# 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. - Skill: `azhi-ss/academic-figure-draft-analyzer` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add azhi-ss/academic-figure-draft-analyzer` - Raw SKILL.md: https://api.skillmd.com/api/skills/azhi-ss/academic-figure-draft-analyzer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: azhi-ss (https://skillmd.com/u/azhi-ss) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/azhi-ss/academic-figure-draft-analyzer --- # 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": "", "venue": null, "sources": [], "figures": [ { "figure_id": "fig1", "figure_type": "Overall Framework", "priority": "must", "communication_goal": "", "claim_scope": [""], "hero_element": "", "required_nodes": [""], "required_connections": [" -> : "], "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.