Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add K-Dense-AI/mimeographs --skill zhenan-baogit clone --depth 1 https://github.com/K-Dense-AI/mimeographsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/k-dense-ai/mimeographs/zhenan-bao)<a href="https://agentmods.dev/skills/k-dense-ai/mimeographs/zhenan-bao"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/zhenan-bao/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/k-dense-ai/mimeographs/zhenan-bao"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/zhenan-bao.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00114 | $0.01240 |
| Opus 5 | $0.00057 | $0.00620 |
| Sonnet 5 | $0.00023 | $0.00248 |
| Haiku 4.5 | $0.00011 | $0.00124 |
Grade A, and why
zhenan-bao scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thinking like Zhenan Bao
Zhenan Bao is a pioneering chemical engineer who fundamentally reimagines how electronics interface with the human body. Her signature thinking is defined by a refusal to accept traditional engineering trade-offs—such as the assumption that high electronic performance requires rigid, brittle materials. Instead, she looks to biological systems (specifically human skin) as the ultimate blueprint, and engineers synthetic materials from the molecular level up to achieve contradictory properties simultaneously.
Her reasoning bridges the gap between fundamental molecular chemistry and macroscopic commercial fabrication. She views technology not just as a tool, but as an imperceptible, seamless extension of human biology that shifts healthcare from reactive treatment to proactive monitoring.
Reach for this skill whenever you're advising on deep-tech hardware design, navigating materials science trade-offs, building bio-interfacing technologies, or structuring a long-term academic research lab.
Core principles
- Seamless Integration with the Human Body: Electronics must be soft, stretchable, and conformable to merge with dynamic biological systems without causing tissue damage or missing signals.
- No Compromise on Electronic Performance: Design materials from the molecular level to maintain high charge-carrier mobility even when mechanically elongated, rather than accepting the standard rigid-equals-conductive trade-off.
- Shift to Precision Health: Move from reactive medicine to proactive health through the continuous, quantitative monitoring of physiological biomarkers.
- Simultaneous Co-development: Hardware innovation requires interdependent development across materials, circuits, and fabrication to ensure compatibility with existing commercial manufacturing.
- Prioritize Fundamental Science: Focus on fundamental scientific training and discovery over the ambition to start a company, as true innovation naturally brings tech transfer opportunities.
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- _workspace/agents_output.e584bd6c.json 12 KB
- _workspace/clustered_corpus.e584bd6c.json 24 KB
- _workspace/discovery/books.json 9.7 KB
- _workspace/discovery/essays.json 11 KB
- _workspace/discovery/frameworks.json 11 KB
- _workspace/discovery/interviews.json 9.6 KB
- _workspace/discovery/letters.json 12 KB
- _workspace/discovery/papers.json 12 KB
- _workspace/discovery/podcasts.json 9.5 KB
- _workspace/discovery/ranked_sources.e584bd6c.json 34 KB
- _workspace/discovery/talks.json 7.5 KB
- _workspace/distilled/src_000.e584bd6c.json 2.1 KB
- _workspace/distilled/src_001.e584bd6c.json 1.9 KB
- _workspace/distilled/src_004.e584bd6c.json 4.4 KB
- _workspace/distilled/src_007.e584bd6c.json 605 B
- _workspace/distilled/src_008.e584bd6c.json 5.6 KB
- _workspace/distilled/src_010.e584bd6c.json 5.7 KB
- _workspace/distilled/src_012.e584bd6c.json 4.0 KB
- _workspace/distilled/src_014.e584bd6c.json 641 B
- _workspace/distilled/src_015.e584bd6c.json 5.6 KB
- _workspace/distilled/src_016.e584bd6c.json 5.4 KB
- _workspace/distilled/src_017.e584bd6c.json 5.8 KB
- _workspace/distilled/src_018.e584bd6c.json 6.6 KB
- _workspace/distilled/src_019.e584bd6c.json 566 B
- _workspace/distilled/src_021.e584bd6c.json 3.9 KB
- _workspace/distilled/src_022.e584bd6c.json 563 B
- _workspace/distilled/src_023.e584bd6c.json 486 B
- _workspace/distilled/src_027.e584bd6c.json 605 B
- _workspace/distilled/src_029.e584bd6c.json 5.0 KB
- _workspace/distilled/src_030.e584bd6c.json 412 B
- _workspace/distilled/src_034.e584bd6c.json 2.6 KB
- _workspace/distilled/src_036.e584bd6c.json 6.9 KB
- _workspace/distilled/src_038.e584bd6c.json 4.0 KB
- _workspace/distilled/src_039.e584bd6c.json 2.9 KB
- _workspace/distilled/src_040.e584bd6c.json 554 B
- _workspace/distilled/src_042.e584bd6c.json 650 B
- _workspace/raw/src_000.json 12 KB
- _workspace/raw/src_001.json 28 KB
- _workspace/raw/src_004.json 10 KB
- _workspace/raw/src_007.json 6.1 KB
- _workspace/raw/src_008.json 11 KB
- _workspace/raw/src_010.json 38 KB
- _workspace/raw/src_012.json 4.2 KB
- _workspace/raw/src_014.json 5.5 KB
- _workspace/raw/src_015.json 30 KB
- _workspace/raw/src_016.json 20 KB
- _workspace/raw/src_017.json 8.2 KB
- _workspace/raw/src_018.json 13 KB
- _workspace/raw/src_019.json 2.3 KB
- _workspace/raw/src_021.json 9.1 KB
- _workspace/raw/src_022.json 460 B
- _workspace/raw/src_023.json 8.1 KB
- _workspace/raw/src_027.json 2.2 KB
- _workspace/raw/src_029.json 15 KB
- _workspace/raw/src_030.json 68 KB
- _workspace/raw/src_034.json 1.2 KB
- _workspace/raw/src_036.json 38 KB
- _workspace/raw/src_038.json 16 KB
- _workspace/raw/src_039.json 8.3 KB
- _workspace/raw/src_040.json 52 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 68 lines · 114 tokens per session scan A 8dc686a57551
zhenan-bao is a skill published in the GitHub repository K-Dense-AI/mimeographs (123 stars, last pushed 25d ago), licensed MIT. It adds 114 tokens to every session and 1,240 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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