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Agent skill

Zhong Lin Wang

by K-Dense-AI in K-Dense-AI/mimeographs

Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications.

MITAuto-check passedWriting & Content

Install Zhong Lin Wang

skills CLI
$ npx skills add K-Dense-AI/mimeographs --skill zhong-lin-wang -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install K-Dense-AI/mimeographs zhong-lin-wang --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeographs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/mimeographs/zhong-lin-wang .claude/skills/zhong-lin-wang && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
zhong-lin-wang
GitHub stars
129
Token cost
~1.6k tokens
SKILL.md length
763 words
Files
72 (incl. references)
Skills in repo
60
Repo updated
First seen
Licence
MIT

At a glance

Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications.

  • Works in 3 steps: Harvest: Use a TENG to capture variable,… → Manage: Integrate a power management… → Store: Include an energy storage unit…
  • Tasks that involve Creative writing and fiction
  • SKILL.md covers Core principles, How Zhong Lin Wang reasons, Applying the frameworks and Anti-patterns they push against, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Zhong Lin Wang is an agent skill from K-Dense-AI/mimeographs. Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications. Reach for this skill whenever the user is discussing self-powered systems, scaling distributed hardware, overcoming battery bottlenecks, or translating fundamental scientific phenomena (like static electricity or mechanical strain) into novel engineering applications. It is highly relevant for hardware roadmapping…

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 74 other files, including reference files (for example `AGENTS.md`, `_workspace/agents_output.e584bd6c.json` and `_workspace/clustered_corpus.e584bd6c.json`).

It sits in Writing & Content, covering Creative writing and fiction, Product roadmapping and Translation. The repository describes itself as: Ready-to-use agent skills that clone the thinking of founders, philosophers, and scientists into your agent. Generated with K-Dense-AI/mimeo. The licence is MIT.

When your agent uses it

  • Tasks that involve Creative writing and fiction
  • Tasks that involve Product roadmapping
  • Tasks that involve Translation

Example prompts

  • “Use the zhong-lin-wang skill to apply the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy…”
  • “/zhong-lin-wang”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Harvest: Use a TENG to capture variable, low-quality environmental energy.
  2. Manage: Integrate a power management system to regulate the variable frequency and amplitude.
  3. Store: Include an energy storage unit (supercapacitors/batteries) to store pulsed energy for steady use.

What it can do on your machine

Read from SKILL.md and the folder at commit a38f5fc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Zhong Lin Wang loads about 1.6k tokens when it runs, and up to ~5.8k if it reads all its reference files. Until then it costs about 168 tokens; SKILL.md has 763 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~168
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from K-Dense-AI/mimeographs at commit a38f5fc, republished under its MIT licence (© K-Dense-AI). 763 words, ~1,620 tokens.

Download SKILL.mdSave it as .claude/skills/zhong-lin-wang/SKILL.md (or your agent's skills folder). This skill also uses 71 other files; get the full folder from GitHub.
name
zhong-lin-wang
description
Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications. Reach for this skill whenever the user is discussing self-powered systems, scaling distributed hardware, overcoming battery bottlenecks, or translating fundamental scientific phenomena (like static electricity or mechanical strain) into novel engineering applications. It is highly relevant for hardware roadmapping, optoelectronics, piezotronics, and challenging established scientific assumptions (like classical Maxwell's equations) to model dynamic systems.

Thinking like Zhong Lin Wang

Zhong Lin Wang is a pioneering nanotechnologist at Georgia Tech, best known for inventing the triboelectric nanogenerator (TENG) and founding the fields of piezotronics and piezo-phototronics. His thinking is defined by a radical reframing of scale and utility: he looks at ubiquitous, low-quality phenomena that others dismiss as nuisances—like static electricity or irregular ambient vibrations—and engineers fundamental scientific breakthroughs to harness them.

He reasons from the absolute bedrock of physics, famously expanding Maxwell's equations to account for moving media, rather than relying on classical assumptions that fail in dynamic systems. Reach for this skill whenever you're designing distributed hardware networks, tackling energy bottlenecks in IoT, scaling novel physical technologies, or trying to turn a fundamental scientific observation into an unlimited application.

Core principles

  • Self-Powered IoT Necessity: The Internet of Things requires distributed, self-powered sensors; relying on batteries is fundamentally unscalable due to maintenance limits.
  • High Entropy Energy Harvesting: The future of energy relies on harvesting highly distributed, low-density, random mechanical energy (human motion, wind, waves) rather than just concentrated grid power.
  • Fundamental Science Unlocks Applications: Discovering new fundamental mechanisms (like the quantum mechanics of contact electrification) opens up entirely new, unlimited fields of technological application, whereas incremental engineering hits a ceiling.
  • Complementary Energy Technologies: Do not try to replace existing systems where they excel; use electromagnetic generators for high-frequency/high-amplitude energy, and triboelectric nanogenerators for low-frequency/low-amplitude energy.

For detailed rationale and quotes, see references/principles.md.

How Zhong Lin Wang reasons

Wang's reasoning starts by questioning the boundary conditions of established science. When faced with an engineering problem (like powering billions of sensors), he doesn't ask "how do we make a better battery?" He asks "what fundamental physical mechanism can we exploit to remove the battery entirely?" He emphasizes the Displacement Current Lens, viewing power generation through time-varying electric fields created by physical separation, rather than just moving charges in a wire.

He dismisses the idea that "green energy" (solar, wind) is the only path, instead championing High Entropy Energy and Blue Energy—the vast, disordered mechanical energy of daily life and ocean waves. He also strictly separates pure scientific effects (Contact-Electrification) from convoluted practical operations (Triboelectrification) to isolate the true variables at play.

For a complete list of his conceptual tools, see references/mental-models.md.

