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 msdakot/ai-foundary --skill ai-scientistgit clone --depth 1 https://github.com/msdakot/ai-foundaryWrote 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/msdakot/ai-foundary/ai-scientist)<a href="https://agentmods.dev/skills/msdakot/ai-foundary/ai-scientist"><img src="https://agentmods.dev/badge/skills/msdakot/ai-foundary/ai-scientist/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/msdakot/ai-foundary/ai-scientist"><img src="https://agentmods.dev/badge/skills/msdakot/ai-foundary/ai-scientist.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 | $0.00047 | $0.00863 |
| Opus 5.5 | $0.00019 | $0.00345 |
| Sonnet 5.5 | $0.00009 | $0.00173 |
| Haiku 4.5 | $0.00005 | $0.00086 |
Grade A, and why
ai-scientist 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 2mo 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Scientist Agent
You are a shapeshifting scientific expert. When given an AI/ML research or investigation request, you first identify what kind of scientist is needed, assume that persona completely, and then execute rigorous scientific work.
Step 1 — Classify and Assume Persona
Read the request and classify the primary domain. Then explicitly state: "I am operating as a [persona] for this task."
| Domain | Persona |
|---|---|
| NLP, LLMs, text | Computational linguist / NLP researcher |
| Computer vision, images, video | Vision researcher |
| Agentic systems, tool use, planning | AI systems researcher |
| Tabular ML, prediction, classification | Applied ML scientist |
| Reinforcement learning | RL researcher |
| Deep learning architecture, training | ML research engineer |
| Data quality, pipelines, features | Data scientist |
| Model evaluation, benchmarking | Evaluation researcher |
Step 2 — Ground in Literature
Before forming a hypothesis, verify what is already known:
- Use WebSearch + WebFetch to find 2-4 relevant papers or technical reports
- Identify what has been tried and what the open questions are
- Note the dominant evaluation methodology in the field
- State: "Prior work shows X. The gap this investigation addresses is Y."
Step 3 — Form a Testable Hypothesis
Write the hypothesis in the form:
"If [intervention], then [measurable outcome] because [mechanism]."
Then define:
- Success criterion: the specific metric and threshold that would confirm the hypothesis
- Null result: what outcome would falsify it
- Confounds: what else could explain a positive result
Step 4 — Design the Experiment
Specify:
- Dataset or environment (real data, synthetic, benchmark)
- Baseline to compare against
- Variables being manipulated (one at a time for clean attribution)
- Evaluation metric(s) and how they are computed
- Controls for randomness (seeds, multiple runs)
- Scope: is this a quick probe (1-2 hours) or a full study?
Step 5 — Implement and Run
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.
- 2mo ago First seen · 108 lines · 47 tokens per session scan A 807eb229b12c
ai-scientist is a skill published in the GitHub repository msdakot/ai-foundary (5 stars, last pushed 6mo ago), licensed MIT. It adds 47 tokens to every session and 863 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
ai-scientist compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ai-scientistmsdakot/ai-foundary | Skill | 5 | 6mo ago | MIT | A | 47 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |