C94D42` |

Recommended material treatment: white canvas; 1.5–2.5px zone outlines; 6–14px corner radius scaled to output size; no drop shadows; white or lighter same-hue subcards; dark neutral arrows `#334155` unless the edge itself carries a zone meaning.

## Semantic color binding contract

Bind tokens to the roles present in the current paper and retain those bindings across its figures. The mappings below are customizable defaults across different scientific domains:

### 1) Standard Multi-Stage Pipeline & Modular Systems
| Domain Role / Stage | Suggested Illustrated Token | Classic/Airy Adaptation |
|---|---|---|
| **Stage 1: Input / Raw Data / Context** | I1 Green (`#F3FBF0` / `#3B7D23`) | Green accent / data pill |
| **Stage 2: Representation / Encoders** | I1 Blue (`#EDF4FB` / `#194166`) | Blue outline / primary container |
| **Stage 3: Core Mechanism / Transformation** | I1 Peach (`#FBE3D6` / `#A94417`) | Orange/peach hero zone |
| **Stage 4: Optimization / Supervision / Loss** | I1 Purple (`#F5ECF5` / `#77206E`) | Purple accent / dashed constraint |
| **Stage 5: Output / Evaluation / Benchmark** | I1 Gold (`#FBF1D1` / `#856B1B`) | Gold heading / output badge |

### 2) Deep Learning & Neural Architectures
| Architecture Component | Suggested Illustrated Token | Visual Metaphor / Shape |
|---|---|---|
| **Raw Input / Embeddings / Tokens** | I1 Green | Structured grid, token pill, or feature map |
| **Backbone / Feature Extractor** | I1 Blue | Layered orthogonal blocks or stacked cards |
| **Cross-Modal Fusion / Attention Core** | I1 Peach | Heatmap matrix or bipartite connection web |
| **Loss Function / Objective / Regularizer** | I1 Purple | Mathematical constraint box or curve |
| **Prediction Head / Downstream Task** | I1 Gold | Terminal prediction pill or task badge |

### 3) Agentic & Scientific Interactive Loops
| Agentic Role | Suggested Illustrated Token | Visual Metaphor / Shape |
|---|---|---|
| **Reasoning / Policy / Planner** | I1 Blue | Decision glyph, thought bubble, or planning box |
| **Evidence / Context / Observation** | I1 Green | Document icon, coordinate plot, or context card |
| **Deterministic Harness / Tool Execution**| I1 Peach | Solid process container or simulation box |
| **Advisory / Feedback / Uncertainty** | I1 Purple | Dashed feedback arrow or advisory pill |
| **Memory / Storage / Provenance** | I1 Cyan | Network graph or database/checkpoint cylinder |
| **Final Output / Report** | I1 Gold | Formatted report card or badge |
| **Exception / Guardrail / Stop** | I1 Coral | Warning badge or coral STOP boundary |

### 4) Dual-Fidelity & Bayesian Optimization Loops
| Optimization Role | Suggested Illustrated Token | Visual Metaphor / Shape |
|---|---|---|
| **High-Fidelity / Real-World Experiment / Discrepancy** | I1 Coral (`#FDE8E5` / `#B83A2F`) | Laboratory glassware, oscilloscope/monitor, focused residual peak $\mathcal{X}_R^*$ |
| **Low-Fidelity / LLM Prior / Global Surrogate** | I1 Blue (`#EDF4FB` / `#194166`) | Electronic brain, prompt balloon, 3D smooth GP surface |
| **Candidate Selection / Acquisition Function** | I1 Peach (`#FBE3D6` / `#A94417`) | 1D search curve, peak marker $x^*$, candidate generator table |
| **Gating Criterion / Adaptive Decision** | Decision Diamond (Neutral/Red/Green) | Diamond node $p_\Delta < \tau$, green checkmark / red cross status badges |
| **Prior Domain Knowledge / Constraints** | I1 Green / Slate (`#F3FBF0` / `#3B7D23`) | Literature stack, coordinate scatter plot, constraint box |

Repeated roles reuse a token; adjacent unrelated zones should also differ by label, geometry, or line style.

---

## Scene → profile and palette decision

After explicit user and reference-grammar requirements, apply production constraints and then choose from figure semantics. Venue and domain are suggestions, not guarantees of a single visual style.

