Xiaohei Hand-Drawn Illustrations Skill
Turn judgments, flows, states, and metaphors in Chinese articles into 16:9 white-background hand-drawn quirky illustrations.
- Source repo
- helloianneo/ian-xiaohei-illustrations
- Stars
- ★ 13k
- Last updated
- 24d ago
- License
- MIT
- FA score
- 34/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specificCodex
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- A Chinese blogger writing articles needs consistent, memorable illustrations for each key section.
- Not a fit if
- Teams that need commercial illustration, brand key visuals, or polished flat art
- Users who want traditional PPT infographics, complex architecture diagrams, or flowcharts
- Anyone who needs strictly editable vector source files
- Source review
- 34/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
Ian Xiaohei Illustrations is a Codex Skill that guides an AI agent to generate in-article illustrations for Chinese articles, posts, blogs, and Notion documents. It uses a default visual IP called 'Xiaohei': a solid black, white-dotted-eye, thin-legged, expressionless character. The style features a pure white background, black hand-drawn line art, generous whitespace, and sparse red/orange/blue handwritten Chinese annotations. The core methodology is to first extract cognitive anchors from the article, then choose a single judgment, process, structure, or metaphor per image, invent a low-tech, quirky but plausible physical metaphor, and make Xiaohei actively participate in the core action. Output is a 16:9 landscape PNG saved to the workspace's assets/<article-slug>-illustrations/ directory. The skill itself lives in the subdirectory ian-xiaohei-illustrations/, containing SKILL.md, agents/openai.yaml, and references/ with style guides and QA checklists.
The skill directs Codex to: read provided articles, Markdown, Notion content, or user topics; distill key insights and visualizable sections; output a shot list with theme, core meaning, structure type, Xiaohei's action, and suggested Chinese annotations; choose a structure type (e.g., Workflow, system partial, before/after contrast, conceptual metaphor); invent a physical metaphor; call an image model to generate 16:9 pure-white-background PNGs; finally verify against a QA checklist and save to assets/<article-slug>-illustrations/. It is triggered by 'Use $ian-xiaohei-illustrations' in Codex.
- A Chinese blogger writing articles needs consistent, memorable illustrations for each key section.
- A creator producing methodology content wants to visualize abstract concepts like 'trust is paved with evidence' as concrete metaphors.
- A user leveraging Codex for content production wants to reuse a consistent hand-drawn visual language without redesigning each time.
- An editor wants to plan 4-8 illustration ideas (shot list) before generating images, to review placements and themes.
- An author prefers a quirky, clean illustration style over PPT-style infographics, wanting a light, recognizable look.
How do you install or deploy this agent?
Clone the repository: git clone https://github.com/helloianneo/ian-xiaohei-illustrations.git; enter the directory: cd ian-xiaohei-illustrations; create the skills directory and copy the subdirectory: mkdir -p "${CODEX_HOME:-$HOME/.codex}/skills" && cp -R ./ian-xiaohei-illustrations "${CODEX_HOME:-$HOME/.codex}/skills/". Requires Codex installed and CODEX_HOME set or default path.
How do you use this agent?
After installation, invoke in Codex: 'Use $ian-xiaohei-illustrations to generate 4 Xiaohei quirky illustrations for the following article.' Or for planning only: 'Use $ian-xiaohei-illustrations without generating images. Analyze where this article deserves illustrations and output a shot list of about 5 images.' Generated images are saved to the workspace's assets/<article-slug>-illustrations/ directory.
What are this agent's strengths and limitations?
- Highly customized visual style with detailed style guides (SKILL.md, references/style-dna.md) and Xiaohei IP specifications, ensuring consistency.
- Focused on Chinese article illustrations, with optimization for Chinese text stability (recommends short text).
- Provides a structured workflow and QA checklist to reduce common image-generation issues (typos, hallucinated labels).
- Works only on Codex platform; not directly usable in other agent environments.
- Requires manual installation into Codex skills directory, involving cloning and copying steps.
- Image generation relies on external image models, may produce typos or style drift, and output is uneditable PNG.
How does this agent compare with similar options?
The README mentions related projects: Ian Handdrawn PPT (hand-drawn PPT-style page generation skill), Awesome Claude Code Skills (curated Claude Code skills list), and Obsidian + Claude AI Second Brain (knowledge base guide), but does not explicitly compare alternatives.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Xiaohei Hand-Drawn Illustrations Skill This agent | 34 · Major gaps | Agent plugin / skillFree + model costs | ★ 13k | 24d ago | — | Codex |
| md2wechat: AI-Agent CLI for WeChat Official Account Publishing | 47 · Major gaps | CLIFreemium | ★ 3.7k | 4d ago | Go | Codex · Claude Code |
| AIWriteX Content Studio | 43 · Major gaps | Desktop appFree + model costs | ★ 2.1k | 1mo ago | Python | — |
| Wonda CLI | 56 · Major gaps | CLIPaid | ★ 158 | 6d ago | TypeScript | Codex · Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence: The repository contains no code or executable files, only documentation and example images, so there are no signs of permission requests, data collection, or external interactions. Deductions: With no code, least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, and source attribution cannot be assessed; all criteria are unmet.
Evidence: The workflow and outputs described in README and SKILL.md are consistent, and example images match the style description. Deductions: Dependencies are not explicitly listed, only mentioning image models and Codex; failure messages are not provided, only QA checks are mentioned.
Evidence: README clearly states target audience, suitable and unsuitable scenarios, and provides multiple usage examples. Deductions: Environment fit is only for Codex, not other platforms; trigger precision is not explicitly defined, but examples provide trigger methods.
Evidence: Directory structure is clear, installation instructions are detailed, examples and FAQ exist, known limitations are listed in notes, and license is MIT. Deductions: No versioning or changelog, maintenance responsibility is only implied via author info, not explicitly stated.
Evidence: Output is PNG images, directly usable, examples show effect; marginal value lies in unique visual style and workflow. Deductions: Cost-benefit not quantified, no cost or performance data.
Evidence: Claims in README are consistent with example images and file structure, but no external verification. Deductions: No cross-source corroboration, facts and inferences not clearly separated.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- Repository contains only documentation and examples, no actual code, so functionality cannot be verified.
- Dependencies are not explicitly listed, which may affect reproducibility.
- No versioning or changelog provided, maintenance status unclear.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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Release notes excerpt
Initial public release of Ian Xiaohei Illustrations.\n\nIncludes:\n- Installable Codex Skill under ian-xiaohei-illustrations/\n- Public README with installation and usage examples\n- 8 GitHub showcase images\n- 14 bundled style calibration examples\n- References for style DNA, Xiaohei IP, composition patterns, prompt template, and QA checklistView on GitHub ↗