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BC scoop-check

Check whether a proposed research novelty (given a problem statement and claimed idea/novelty) overlaps with existing published work. TRIGGER when the user asks to verify research novelty, check if an idea is new, compare a proposed contribution to prior art, do a literature/prior-art search for a specific claim, or assess whether a paper idea has been scooped. DO NOT TRIGGER for general literature reviews unrelated to a specific novelty claim, or for writing related-work sections of an already-validated idea.

microsoft/ResearchStudio Agent Skills author: microsoft 2 files · 1 script body ≈ 5 009 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Check whether a proposed research novelty (given a problem statement and claimed idea/novelty) overlaps with existing published work.

As a process C 58/100 · Has gaps — weak spots: result and completion, consistency

ProcedurePDFData and analyticsResearchtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Consistency w 8
40
Failures and branches w 10
50
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5009 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 40Consistency. Frontmatter name (scoop-check) differs from the folder (scoop_check)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5009 tokens
  • 85Steps. 66 steps, 2 vague phrases
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 515: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 66 items
  • +4Has examples (1 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.