### 1) Hard constraints

| Constraint | Choose | Alternate |
|------------|--------|-----------|
| Strict B&W / grayscale print only | **Print-Safe Gray** | Grayscale |
| Theory paper, no color budget | **Grayscale** | Print-Safe Gray |
| Color-vision accessibility required or prudent | Start from Okabe-Ito / ML TopConf Colorblind / a verified monochrome ramp, then dual-encode and test | Never treat a palette name as proof of accessibility |
| Must match existing Matplotlib Tab10 experiment plots | **ML TopConf Tab10** | ML TopConf Colorblind if a11y matters more than match |
| Reference has tinted zones and strong outlines | **Illustrated modular + derived pairs** | I1 Illustrated Zones |

### 2) Figure type

| Figure type | Prefer | Alternate | Why |
|-----------|--------|-----------|-----|
| Overall Framework, technical pipeline | Okabe-Ito | ML TopConf Colorblind | clear categorical accents |
| Overall Framework, modular narrative | **Illustrated modular + I1** | reference-derived pairs | semantic zones and hierarchy |
| Network Architecture | Okabe-Ito or Blue Monochrome | Nature Blue for a restrained single-family stack | structure > decoration |
| Module Detail | **Blue Monochrome** | Okabe-Ito | detail density; gray-print friendly |
| Comparison / Ablation (few panels) | Purple-Green | Okabe-Ito | category contrast |
| Dense multi-panel ablation | **ML TopConf Deep** | Purple-Green | softer multi-hue grid |
| Data Behavior (curves / heatmaps / t-SNE) | ML TopConf Colorblind | Okabe-Ito | series/categories stay separable |
| Qualitative image grids | Okabe-Ito accents only | Grayscale frames | color on labels, not photo washes |

### 3) Venue and domain constraints

Do not map a venue or research domain directly to a palette. Use venue/domain
information only when it supplies a concrete production constraint: an official
grayscale rule, an existing paper-wide color system, a required plot palette,
an accessibility requirement, or a reference figure. Otherwise choose from the
content relationships above. “Nature”, “CVPR”, “biology”, “materials”, or
“robotics” alone is not a color instruction.

### 4) Vibe words → concrete choice

| User says | Family | Palette / scheme |
|-----------|--------|------------------|
| 高级 / 克制 / 顶刊 | ask for observable traits or use a supplied reference | no venue-name palette default |
| 科技感 / 工程感 / 干净 | `classic-technical` | Blue Monochrome or Okabe-Ito |
| 柔和 / 空气感 / token 卡片 | `pastel-airy-ui` | P2 |
| 手绘 / 叙事 / 模块拼图 / agent 框架 | `illustrated-modular` | I1 or reference-derived pairs |
| 活泼 / 教学感 | `pastel-airy-ui` or `illustrated-modular` | P1 or reference-derived pairs |
| 自然 / 生物感 | classic Warm Earth, airy P3, or illustrated zones | state accessibility tradeoff |
| 不要花 | any coherent profile | reduce active semantic zones; monochrome only if hierarchy remains clear |
| 和实验曲线一个色 | `classic-technical` | ML TopConf Tab10 / Colorblind |
| 黑白印刷 | `classic-technical` | Print-Safe Gray / Grayscale |

---

## Worked decision recipes

| Scenario | Family | Palette | One-line reason |
|----------|--------|---------|-----------------|
| Technical three-stage method pipeline, no reference | `classic-technical` | Okabe-Ito | categorical accents and compact geometry |
| Modular agent/scientific framework, no reference | `illustrated-modular` | I1 Illustrated Zones | semantic zones plus asymmetric hierarchy |
| Reference with tinted panels and dark outlines | `reference-led` (observed illustrated grammar) | derived paired tokens | preserve observed visual grammar without assuming every reference is illustrated |
| Dense 2×3 ablation grid | `classic-technical` | ML TopConf Deep | multiple comparable panels |
| Token-flow explainer with white cards | `pastel-airy-ui` | P2 | token-centric surface grammar |
| Continuous single-family mechanism with an explicit restrained-blue preference | `classic-technical` | Nature Blue | user request and hierarchy support one hue family |
| Strict B&W journal output | `classic-technical` | Print-Safe Gray | hard print constraint |
| Human-centered concept diagram | `pastel-airy-ui` or reference-supported `illustrated-modular` | content/reference dependent | distinguish UI cards from narrative zones |
| High-density module detail with no reference | `classic-technical` | Blue Monochrome or accessible custom | detail density and print behavior |
| User: 配色随便 | profile from content; Okabe-Ito/P2/I1 | profile default | safe default branch |

