gget
by K-Dense-AIgget is a bioinformatics skill for fast, unified access to 20+ genomic databases and analysis tools from CLI or Python. Use it for gene info, BLAST-related lookups, AlphaFold structures, expression data, disease associations, and enrichment-style analysis. It suits quick exploration and gget for Data Analysis workflows.
This skill scores 85/100, which means it is a solid listing candidate for directory users. The repository gives enough real workflow content to justify installation: it clearly targets fast bioinformatics lookups, shows both CLI and Python usage, and outlines what outputs and flags to expect, so agents can trigger it with less guesswork than a generic prompt.
- Clear install/use case for 20+ bioinformatics databases, including gene info, BLAST, AlphaFold structures, enrichment analysis, and disease associations.
- Strong operational clarity: the skill documents a shared CLI/Python pattern, common flags, and output formats (JSON/CSV, DataFrame/dict).
- Good trust signals for adoption: valid frontmatter, no placeholder markers, substantial body length, and explicit note that modules are tested biweekly against database changes.
- The excerpt does not show the full set of module-specific workflows, so some tasks may still require reading deeper in the skill.
- The install commands in the excerpt look malformed/redundant ('uv uv pip' and 'uv pip' for pip), which may slow first-time setup if not corrected.
Overview of gget skill
What gget does
gget is a bioinformatics skill for fast, unified access to 20+ genomic databases and analysis tools from the command line or Python. It is built for people who need gene lookups, BLAST-related queries, AlphaFold structure checks, expression data, disease associations, and enrichment-style analysis without stitching together many separate APIs.
Who should use it
The gget skill is best for researchers, bioinformatics analysts, and AI agents doing exploratory data analysis or lightweight pipeline steps. It fits when you want a quick answer, a consistent interface, and a tool that works in both CLI and Python workflows.
Why it stands out
The main value of gget is speed of access, not deep pipeline orchestration. It is useful when a task spans multiple public biology resources and you want one tool to query them in a repeatable way. If you need heavy batch processing or advanced BLAST control, the repo itself points you toward specialized alternatives like Biopython; for broader multi-database Python workflows, bioservices may fit better.
How to Use gget skill
Install the gget skill
Install it in your skills environment with:
npx skills add K-Dense-AI/claude-scientific-skills --skill gget
For local Python use, create a clean environment first so database and package dependencies do not conflict with other scientific tools.
Read the right files first
Start with SKILL.md, then check README.md if present in your copied workspace, along with any AGENTS.md, metadata.json, rules/, references/, resources/, or scripts/ folders. For this repository, the core guidance is concentrated in scientific-skills/gget/SKILL.md, so there is little value in hunting for extra helper files that do not exist.
Turn a rough goal into a usable prompt
A good gget prompt names the biological entity, the database target, and the output format you want. For example: “Use gget to find gene summary, aliases, and related expression information for TP53, then return a compact table I can paste into a report.” That is stronger than “look up TP53” because it tells the skill what to retrieve and how to shape the result.
Practical workflow tips
Use gget for targeted queries and exploratory steps, then save the output when you need a traceable result. Prefer one clear module request at a time, especially when you care about reproducibility or need to compare outputs across databases. If you are doing gget for Data Analysis, ask for tabular output early so the result can be inspected, filtered, or merged downstream.
gget skill FAQ
Is gget mainly for CLI or Python?
Both. The gget skill is designed to work as command-line tooling and as Python functions, so the choice depends on whether you are exploring interactively or embedding lookups in a notebook or script.
When is gget a good fit?
Use the gget skill when you need quick access to curated biological data sources, especially for gene-centric research, structure checks, or lightweight enrichment questions. It is a good fit when the problem is “fetch and inspect” rather than “build and manage a full analysis pipeline.”
When should I not use gget?
Skip gget if you need large-scale batch processing, advanced BLAST parameter control, or a more general multi-API integration layer. In those cases, the repository’s own guidance suggests more specialized tools.
Is gget beginner-friendly?
Yes, if the task is simple and well-scoped. Beginners usually do best when they start with a single gene, a single database question, and a clear output format instead of trying to query everything at once.
How to Improve gget skill
Give gget better biological context
The strongest gget results come from specific inputs: gene symbol plus organism, protein ID plus structure question, or pathway term plus desired evidence type. “Find information on BRCA1” is weaker than “Summarize BRCA1 human gene aliases, disease links, and expression-related records for a literature note.”
Ask for the output shape you actually need
If you want gget for Data Analysis, say whether you need JSON, CSV, or a table-ready summary. That reduces cleanup work and helps the skill choose a response that can be compared across samples or merged into your notebook.
Watch for database and version drift
The repo notes that upstream databases change and gget is updated on a biweekly basis to follow those changes. If a query fails or returns a different structure than expected, retry with a narrower query and inspect whether the upstream source format changed rather than assuming the skill is broken.
Iterate from the first result
Use the first answer to narrow the next prompt: ask for related genes, a different database view, or a stricter filter only after you see the initial output. For gget skill usage, this stepwise approach usually gives cleaner results than one oversized request with too many biological questions bundled together.