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 skills/microsoft/researchstudio/scoop_checknpx skills add microsoft/ResearchStudio --skill scoop_checkgit clone --depth 1 https://github.com/microsoft/ResearchStudioWhat 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 | $0.00106 | $0.04619 |
| Opus 5 | $0.00053 | $0.02309 |
| Sonnet 5 | $0.00021 | $0.00924 |
| Haiku 4.5 | $0.00011 | $0.00462 |
Grade A, and why
scoop-check 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scoop Check
Systematically verify whether a proposed research novelty overlaps with existing literature. The goal is to either (a) surface prior work that already covers the claim, or (b) produce a crisp, defensible "delta" statement that distinguishes the proposed contribution.
Inputs
This skill requires exactly two inputs from the user:
- Research problem — the specific issue, challenge, or gap in knowledge being addressed.
- Novelty — the specific claimed contribution (new method, theorem, dataset, framing, or insight) that distinguishes this work from prior art.
If either input is missing, vague, or conflated (e.g., the "novelty" merely restates the problem), infer the most reasonable interpretation and proceed immediately. Do not use AskUserQuestion or pause for confirmation at any point — always proceed directly through every step.
Procedure
Execute the steps below in order. At the start of the procedure, use TaskCreate to register all seven steps as tasks up front, then mark each as in_progress when you begin it and completed when you finish. The procedure is long enough that progress tracking is always worth the small overhead — without it, it's easy to skip a step (especially the per-step logging in the Important Notes) or lose track of which candidates still need a deep dive.
Step 1 — Decompose the Novelty
Break the claimed Novelty into four atomic axes, presented as a labeled list:
- Problem framing — Task definition, inputs, outputs, and evaluation regime.
- Core mechanism — The technical contribution — architecture, algorithm, proof technique, or data construction.
- Key insight — What makes it work; what prior state-of-the-art lacked.
- Application domain — Where it applies and how broadly.
Step 2 — Search and Deduplicate
Read the Research problem and craft three complementary search queries:
- Query 1 — Original-Problem: restate the original research problem.
Example:
score-based generative model fast inference - Query 2 — Broad-Domain: the high-level area, ~3–5 words.
Example:
diffusion model sampling efficiency - Query 3 — Method-Signature: the specific technical move, ~5–8 words.
Example:
consistency model knowledge distillation
What ships with it
1 file 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.
- 3d ago First seen · 200 lines · 106 tokens per session scan A a6bc30399f18
scoop-check is a skill published in the GitHub repository microsoft/ResearchStudio (2,614 stars, last pushed 4d ago), licensed MIT. It adds 106 tokens to every session and 4,619 once invoked, about $0.0005 per session on Opus 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.
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