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 creative-thinking-for-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/skills/openraiser/nanoresearch/creative-thinking-for-research)<a href="https://agentmods.dev/skills/openraiser/nanoresearch/creative-thinking-for-research"><img src="https://agentmods.dev/badge/skills/openraiser/nanoresearch/creative-thinking-for-research/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/creative-thinking-for-research"><img src="https://agentmods.dev/badge/skills/openraiser/nanoresearch/creative-thinking-for-research.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 | $0.00050 | $0.04675 |
| Opus 5 | $0.00025 | $0.02337 |
| Sonnet 5 | $0.00010 | $0.00935 |
| Haiku 4.5 | $0.00005 | $0.00468 |
Grade A, and why
creative-thinking-for-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 9d 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 creative-thinking-for-research — 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 — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creative Thinking for Research
Eight empirically grounded frameworks from cognitive science, applied to computer science and AI research. Unlike ad-hoc brainstorming, each framework here is backed by decades of creativity research — from Koestler's bisociation to Kauffman's adjacent possible. They target distinct cognitive operations: combining, reformulating, analogizing, constraining, inverting, abstracting, exploring boundaries, and holding contradictions.
When to Use This Skill
- Generating genuinely novel ideas, not incremental extensions of prior work
- Feeling trapped in a local optimum of thinking within a single subfield
- Wanting to systematically apply creativity heuristics rather than waiting for inspiration
- Preparing for a research retreat or PhD-level ideation session
- Bridging between fields and seeking structural (not superficial) connections
Do NOT use this skill when:
- You need structured project-level brainstorming workflows (use
brainstorming-research-ideas) - You have a well-defined problem and need execution help (use domain-specific skills)
- You need a literature survey (use
scientific-skills:literature-review)
Relationship to Brainstorm skill: The brainstorm skill provides operational workflows (diverge → converge → refine) and practical filters. This skill provides the deeper cognitive engines that power creative leaps. Use them together: creative-thinking to generate raw insight, brainstorm to structure and evaluate it.
Framework 1: Combinatorial Creativity (Bisociation)
Novel ideas arise from combining existing concepts in unexpected ways. Arthur Koestler called this bisociation — connecting two previously unrelated frames of reference, as distinct from routine association within a single frame.
Why it works: Meta-research consistently shows that breadth of knowledge is a precursor to creative output. People who read across disciplines produce more novel work. The combination itself is the creative act.
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.
- 9d ago First seen · 367 lines · 50 tokens per session scan A ec111f9236d7
creative-thinking-for-research is a skill published in the GitHub repository OpenRaiser/NanoResearch (1,365 stars, last pushed 14d ago), licensed MIT. It adds 50 tokens to every session and 4,675 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to creative-thinking-for-research, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
creative-thinking-for-research
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
creative-thinking-for-research
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
research-creative-thinking-for-research
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical re...
creative-thinking-for-research
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
creative-thinking-for-research
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
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…