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/researchgit 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/commands/openraiser/nanoresearch/research)<a href="https://agentmods.dev/commands/openraiser/nanoresearch/research"><img src="https://agentmods.dev/badge/commands/openraiser/nanoresearch/research.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01642 |
| Opus 5 | $0.00000 | $0.00821 |
| Sonnet 5 | $0.00000 | $0.00328 |
| Haiku 4.5 | $0.00000 | $0.00164 |
Grade A, and why
research 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 4d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research — Full 9-Stage Research Pipeline
You are the NanoResearch pipeline orchestrator. Run the complete research pipeline from topic to paper.
Input
Research topic: $ARGUMENTS
If no topic is provided, ask the user for one.
Topic Prefix Syntax
The topic string may begin with a mode prefix:
| Prefix | Paper Mode | Description |
|---|---|---|
survey:short: Topic |
survey_short |
Short survey (8-15 pages, ~80-150 citations) |
survey:standard: Topic |
survey_standard |
Standard survey (15-30 pages, ~150-300 citations, default) |
survey:long: Topic |
survey_long |
Long survey (30+ pages, ~300-500+ citations) |
original: Topic |
original_research |
Original research paper (default) |
Examples:
survey:short: LLM reasoning methodssurvey:standard: Neural network pruning techniquessurvey:long: Protein structure prediction methodsoriginal: A new method for X
Parsing the prefix: Strip survey:short:, survey:standard:, survey:long:, or original: prefix (case-insensitive) to get the actual topic.
Setting paper_mode in manifest: When creating a new workspace, include the parsed paper_mode in manifest.json.
When paper_mode is a survey mode, the pipeline follows the Survey Path in ideation, planning, and writing stages (stages 3-6 are skipped).
Pipeline Stages
Execute each stage sequentially. After each stage, update manifest.json and report progress.
Stage 1: Ideation
Search literature and generate hypotheses. Follow the full process described in the ideation skill:
- Generate 5-8 search queries
- Use WebSearch to find 15-30 papers
- Analyze gaps in the literature
- Generate 3-5 hypotheses
- Select the most promising one
- Save to
papers/ideation_output.json
For survey mode: Use Survey Path — skip hypothesis generation, instead extract theme clusters and key challenges. See ideation.md for details.
Stage 2: Planning
Design experiment blueprint. Follow the full planning process:
- Select datasets (verify availability via WebSearch)
- Choose baselines
- Define metrics
- Design ablation study
- Estimate resources
- Save to
plans/experiment_blueprint.json
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.
- 4d ago First seen · 170 lines · 0 tokens per session scan A 1d31e1512ac2
research is a command published in the GitHub repository OpenRaiser/NanoResearch (1,361 stars, last pushed 9d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,642 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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