ResearchStudio is a collection of skills that helps researchers develop an initial research direction into a paper and its related publication materials. It is used with Claude Code and Codex to create outputs such as papers, posters, videos, blogs, and reels. The catalogue entries are its own workflow skills for different stages of the research lifecycle.
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 microsoft/ResearchStudio --skill idea_qualitygit clone --depth 1 https://github.com/microsoft/ResearchStudioWrote 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/microsoft/researchstudio/idea_quality)<a href="https://agentmods.dev/skills/microsoft/researchstudio/idea_quality"><img src="https://agentmods.dev/badge/skills/microsoft/researchstudio/idea_quality/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/microsoft/researchstudio/idea_quality"><img src="https://agentmods.dev/badge/skills/microsoft/researchstudio/idea_quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00215 | $0.02378 |
| Opus 5.5 | $0.00086 | $0.00951 |
| Sonnet 5.5 | $0.00043 | $0.00476 |
| Haiku 4.5 | $0.00021 | $0.00238 |
Grade A, and why
idea-quality 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 1mo 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Idea Quality
Judge how strong a research idea is at the idea stage — before any experiments exist — and return a quality score with reasons a reviewer would recognize. This skill is self-contained: it judges from first principles on the three axes below. It consults no external corpus, dataset, or other skill, so its judgment is reproducible from the idea text alone.
Input: an idea Markdown file
The idea is a .md file with three sections. Read the file, then map each section to what it feeds:
# Title
<≤ ~15 words: the idea's handle>
## Motivation
<the bottleneck / gap the idea attacks, why it matters, and why it is still open>
## Method
<the proposed contribution as concrete numbered steps>
- Title → the handle.
- Motivation → feeds Axis A (is the problem worth attacking) and gives Axis C the "problem" half.
- Method → feeds Axis B (is the method good) and gives Axis C the "method" half.
If a section is missing or thin, infer the most reasonable reading from the rest and note the assumption in the report — do not stall asking for clarification. If the user passes two files (or one file with two ideas) and wants a comparison, run the pairwise track.
Scope: what this judges, and what it deliberately does not
- No experiments exist. Every axis is a reasoning-level judgment — the kind a reviewer makes from an abstract before seeing results. Never invent or assume experimental results to score with. If a claim's truth would need an experiment, judge the plausibility of its argument, not an imagined outcome.
- Not a prior-art check. "Has someone already done this?" is a separate literature-collision task. Judge whether the idea is good (real gap, deep method, sound + on-target), NOT whether a near-duplicate exists. Judge the contribution's intrinsic ambition, not its novelty against a literature search.
A strong score means the idea is strong; it does not predict acceptance, which also turns on execution this skill cannot see.
What ships with it
4 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.
- 1mo ago First seen · 128 lines · 215 tokens per session scan A 2defd8d45c65
idea-quality is a skill published in the GitHub repository microsoft/ResearchStudio (3,264 stars, last pushed 25d ago), licensed MIT. It adds 215 tokens to every session and 2,378 once invoked, about $0.0009 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 2026-08-30.
Other skills, from other repositories
sciatlas-idea-evaluate
Use only the current SciAtlas automated review workflow (reviewpipeline) to take a novice user from zero setup to a final automated review of a research idea or paper, including setup, registration guidance, workflow configuration, retrieval, artifact reading, and synthesis. Trigger when the user asks whether an idea…
sciatlas-literature-review
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sciatlas-idea-grounding
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sciatlas-trend-report
Use only SciAtlas search-papers to take a novice user from zero setup to a final timeline-oriented research trend report, including setup, registration guidance, configuration, retrieval, artifact reading, and synthesis. Trigger when the user asks for topic history, field evolution, recent trends, representative…
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Performs bounded, local exploratory analysis of explicitly supported scientific files. Supports redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…