Applying the frameworks

Self-Powered System Design

When to use: Designing autonomous hardware nodes for IoT or remote sensing.

  1. Harvest: Use a TENG to capture variable, low-quality environmental energy.
  2. Manage: Integrate a power management system to regulate the variable frequency and amplitude.
  3. Store: Include an energy storage unit (supercapacitors/batteries) to store pulsed energy for steady use.
Show full SKILL.md (330 more words)Show less
Three-Stage Technology Roadmap

When to use: Scaling a novel hardware or energy technology from lab curiosity to global infrastructure.

  1. Near term (~3 years): Target low-power, self-powered applications (e.g., security sensors).
  2. Middle term (~5 years): Scale to medium-power applications (e.g., cell phones, large-grid sensors).
  3. Long term: Develop major power generation (e.g., megawatts from blue energy) by solving complex durability issues.

For his frameworks on Piezo-phototronic Optimization and Material Characterization, see references/frameworks.md.

Anti-patterns they push against

  • Relying on Batteries for IoT: Assuming batteries can power the estimated 30 billion sensors needed for the Internet of Things; the maintenance makes this impossible.
  • Using EMGs for Low-Frequency Energy: Relying on electromagnetic generators to harvest slow, random motions; their output scales quadratically with frequency, making them highly inefficient here.
  • Blindly Applying Classical Maxwell's Equations: Using classical electrodynamics on systems with moving media without adding a non-linear displacement current term.
  • Dismissing Triboelectricity as a Nuisance: Treating static electricity solely as a destructive force to be mitigated, which delayed paradigm-shifting energy harvesting technologies for centuries.
  • Giving Up Due to Early Skepticism: Abandoning an emergent idea because early definitions are vague or the scientific community demands data you haven't had time to publish yet.

How to use this skill in conversation

When the user is designing hardware, sensor networks, or novel energy systems, channel Wang's focus on fundamental physics and self-powered autonomy. If they suggest battery-powered IoT, gently challenge it using the Self-Powered IoT Necessity principle. If they are trying to harvest ambient energy, introduce the concept of High Entropy Energy and suggest matching the technology to the frequency (TENGs for low frequency, EMGs for high).

Always ground your advice in the physics of the problem. Surface relevant frameworks by name (e.g., "Zhong Lin Wang's Three-Stage Technology Roadmap suggests we first target...") and apply them directly to the user's context. Do not speak in the first person ("I invented TENGs..."); instead, act as an expert consultant applying Wang's specific mental models to their engineering challenges.

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 71 other files (references) in mimeographs/zhong-lin-wang of K-Dense-AI/mimeographs.

  • SKILL.md
  • AGENTS.md
  • _workspace/agents_output.e584bd6c.json
  • _workspace/clustered_corpus.e584bd6c.json
  • _workspace/discovery/books.json
  • _workspace/discovery/essays.json
  • _workspace/discovery/frameworks.json
  • _workspace/discovery/interviews.json
  • _workspace/discovery/letters.json
  • _workspace/discovery/papers.json
  • _workspace/discovery/podcasts.json
  • _workspace/discovery/ranked_sources.e584bd6c.json
  • _workspace/discovery/talks.json
  • _workspace/distilled/src_000.e584bd6c.json
  • _workspace/distilled/src_001.e584bd6c.json
  • _workspace/distilled/src_003.e584bd6c.json
  • _workspace/distilled/src_004.e584bd6c.json
  • _workspace/distilled/src_005.e584bd6c.json
  • … and 54 more

Open the folder on GitHubat commit a38f5fc

Compare with similar skills

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Categories

Questions about Zhong Lin Wang

What does Zhong Lin Wang do?

Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications. Zhong Lin Wang is an agent skill from K-Dense-AI/mimeographs. Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications.

When should I use Zhong Lin Wang?

Zhong Lin Wang fits situations like: tasks that involve Creative writing and fiction; tasks that involve Product roadmapping; tasks that involve Translation.

How do I install Zhong Lin Wang in Claude Code?

Run `npx skills add K-Dense-AI/mimeographs --skill zhong-lin-wang -a claude-code`. Or copy the skill folder (mimeographs/zhong-lin-wang in K-Dense-AI/mimeographs) into .claude/skills/zhong-lin-wang in your project. Claude Code loads it when a task matches its description.

How do I install Zhong Lin Wang in Codex?

Run `npx skills add K-Dense-AI/mimeographs --skill zhong-lin-wang -a codex`. Or copy the skill folder (mimeographs/zhong-lin-wang in K-Dense-AI/mimeographs) into .agents/skills/zhong-lin-wang in your project. Codex loads it when a task matches its description.

Can I use Zhong Lin Wang in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add K-Dense-AI/mimeographs --skill zhong-lin-wang -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/zhong-lin-wang, .gemini/skills/zhong-lin-wang, .github/skills/zhong-lin-wang and .opencode/skills/zhong-lin-wang in your project.

What does Zhong Lin Wang need to run?

SKILL.md names no scripts, command-line tools or credentials: Zhong Lin Wang is instructions for the agent only.

Does Zhong Lin Wang access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Zhong Lin Wang safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Zhong Lin Wang use?

Zhong Lin Wang is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Zhong Lin Wang use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.2k tokens, read only when the agent opens those files.

What are the alternatives to Zhong Lin Wang?

Skills that share tags, products or a category with Zhong Lin Wang: InkOS Creative Harness (Narcooo/inkos, 10k stars), Worldbuilding (danjdewhurst/story-skills, 286 stars), Bio Ribo Seq Orf Detection (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Bio Ribo Seq Orf Detection (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Zhong Lin Wang?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/mimeographs, which has 129 GitHub stars. The repository holds 60 skills in this directory. The repository was last updated on August 18, 2026.

Source: K-Dense-AI/mimeographs on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.