---

## Decision checklist (emit with every Palette Decision)

1. Explicit user constraints recorded.
2. Reference grammar summarized, or `no reference supplied`.
3. Canonical profile: `classic-technical` / `pastel-airy-ui` / `illustrated-modular` / `reference-led`.
4. Hard constraint fired? (print / accessibility / match existing plots).
5. Semantic zones and color carriers identified.
6. Primary + alternate named with exact classic colors or paired tokens.
7. Small-text contrast and grayscale dual encoding checked.
8. Branch stated: `user` / `reference` / `scene` / `default`.


## Palette Decision handoff

Downstream skills consume:

```
style_profile: <classic-technical | pastel-airy-ui | illustrated-modular | reference-led>
style_preset: <named library variant | none>
reference_grammar: <summary | none>
palette_or_token_set: <name>
canvas / body_text / arrow / divider: <hex>
semantic_zone_tokens:
  <role>: {soft_fill: <hex>, dark_outline: <hex>, title_text: <hex>, icon_accent: <hex>}
reason: <one line>
accessibility: colorblind-aware-tested | needs dual encoding/testing | print-only
```

---

## 1. Okabe-Ito — default polychrome

**Use:** general categorical starting palette when several roles need distinct hues; always add non-color cues and test the rendered figure

| role | hex | use |
|------|-----|-----|
| primary | `#0072B2` | core module borders, section labels |
| secondary | `#E69F00` | secondary borders, alternate highlight |
| tertiary | `#009E73` | output / result (sparse) |
| text | `#333333` | body text |
| fill | `#FFFFFF` | canvas / boxes |
| section_bg | `#F7F7F7` | region grouping |
| border | `#767676` | semantic outline (4.54:1 on white) |
| arrow | `#4D4D4D` | arrows / lines |

---

## 2. Blue Monochrome

**Use:** module detail; grayscale-friendly journals

| role | hex |
|------|-----|
| primary | `#1565C0` |
| secondary | `#42A5F5` |
| tertiary | `#90CAF9` |
| text | `#212121` |
| fill | `#FFFFFF` |
| section_bg | `#F5F8FC` |
| border | `#607D8B` |
| arrow | `#37474F` |

---

## 3. Warm Earth

**Use:** explicit earth-toned user/reference direction. Dual-encode; do not infer this palette from a research domain alone.

| role | hex |
|------|-----|
| primary | `#C0392B` |
| secondary | `#E67E22` |
| tertiary | `#F39C12` |
| text | `#2C2C2C` |
| fill | `#FFFFFF` |
| section_bg | `#FDF6EC` |
| border | `#8D6E63` |
| arrow | `#5D4037` |

---

## 4. Purple-Green

**Use:** two-category comparison or ablation when purple/green fits the labels and reference; never bind a hue to “ours” without the spec

| role | hex |
|------|-----|
| primary | `#6A1B9A` |
| secondary | `#2E7D32` |
| tertiary | `#AB47BC` |
| text | `#1A1A1A` |
| fill | `#FFFFFF` |
| section_bg | `#F8F5FC` |
| border | `#7B1FA2` |
| arrow | `#4A148C` |

---

## 5. Grayscale

**Use:** explicit grayscale/print-only requirement or a user-selected austere monochrome treatment

| role | hex |
|------|-----|
| primary | `#212121` |
| secondary | `#616161` |
| tertiary | `#9E9E9E` |
| text | `#111111` |
| fill | `#FFFFFF` |
| section_bg | `#F5F5F5` |
| border | `#757575` |
| arrow | `#424242` |

Distinguish categories by shape / line weight, not hue.

