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c-narcissus

Inspiration / Case Figure Guide

by c-narcissus · GitHub ↗ · v2.7.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install inspiration-case-figure-guide
Description
Design and refine publication-facing research paper figures from drafts, abstracts, reviewer comments, or short method descriptions. Use when the user needs...
README (SKILL.md)

Inspiration / Case Figure Guide

Use this skill as a stateful, human-in-the-loop figure director for research paper figures. Start from the intended reader effect and paper logic, then choose figure role, layout, visual rhetoric, style family, and image-generation brief.

Load only the reference files needed for the current step. Do not bulk-load all references.

Hard Rules

  • Do not produce SVG, Mermaid, TikZ, Graphviz, HTML/CSS, matplotlib, or other code-rendered output as the final figure.
  • Use the host's native OpenAI image-generation capability for final visuals when available. If image generation is unavailable, stop before generation and provide a copyable prompt handoff.
  • Keep each turn to one modality:
    • TEXT_ONLY: analysis, plan, state update, brief, prompt, critique, or question. No image generation.
    • IMAGE_ONLY: image generation only. No prose.
  • Do not fake image generation or claim an image was generated when no image tool was used.
  • Ask for optional reference images at useful decision points, but never make them required.
  • Analyze reference images as design evidence. Extract layout, hierarchy, density, label strategy, color semantics, and transferable visual grammar; do not copy them exactly.
  • Give an opinionated default recommendation in every planning or selection reply, and state what would make another option better.
  • Put copyable next-turn user prompts only in the final section named 下一步你可以这样问.

First Trigger Gate

If this is a new figure-design task and there is no active state with start_confirmed: true, the first reply must be STARTUP_PLAN_ONLY.

In that first reply:

  1. Show 当前执行计划 near the beginning with 当前处于:第 0/N 步 — 启动确认与流程预览.
  2. Preview the complete workflow and explain what each step will do.
  3. Briefly say what can be inferred from the user's material and what the user may optionally provide.
  4. Mention that reference images can help but are optional.
  5. Recommend the default route for proceeding directly.
  6. Do not perform substantive figure analysis, scheme ranking, prompt construction, or image generation yet.
  7. End with 当前状态与产物 and 下一步你可以这样问.

Treat confirmation phrases such as "开始", "继续", "确认开始", "直接开始", "按默认路线开始", or a new paper/material bundle plus a request to proceed as confirmation. Then set start_confirmed: true and move to intake.

For the full gate behavior, read references/startup-confirmation-gate-protocol.md.

Visible Plan And State

Every TEXT_ONLY reply after the gate must include a visible 当前执行计划 block near the beginning. Include:

  • 当前处于:第 X/Y 步 — \x3Cstep name>
  • 本轮目标:...
  • 计划步骤:...
  • 本轮是否调整计划:无 / 因为...,调整为...

Every TEXT_ONLY reply must also end with:

## 当前状态与产物
- state_version: v2.7
- start_confirmed:
- 当前处于计划第 X/Y 步:
- working_plan:
- fixed_decisions:
- changed_assumptions:
- recommended_default:
- reference_image_status:
- artifacts_so_far:
- immediate_next_action:

## 下一步你可以这样问
1. 请根据引导skill以及当前的状态,继续...
2. ...

Make the visible plan, footer, default recommendation, and final prompt suggestions consistent. Read these references when the conversation involves continuation, recovery, or plan changes:

  • references/planning-and-state-update-protocol.md
  • references/plan-step-visibility-protocol.md
  • references/state-and-turn-contract.md
  • references/session-state-schema-v2.md
  • references/next-step-consistency-protocol.md

