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 agentmods add skills/ai4scientist/nano-scientist/embodiment-descriptionnpx skills add AI4Scientist/nano-scientist --skill embodiment-descriptiongit clone --depth 1 https://github.com/AI4Scientist/nano-scientistWhat 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 | $0.00048 | $0.01500 |
| Opus 5 | $0.00024 | $0.00750 |
| Sonnet 5 | $0.00010 | $0.00300 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
embodiment-description 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 yesterday.
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
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- yesterday First seen · 130 lines · 48 tokens per session scan A c1ca836a52ae
embodiment-description is a skill published in the GitHub repository AI4Scientist/nano-scientist (126 stars, last pushed 2mo ago), with no licence file. It adds 48 tokens to every session and 1,500 once invoked, about $0.0002 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.
Other skills, from other repositories
research-workflow
PRIMARY ORCHESTRATOR — trigger this skill FIRST for any non-trivial AI R&D task. Coordinates run-governor, memory-manager, deep-research, research-plan, project-context, experiment-execution, human-checkpoint, and paper-writing. TRIGGER FIRST when: any non-trivial research task begins (analysis, debugging…
memory-manager
Manage long-term AI R&D memory: retrieval, writeback, promotion, and shared export. TRIGGER when: run bootstrap, each new user turn, each execution batch, significant failure, replan, high-resource action, long-action resume, final report handoff, or compaction markers detected (Compact/压缩/Summary). DO NOT TRIGGER…
experiment-execution
Execute AI/ML experiments locally or remotely with environment, runtime, and logging controls. Prefer invoking via research-workflow. TRIGGER when: user asks to run/launch/start/resume/monitor a training job, evaluation, or benchmark, or a plan is ready for execution, or experiment needs rerun/recovery. DO NOT TRIGGER…
project-context
Initialize and maintain per-project runtime context (env, secrets, snapshots). Prefer invoking via research-workflow. TRIGGER when: new run needs env setup, preflight before experiment/eval, runtime fields missing (paths, API keys, GPU config, proxy), run snapshot needed, or shared-memory needs project config. DO NOT…
deep-research
Deep evidence-first research with broad discovery, verification, and traceable citations. Prefer invoking via research-workflow. TRIGGER when (MANDATORY — you MUST invoke this skill, no exceptions): user message contains ANY of these keywords or synonyms — 调研/研究/对比/综述/文献/证据/机制/根因/为什么/可行性/路线图/分析/探索, or…
run-governor
Govern run-level execution policy: mode selection, durable run tracking, long-action watch/resume policy, stage reporting, and safety allowances. TRIGGER when: starting a non-trivial research task (set mode + runid), switching local/remote target, creating a new run, or mode-aware policy decisions needed. DO NOT…