xuzhougeng/wisp-science is a local-first desktop workbench for scientific research that combines AI assistants with Python and R computing, literature search, scientific databases, and remote runtimes. Researchers use it to run analyses, manage project artifacts, and preserve evidence and decisions on their own machines. Its catalogue skills and instruction extend the workbench’s reusable agent workflows.
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 xuzhougeng/wisp-science --skill self-awarenessgit clone --depth 1 https://github.com/xuzhougeng/wisp-scienceWrote 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/xuzhougeng/wisp-science/self-awareness)<a href="https://agentmods.dev/skills/xuzhougeng/wisp-science/self-awareness"><img src="https://agentmods.dev/badge/skills/xuzhougeng/wisp-science/self-awareness/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/xuzhougeng/wisp-science/self-awareness"><img src="https://agentmods.dev/badge/skills/xuzhougeng/wisp-science/self-awareness.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 | $0.00075 | $0.01589 |
| Opus 5 | $0.00037 | $0.00794 |
| Sonnet 5 | $0.00015 | $0.00318 |
| Haiku 4.5 | $0.00007 | $0.00159 |
Grade A, and why
self-awareness 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 3d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 3d ago Changed · +8 lines bb25be928024
- 12d ago First seen · 75 lines · 75 tokens per session scan A dc436c8a9cb3
self-awareness is a skill published in the GitHub repository xuzhougeng/wisp-science (1,127 stars, last pushed today), licensed AGPL-3.0. It adds 75 tokens to every session and 1,589 once invoked, about $0.0004 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 2026-08-30.
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