NanoResearch is an autonomous AI research system that turns research ideas into executable experiments and LaTeX papers supported by results from real training runs. It is for researchers validating prototypes, running GPU experiments, generating benchmarks, analyzing logs, and preparing paper drafts. The catalogue add-ons support its research pipeline and 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 OpenRaiser/NanoResearch --skill 0-autoresearch-skillgit clone --depth 1 https://github.com/OpenRaiser/NanoResearchWrote 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/openraiser/nanoresearch/0-autoresearch-skill)<a href="https://agentmods.dev/skills/openraiser/nanoresearch/0-autoresearch-skill"><img src="https://agentmods.dev/badge/skills/openraiser/nanoresearch/0-autoresearch-skill/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/openraiser/nanoresearch/0-autoresearch-skill"><img src="https://agentmods.dev/badge/skills/openraiser/nanoresearch/0-autoresearch-skill.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 | $0.00098 | $0.05297 |
| Opus 5 | $0.00049 | $0.02648 |
| Sonnet 5 | $0.00020 | $0.01059 |
| Haiku 4.5 | $0.00010 | $0.00530 |
Grade A, and why
autoresearch 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 11d 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.
This is a copy
100% identical to autoresearch — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch
Autonomous research orchestration for AI coding agents. You manage the full research lifecycle — from literature survey to published paper — by maintaining structured state, running a two-loop experiment-synthesis cycle, and routing to domain-specific skills for execution.
You are a research project manager, not a domain expert. You orchestrate; the domain skills execute.
This runs fully autonomously. Do not ask the user for permission or confirmation — use your best judgment and keep moving. Show the human your progress frequently through research presentations (HTML/PDF) so they can see what you're doing and redirect if needed. The human is asleep or busy; your job is to make as much research progress as possible on your own.
Getting Started
Users arrive in different states. Determine which and proceed:
| User State | What to Do |
|---|---|
| Vague idea ("I want to explore X") | Brief discussion to clarify, then bootstrap |
| Clear research question | Bootstrap directly |
| Existing plan or proposal | Review plan, set up workspace, enter loops |
| Resuming (research-state.yaml exists) | Read state, continue from where you left off |
If things are clear, don't over-discuss — proceed to full autoresearch. Most users want you to just start researching.
Step 0 — before anything else: Set up the agent continuity loop. See Agent Continuity. This is MANDATORY. Without it, the research stops after one cycle.
Initialize Workspace
Create this structure at the project root:
{project}/
├── research-state.yaml # Central state tracking
├── research-log.md # Decision timeline
├── findings.md # Evolving narrative synthesis
├── literature/ # Papers, survey notes
├── src/ # Reusable code (utils, plotting, shared modules)
├── data/ # Raw result data (CSVs, JSONs, checkpoints)
├── experiments/ # Per-hypothesis work
│ └── {hypothesis-slug}/
│ ├── protocol.md # What, why, and prediction
│ ├── code/ # Experiment-specific code
│ ├── results/ # Raw outputs, metrics, logs
│ └── analysis.md # What we learned
├── to_human/ # Progress presentations and reports for human review
└── paper/ # Final paper (via ml-paper-writing)
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 412 lines · 98 tokens per session scan A b7ca0d7a6e90
autoresearch is a skill published in the GitHub repository OpenRaiser/NanoResearch (1,365 stars, last pushed 16d ago), licensed MIT. It adds 98 tokens to every session and 5,297 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to autoresearch, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
autoresearch
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports…
autoresearch
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports…
research-0-autoresearch-skill
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes re...
autoresearch
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports…
auto-claim
Workflow 1: Claim-stage pipeline, controlled by two orthogonal axes. BEHAVIORSOURCE selects the behavior stage: given (default; behavior taken from task.md and assumed to hold — no ideation, no novelty, no M0), given-validation (behavior taken from task.md but the experiment plan opens with an M0 phenomenon-validation…
auto-experiment
Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENTPLAN.md, routes mechanism family inline (Phase 1.5), implements experiment code, deploys to GPU, and collects initial results. Use when user says "implement experiments", "experiment", "deploy the plan", or has an experiment plan ready to…