---

## 6. Teal-Coral

**Use:** explicit teal/coral two-category contrast. Dual-encode and test for color-vision deficiencies.

| role | hex |
|------|-----|
| primary | `#00695C` |
| secondary | `#E64A19` |
| tertiary | `#26A69A` |
| text | `#212121` |
| fill | `#FFFFFF` |
| section_bg | `#F0F9F8` |
| border | `#00796B` |
| arrow | `#004D40` |

---

## 7. ML TopConf Tab10

**Use:** match an existing Matplotlib Tab10 experiment palette; do not choose from venue name alone

| role | hex |
|------|-----|
| primary | `#1F77B4` |
| secondary | `#FF7F0E` |
| tertiary | `#2CA02C` |
| text | `#1F2937` |
| fill | `#FFFFFF` |
| section_bg | `#F8FAFC` |
| border | `#64748B` |
| arrow | `#334155` |

---

## 8. ML TopConf Colorblind

**Use:** muted colorblind-aware categorical starting palette; still requires dual encoding and rendered-output checks

| role | hex |
|------|-----|
| primary | `#0173B2` |
| secondary | `#DE8F05` |
| tertiary | `#029E73` |
| text | `#1F2937` |
| fill | `#FFFFFF` |
| section_bg | `#F8FAFC` |
| border | `#64748B` |
| arrow | `#334155` |

---

## 9. ML TopConf Deep

**Use:** multi-panel ablation / dense comparison grids

| role | hex |
|------|-----|
| primary | `#4C72B0` |
| secondary | `#DD8452` |
| tertiary | `#55A868` |
| text | `#1F2937` |
| fill | `#FFFFFF` |
| section_bg | `#F8FAFC` |
| border | `#64748B` |
| arrow | `#334155` |

---

## 10. Print-Safe Gray

**Use:** explicit strict black-and-white print requirement

| role | hex |
|------|-----|
| primary | `#000000` |
| secondary | `#333333` |
| tertiary | `#666666` |
| text | `#333333` |
| fill | `#FFFFFF` |
| section_bg | `#F7F7F7` |
| border | `#666666` |
| arrow | `#4D4D4D` |

---

## 11. Journal Standard

**Use:** figures with several verified categories that genuinely need additional accents; not a journal-name default

| role | hex |
|------|-----|
| primary | `#1F77B4` |
| secondary | `#FF7F0E` |
| tertiary | `#2CA02C` |
| accent1 | `#D62728` |
| accent2 | `#9467BD` |
| accent3 | `#8C564B` |
| text | `#1F2937` |
| fill | `#FFFFFF` |
| section_bg | `#F8FAFC` |
| border | `#64748B` |
| arrow | `#334155` |

Activate only the category colors needed by the current comparison and repeat them consistently.

---

## 12. Nature Blue — restrained monochrome

**Use:** a continuous single-family hierarchy, an explicit restrained-blue direction, matching reference grammar, or verified grayscale-friendly output. Do not select it from module count, venue, or domain alone.

| role | hex |
|------|-----|
| primary | `#1B3A5C` |
| secondary | `#2E6B9E` |
| tertiary | `#5BA0D0` |
| gray | `#8EAEC4` |
| text | `#333333` |
| fill | `#FFFFFF` |
| section_bg | `#F7F7F7` |
| border | `#5B7890` |
| arrow | `#4D4D4D` |

---

## Monochrome vs semantic-zone color

| | monochrome (Blue Monochrome / Nature Blue) | semantic-zone color (Okabe-Ito / I1 / custom pairs) |
|---|---|---|
| visual unity | one hue family | coordinated role-based pairs |
| separation | lightness + border + label | fill/outline pair + label + shape |
| best when | hierarchy within one conceptual family | readers must scan distinct subsystems or decisions |
| print / colorblind | usually robust after value check | robust when dual-encoded and contrast-checked |

---

## Production checks

- Preserve explicit user constraints and reference-image grammar unless accessibility or print requirements require an explained adjustment.
- Use white, near-white, or soft tinted panel surfaces according to the selected profile; tinted semantic zones are valid academic material.
- Use the fewest semantic tokens that keep roles easy to scan, without an arbitrary module-count or three-hue cutoff.
- Dual-encode important categories with label, shape, border, icon, or line style in addition to color.
- Normal-size text and its actual background should meet a 4.5:1 contrast target; do not use pale accent colors for small text.
- Essential outlines, arrow shafts/heads, markers, and focus boundaries should meet a 3:1 graphical contrast target against adjacent colors. Lighter dividers may be decorative only and must not carry meaning.
- Avoid unintentional gradients, glossy 3D chrome, photorealistic decoration, and rainbow ordering. Follow a supplied reference when a different treatment is deliberate and legible.
- Check the downscaled figure and a grayscale preview before handoff.

