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 local-env-setupgit 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/local-env-setup)<a href="https://agentmods.dev/skills/xuzhougeng/wisp-science/local-env-setup"><img src="https://agentmods.dev/badge/skills/xuzhougeng/wisp-science/local-env-setup/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/local-env-setup"><img src="https://agentmods.dev/badge/skills/xuzhougeng/wisp-science/local-env-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00077 | $0.03347 |
| Opus 5 | $0.00039 | $0.01673 |
| Sonnet 5 | $0.00015 | $0.00669 |
| Haiku 4.5 | $0.00008 | $0.00335 |
Grade C, and why
local-env-setup scanned grade C with 2 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 today.
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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -LsSf https://astral.sh/uv/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| `curl -s --connect-timeout 3 https://pypi.org/simple/` fails or >5s; tuna mirror responds in <2s | yes | 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.
- today Changed 23cae1b84f0c
- yesterday Changed · +34 lines · -29 tokens per session b57c06d1e2d1
- 9d ago First seen · 290 lines · 106 tokens per session scan C 318759efaba7
local-env-setup is a skill published in the GitHub repository xuzhougeng/wisp-science (1,114 stars, last pushed today), licensed AGPL-3.0. It adds 77 tokens to every session and 3,347 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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