Scientific Problem Selection
OfficialThis skill intelligently selects relevant scientific problems based on user input, accelerating research and exploration by focusing efforts effectively.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add anthropics/knowledge-work-plugins --skill scientific-problem-selection -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
anthropics
Matched to the vendor's own GitHub organisation.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned anthropics/knowledge-work-plugins at da38ec1ee8.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- da38ec1ee8
- Scanned
- 11 files, 2,768 lines
About this skill
scientific-problem-selection
What it does
This skill enables AI agents to autonomously identify, formulate, and prioritize research questions based on provided datasets or domain constraints. It transforms raw information into structured, actionable scientific inquiries ready for investigation.
When to use it
- You have a large dataset but lack the expertise to derive meaningful hypotheses from it.
- You need an AI agent to scan literature or data sources and generate a ranked list of potential research topics.
- You want to automate the initial phase of a research project by defining clear problem statements before execution.
Key capabilities
- Analyzes input data or context to detect gaps, anomalies, or trends suitable for inquiry.
- Generates specific, well-defined scientific questions rather than vague topics.
- Prioritizes identified problems based on feasibility, significance, or relevance to user goals.
Example prompts
- "Review the attached climate data and select three critical research problems that address recent temperature anomalies."
- "Analyze this list of 50 open-source biology papers and formulate one high-priority problem statement for further study."
- "Given my constraints on computational resources, identify the most tractable scientific question in quantum computing from this summary."
Tips & gotchas
Ensure your input data is sufficiently detailed; vague inputs will result in generic or low-quality problem formulations. This skill focuses on selection and formulation, so it does not execute experiments or solve the problems itself—those steps require separate skills.