Hypothesis Generation
CheckedGenerates testable hypotheses from brass log data to identify root causes of operational issues.
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 braselog/researchassistant --skill hypothesis-generation -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
braselog
Taken from the source listing. We haven't verified who controls this account.
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 braselog/researchassistant at 6e1e60a1d7.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 6e1e60a1d7
- Scanned
- 1 file, 19 lines
About this skill
What it does
The braselog-hypothesis-generation skill generates research hypotheses based on provided text. It can identify potential relationships and suggest testable statements for further investigation. This is useful for accelerating the initial phases of a research project or brainstorming new avenues of inquiry.
When to use it
- Literature Review: Quickly generate potential research questions from a large body of existing literature.
- Data Exploration: Formulate hypotheses about relationships within a dataset you're exploring.
- Brainstorming: Spark new ideas and directions for a project by having the skill suggest possible hypotheses.
- Initial Research Planning: Develop preliminary hypotheses to guide your research design and data collection efforts.
Key capabilities
- Hypothesis generation from text input
- Identification of potential relationships within provided information
- Suggestion of testable statements
Example prompts
- "Generate some research hypotheses based on this article about climate change."
- "What are some possible explanations for the correlation between X and Y in this dataset?"
- "Suggest a few hypotheses I could investigate regarding the impact of social media on teenagers."
Tips & gotchas
The quality of generated hypotheses depends heavily on the clarity and relevance of the input text. Providing well-defined context will yield more useful results.