CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

paper-illustration

Generate publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Claude-supervised iterative refinement loop. Use when user says "生成图表", "画架构图", "AI绘图", "paper illustration", "generate diagram", or needs visual figures for papers.

Quality

76%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

High

Do not use without reviewing

Fix and improve this skill with Tessl

tessl review fix ./skills/paper-illustration/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

62%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill's greatest strength is its workflow: a fully specified multi-stage pipeline with strict review scoring, an acceptance gate, and a bounded refinement loop, backed by largely copy-paste-ready scripts. It is undermined by heavy redundancy — style and arrow rules restated four times in two languages — and by a monolithic structure that inlines templates which should live in reference files.

Suggestions

Deduplicate the CVPR style/arrow/color guidance: state it once in the Step 1 prompt template (which is already sent to Gemini) and drop the separate 'CVPR/ICLR/NeurIPS Top-Tier Conference Style Guide' section and repeated 'Key Rules' items to cut the body by roughly half.

Move the Step 1 prompt template and the Step 5 strict-review checklist into references/ files (e.g. references/prompt-template.md, references/review-checklist.md) and link to them, keeping SKILL.md as a concise workflow overview.

Close the actionability gaps: actually create figure_final.png, latex_include.tex, and review_log.json in the Step 4/Step 8 scripts, and parameterize the iteration counter instead of hard-coding 'iteration = 1'.

DimensionReasoningScore

Conciseness

The ~700-line body is noticeably verbose: the CVPR style/arrow/color guidance is restated four times (style guide, prompt template, Step 3 request, Step 5 checklist), the 'Key Rules' section repeats checklist items again, and bilingual Chinese/English lines duplicate the same instruction. It does not explain concepts Claude already knows, so not a 1, but the padding is substantial.

2 / 5

Actionability

Mostly executable: complete bash scripts with real curl calls, Python JSON-payload builders, base64 image extraction, and error checks. Minor gaps remain — INITIAL_PROMPT is a placeholder, the image iteration counter is hard-coded to 'iteration = 1 # Claude increments this each iteration', and figure_final.png/latex_include.tex/review_log.json in the output structure are described but never created by any script.

4 / 5

Workflow Clarity

The eight-step workflow is clearly sequenced with explicit validation checkpoints: pre-flight API-key check, a mandatory strict review with a per-arrow/per-block checklist, a score ≥ 9 gate, a decision point with a refine loop back to Step 2, MAX_ITERATIONS bound, and targeted improvement-prompt generation. Textbook feedback-loop structure.

5 / 5

Progressive Disclosure

Section headers are clear, but the skill is a monolithic 700-line file with no bundle files — the style guide, prompt template, and review-checklist template clearly belong in separate reference files. Referenced paths (tools/extract_paper_style.py, shared-references/integration-contract.md, /overleaf-sync) are external to this bundle and only loosely signaled.

3 / 5

Total

14

/

20

Passed

Description

83%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: third-person, concrete about what the skill produces and how, and equipped with an explicit 'Use when' clause carrying multiple natural trigger phrases in both Chinese and English. Only minor gaps in keyword coverage and slight overlap risk with generic diagramming skills.

DimensionReasoningScore

Specificity

Names the domain (academic papers, Gemini image generation) and several concrete outputs — 'publication-quality AI illustrations', 'architecture diagrams', 'method illustrations' — plus the refinement mechanism, though coverage of figure types is not exhaustive.

4 / 5

Completeness

Explicitly answers both 'what' ('Generate publication-quality AI illustrations... Creates architecture diagrams, method illustrations with Claude-supervised iterative refinement loop') and 'when' ('Use when user says... or needs visual figures for papers') with concrete trigger phrases in third-person voice.

5 / 5

Trigger Term Quality

Good natural-term coverage including bilingual synonyms ('生成图表', '画架构图', 'AI绘图', 'paper illustration', 'generate diagram', 'needs visual figures for papers'); a few common variations (e.g. 'draw figure', 'method diagram') are missing.

4 / 5

Distinctiveness Conflict Risk

The academic-paper-figures niche is clear and bilingual triggers are distinctive, but generic phrases like 'generate diagram' and '画架构图' create minor overlap risk with general diagramming or charting skills.

4 / 5

Total

17

/

20

Passed

Validation

81%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (737 lines); consider splitting into references/ and linking

Warning

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

13

/

16

Passed

Repository

wanshuiyin/Auto-claude-code-research-in-sleep

Path

skills/paper-illustration/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.