## Custom palette minimum

```
style_profile: <name>
canvas: #XXXXXX
body_text: #XXXXXX
arrow: #XXXXXX
semantic_zone:
  soft_fill: #XXXXXX
  dark_outline: #XXXXXX
  title_text: #XXXXXX
  icon_accent: #XXXXXX
```

Add only the semantic zones the figure needs. Validate text contrast, colorblind distinguishability, and grayscale reproduction before handoff.

SKILL.md 7.8 KB

---
name: academic-figure-draft-analyzer
description: Plan evidence-backed figures for a paper draft, markdown notes, outline, manuscript, PDF, or paper webpage. Supports Draft-to-Figure fast-track for Markdown notes as well as comprehensive multi-figure planning for full manuscripts.
metadata:
  version: "1.4.0"
  stages: [research, review]
---

# Academic Draft and Paper Analyzer (Figure Planner)

Produce a human-readable figure strategy and a machine-readable **Figure Plan v1**. Plan figures around the paper's claims and reader questions, not around a fixed count or a generic pipeline template.

Read `references/missing-info-policy.md` when the paper is incomplete. If a repository handoff or extracted reference-style profile exists, carry it forward without renaming fields.

## Dual-Track Planning Workflow

The analyzer operates in one of two modes depending on the input:

1. **Draft-to-Figure Fast-Track (草稿敏捷直出)**:
   - **Trigger**: Input is notes, an outline, or an early draft (Markdown, text, or draft sections) without complete experimental/analysis results.
   - **Target**: Focus exclusively on **Figure 1: Overall Framework / Methodology Overview**.
   - **Policy**: Do NOT force a multi-figure plan (ablation/data behavior). Do NOT warn about missing experimental/analysis sections.
2. **Camera-Ready Multi-Figure Plan (完整定稿多图规划)**:
   - **Trigger**: Input is a complete manuscript with experimental results (Markdown, LaTeX, PDF, or full text).
   - **Target**: Systematically plan the multi-figure suite with claim verification and publication constraints.

## Input contract

- Prefer: manuscript text or source, abstract, method, experiments, target venue/page limit, semantic architecture handoff, and any reference figures.
- Accept: Markdown (`.md`) draft or notes, outline, title plus abstract, local PDF, paper URL/HTML, Word/LaTeX.
- A URL is a paper source only after its content is inspected; do not classify every URL as a code repository.
- If a PDF or webpage cannot be read in the current environment, report that limitation rather than inventing paper structure.

## Output contract

Include:

1. Paper overview: question, contribution, evidence, and intended venue constraints.
2. Completeness statement: sections and artifacts actually inspected.
3. Per-figure recommendation with a controlled type and `must`, `strong`, or `nice` priority (in Draft mode, emit exactly one primary Figure 1).
4. A one-sentence communication goal: what the reader should understand after viewing the figure.
5. Required nodes, edges, authority boundaries, and forbidden implications.
6. Aspect ratio and final publication width hint.
7. `Figure Plan v1` JSON.

Controlled figure types: `Overall Framework`, `Network Architecture`, `Module Detail`, `Comparison/Ablation`, `Data Behavior`, `Concept/Motivation`, `Protocol/Sequence`, `Timeline/Lifecycle`.

