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 commands/openraiser/nanoresearch/experimentgit clone --depth 1 https://github.com/OpenRaiser/NanoResearchWhat 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 | $0.00000 | $0.01023 |
| Opus 5 | $0.00000 | $0.00511 |
| Sonnet 5 | $0.00000 | $0.00205 |
| Haiku 4.5 | $0.00000 | $0.00102 |
Grade A, and why
experiment 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.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment — Setup + Code Generation + Execution
You are the Experiment Agent for NanoResearch. This command combines the Setup, Coding, and Execution stages. You will generate runnable experiment code and execute it.
Input
$ARGUMENTS — workspace path (optional). If not provided, use the most recent workspace under ~/.nanoresearch/workspace/research/.
Prerequisites
Read:
{workspace}/papers/ideation_output.json{workspace}/plans/experiment_blueprint.json
If the blueprint doesn't exist, tell the user to run /project:planning first.
Process
Phase 1: Setup (update manifest: setup → running)
-
Environment setup: Create a Python environment for the experiment:
cd {workspace}/experiment python -m venv .venv source .venv/bin/activate -
Dependency analysis: Based on the blueprint, determine required packages:
- Deep learning framework (torch/tensorflow/jax)
- Data processing (pandas, numpy, scikit-learn)
- Domain-specific libraries
- Evaluation libraries
-
Write
requirements.txtto{workspace}/experiment/requirements.txt -
Install dependencies:
pip install -r requirements.txt -
Dataset preparation: Download or prepare datasets specified in the blueprint.
- Use WebSearch to find download links if needed
- Write download/preprocessing scripts
Update manifest: setup → completed.
Write {workspace}/plans/setup_output.json with environment details.
Phase 2: Coding (update manifest: coding → running)
Generate the experiment code in {workspace}/experiment/:
-
config.py— Experiment configuration (hyperparameters, paths, dataset config) -
data.py— Data loading and preprocessing:- Dataset class(es) for each dataset in the blueprint
- Train/val/test split handling
- Data augmentation if applicable
-
model.py— Model implementations:- Proposed method
- Each baseline method
- Shared components (encoder, decoder, etc.)
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 First seen · 135 lines · 0 tokens per session scan A 480eb20ac0c0
experiment is a command published in the GitHub repository OpenRaiser/NanoResearch (1,361 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,023 tokens. 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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