Core Workflow

  1. Startup confirmation gate.
  2. Intake and source readiness: identify paper material, figure slot, missing inputs, and optional reference images.
  3. Figure Effect Contract: define what the reader should understand in 10 seconds and 60 seconds, and what misconception the figure must prevent.
  4. Paper compression and bottleneck diagnosis: compress claim, gap, mechanism, evidence, and the main explanation bottleneck.
  5. Figure opportunity map: compare plausible figure roles and recommend one default direction.
  6. Candidate scheme generation: produce multiple text schemes with reader effect, layout skeleton, content units, style risk, and reviewer risk.
  7. Selection and locking: choose or combine a scheme; update fixed decisions.
  8. Content architecture and panel choreography: define reading order, labels, panel count, hierarchy, and aspect ratio.
  9. Visual decision rounds: use a figure-direction, layout, style, metaphor, or density board when the next choice is easier to make visually.
  10. Image brief and prompt: prepare a generation-ready brief and negative constraints.
  11. IMAGE_ONLY generation: generate the agreed candidate batch only after a text-only brief turn.
  12. Review and final package: diagnose the image, revise if needed, then provide title, caption draft, callout labels, and paper-integration notes.

Use references/request-template.md or references/user-input-bundle-template.md to compress incomplete user input.

Figure Direction References

For paper-logic and figure-type decisions, read only the relevant files:

  • references/human-best-practice-methodology.md: high-level method for top-tier inspiration/case figures.
  • references/taxonomy-reference.md: reader question, gap type, narrative role, rhetoric, style, density, and risk taxonomy.
  • references/inspiration-case-patterns.md: inspiration-source and case schematic pattern families.
  • references/figure-scheme-patterns.md: reusable figure schemes such as motivation contrast, toy storyboard, method pipeline, and idea-to-model bridge.
  • references/design-principles-by-type.md: detailed design principles by figure type.
  • references/recommendation-and-reference-image-protocol.md: default recommendation and reference-image handling.

Visual Decision Boards

Do not force the user to choose category, layout, style, metaphor, or density from prose only when seeing candidates would be more informative.

Before a visual board, use a TEXT_ONLY reply to specify:

  • what stays fixed
  • what varies
  • how many candidates will be generated
  • what the user should compare
  • the recommended default if the user wants to proceed

The following turn may be IMAGE_ONLY.

Typical board choices:

  • Figure-direction board: 3-5 candidates with different figure roles.
  • Layout board: 3-5 panel skeletons with the same role and style.
  • Style board: 4-8 style families when style is a live decision.
  • Metaphor board: 3-5 visual metaphors.
  • Density board: 2-4 variants from sparse hero to denser evidence board.

Read these files when visual choices are active:

  • references/visual-first-decision-board-protocol.md
  • references/visual-decision-protocol.md
  • references/visual-style-taxonomy-and-selection.md
  • references/image-generation-policy.md

Image Brief Contract

A generation brief must include:

  • figure role and paper slot
  • primary reader effect
  • central claim/gap/mechanism
  • anchor case or analogy
  • layout skeleton and reading path
  • panel count and aspect ratio
  • style family and risk controls
  • color semantics
  • label and text-density rules
  • candidate count
  • negative constraints

For reusable wording, read references/prompt-library.md.

Completion Criteria

The task is complete when the user has one or more of these artifacts:

  • locked figure thesis and role
  • selected candidate scheme or visual board direction
  • generation-ready image prompt
  • generated candidate image batch
  • review diagnosis and revision plan
  • final title, caption, callout labels, and paper-placement notes

ClawHub Safety Notes

This is an instruction-only skill. It declares no environment variables, no CLI binaries, no install steps, and no external service credentials. Publish it under MIT-0 only; do not add conflicting license language.

Usage Guidance
This skill is an instruction-only, self-contained figure-design workflow and appears coherent with its stated goal. Before installing or using it: 1) remember that any drafts or reference images you upload to the agent may be sent to the host's image-generation provider (the skill explicitly prefers the host's OpenAI/ChatGPT Images capability), so avoid sending highly sensitive or unpublished data you are not comfortable sharing externally; 2) no credentials are requested by the skill, but confirm your agent's platform policy for image-generation calls and data retention; 3) the skill enforces structured Chinese-language plan/state templates — expect the assistant to require/produce those blocks in replies; 4) because the skill can be invoked autonomously by the agent (default), grant it only if you trust the agent to operate with your data-sharing preferences. Overall this package is internally consistent and does not show incoherent or suspicious requests.
Capability Analysis
Type: OpenClaw Skill Name: inspiration-case-figure-guide Version: 2.7.0 The skill bundle is a highly structured framework for designing scientific research figures. It employs a sophisticated state-management system via a mandatory markdown footer to maintain context across turns. Analysis of the instructions and reference files (such as SKILL.md and the protocols in the references/ directory) shows no evidence of malicious intent, data exfiltration, or unauthorized execution. Notably, the skill explicitly prohibits the generation of code-based visuals (SVG, TikZ, Mermaid) in favor of native image generation, which effectively reduces the risk of code-injection vulnerabilities. The behavior is entirely consistent with its stated purpose as a human-in-the-loop design assistant.
Capability Tags
crypto
Capability Assessment
Purpose & Capability
The name/description (figure design/refinement) match the SKILL.md content and the included references. No unrelated binaries, credentials, or config paths are requested.
Instruction Scope
All runtime instructions are about figure design workflows, state tracking, and use of the host's image-generation capability. The skill references only its bundled reference files and mandates visible planning/state footers; it does not instruct reading arbitrary system files, accessing credentials, or calling external endpoints beyond using the host's native image-generation tool (expected for image briefs).
Install Mechanism
No install spec or code files are present; this is an instruction-only skill, so nothing is written to disk or downloaded at install time.
Credentials
The skill declares no required environment variables or credentials. It does instruct using the host's image-generation capability but does not request keys or unrelated secrets.
Persistence & Privilege
Flags show normal defaults (always: false, user-invocable: true, autonomous invocation allowed). The skill does not request permanent presence or system-level config changes.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install inspiration-case-figure-guide
  3. After installation, invoke the skill by name or use /inspiration-case-figure-guide
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v2.7.0
Version 2.7.0 — Major revision with updated references, streamlined workflow, and improved state handling. - Removed outdated files; added new protocol and template references for modern workflow support. - Restructured skill description and workflow for clarity and step-by-step guidance. - Expanded and refined state and plan visibility rules; updated turn and footer contracts. - Shifted to load references on-demand per step; no more bulk or unused reference loading. - Upgraded visual decision and prompt generation process, including new visual-first board protocol. - Made reference images optional and clarified analysis strategy for user-provided visuals.
v2.6.0
Version 2.6.0 summary: Major upgrade introducing a stateful, confirmation-gated workflow, expanded best practice references, and strict output and planning contracts. - Added 12 reference assets and protocols to enforce planning consistency, state recovery, visual decision rules, and prompt design. - Introduced a structured, stepwise workflow with startup confirmation gating, explicit state schema, and active plan display at each step. - Upgraded the skill description, text turn contract, and workflow to cover effect-driven figure planning, candidate scheme generation, style selection, and OpenAI-native image generation. - Updated rules to require a plan/state summary at the top and copyable next-step prompts in each text response. - Clarified strict prohibition of SVG/code-based figure outputs and any non-native image generation workarounds. - Enhanced guidance for using reference images, labeling, and handling user input bundles.
v1.0.0
- Initial release of inspiration-case-figure-guide skill. - Guides users step-by-step from raw research drafts or descriptions to concrete, prompt-ready figure plans. - Supports creation of inspiration-source figures, case schematics, toy examples, and idea-to-model diagrams—while banning SVG/code-based figures as output. - Ensures every text response is stateful and includes clear next-step guidance and suggestions for generating multiple visual candidates. - Designed for integration with ChatGPT Images 2.0 or compatible image generation tools.
Metadata
Slug inspiration-case-figure-guide
Version 2.7.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 3
Frequently Asked Questions

What is Inspiration / Case Figure Guide?

Design and refine publication-facing research paper figures from drafts, abstracts, reviewer comments, or short method descriptions. Use when the user needs... It is an AI Agent Skill for Claude Code / OpenClaw, with 101 downloads so far.

How do I install Inspiration / Case Figure Guide?

Run "/install inspiration-case-figure-guide" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Inspiration / Case Figure Guide free?

Yes, Inspiration / Case Figure Guide is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Inspiration / Case Figure Guide support?

Inspiration / Case Figure Guide is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Inspiration / Case Figure Guide?

It is built and maintained by c-narcissus (@c-narcissus); the current version is v2.7.0.

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