## Workflow

### 1. Identify Input Mode and Structure

- **In Draft Mode (`.md` notes / outline / unstructured prose / draft)**:
  - **Structured Markdown**:
    - Headers (`#`, `##`): Primary semantic container zones (e.g. Input Context, Core Framework, Optimization Objective).
    - Lists (`1. 2. 3.`, `- `) and arrows (`->`): Sequential stages, data flow, and pipeline steps.
    - Bold terms (`**Name**`): Canonical node/module labels (`visible_text`).
    - Inline code (`` `B x C x H x W` ``): Tensor dimensions or data descriptions.
  - **Unstructured / Plain Prose Notes**:
    - Extract entities, transforms, and dataflow by tracing the grammatical subject-verb-object sequence (e.g. "Input X is encoded by Y and supervised by loss Z").
    - Group into 3 canonical panels: *Input / Context* -> *Core Method / Interaction* -> *Output / Supervision*.
  - Skip formal claim verification and proceed straight to narrative topology for Figure 1.
- **In Full Paper Mode**:
  - Map Introduction, Method, Experiments, Analysis, and Limitations. For each claimed contribution, record the source span and the evidence that could support a visual statement.

### 2. Assign Visual Jobs

Recommend a figure only when a visual materially improves understanding:

| Reader question / Objective | Common type | Typical priority |
|---|---|---|
| What is the end-to-end idea and authority/data flow? | Overall Framework | must (Default for Drafts) |
| What is the conceptual motivation or problem setting? | Concept/Motivation | strong or must |
| What is the internal executable structure? | Network Architecture | must or strong |
| How does the central mechanism work? | Module Detail | must or strong |
| Which choices matter empirically? | Comparison/Ablation | strong |
| How does behavior change over data, time, or conditions? | Data Behavior | strong or nice |
| How do components interact over time or protocol? | Protocol/Sequence / Timeline | strong |

Do not use venue stereotypes or fixed counts as requirements. For draft notes, emit a single high-impact Figure 1.

### 3. Design the narrative topology

For each figure specify:

- `communication_goal` and `claim_scope`;
- `hero_element`: the dominant visual story, not merely the largest box;
- `required_nodes` and `required_connections` from source evidence or draft structure;
- `secondary_context` that may be dropped under space pressure;
- `forbidden_claims` and `forbidden_connections`;
- `authority_boundaries` when agents, tools, or external oracles are involved;
- `text_budget`: short labels, with detail reserved for the caption;
- `reference_style_profile` if the user supplied a reference image.

An Overall Framework need not be a left-to-right chain. Choose among a loop, storyboard, asymmetric modular collage, layered authority diagram, central mechanism with satellites, or pipeline according to the scientific story.

### 4. Set publication geometry

- Overall Framework: usually 16:9 or 3:2 at double-column width (183 mm).
- Network Architecture / Pipeline: 16:9, 3:2, or tall layout when topology requires it.
- Module Detail / Concept: commonly 4:3 or 1:1.
- Comparison/Ablation: match the number and reading order of panels.
- Data Behavior / Timeline: let axes and sequence determine geometry.

### 5. Emit Figure Plan v1

```json
{
  "schema": "academic-figure/FigurePlan@1",
  "source_revision": "<paper-or-repo-revision>",
  "venue": null,
  "sources": [],
  "figures": [
    {
      "figure_id": "fig1",
      "figure_type": "Overall Framework",
      "priority": "must",
      "communication_goal": "<one sentence>",
      "claim_scope": ["<evidence-backed claim or draft objective>"],
      "hero_element": "<loop|mechanism|modular collage|pipeline|other>",
      "required_nodes": ["<component-id>"],
      "required_connections": ["<from-id> -> <to-id>: <kind>"],
      "authority_boundaries": [],
      "secondary_context": [],
      "forbidden_claims": [],
      "forbidden_connections": [],
      "aspect_ratio": "16:9",
      "final_width_mm": 183,
      "style_profile_hint": null,
      "reference_assets": [],
      "open_questions": [],
      "confidence": "high|partial|sparse",
      "review_status": "pending|confirmed|waived"
    }
  ]
}
```

The example defines fields only. Replace every placeholder with sourced content or an explicit null/empty value.

## Sparse input & Draft Handling

- Single `.md` draft or notes: Extract pipeline directly from headers and bullet points; plan Figure 1 immediately.
- Title and abstract only: plan high-level visual jobs; do not invent submodules.
- Method without experiments: plan method framework; do not block or raise spurious errors.
- Repository handoff only: produce a system-centric plan and flag narrative review.
- Reference image only: analyze visual grammar, but request or locate the target system content before planning its topology.

## Stop

Stop after delivering the strategy and Figure Plan v1. Do not generate prompts or images unless the user requested the downstream workflow.