database-lookup
by K-Dense-AIdatabase-lookup helps route research questions to the right public database API and return raw JSON with source databases named. Use it for compounds, genes, proteins, variants, clinical trials, patents, environmental data, or economic indicators when you need a database-lookup guide instead of a generic web summary.
This skill scores 82/100, which means it is a solid listing candidate for Agent Skills Finder. The repository gives directory users enough evidence that it is a real, high-leverage database-query workflow rather than a placeholder: it targets 78 public scientific and economic databases, has a valid SKILL frontmatter entry, and includes substantial step-by-step operational content. Users should still expect to read the skill carefully before installing because there is no separate README, scripts, or reference assets to support onboarding or validation.
- Broad, explicit trigger coverage: the description names many concrete use cases and database families, making it easy for agents to know when to invoke it.
- Strong operational substance: the SKILL body is large with many headings and workflow sections, suggesting real execution guidance rather than a stub.
- Good install decision value: it clearly promises API-based retrieval of raw JSON from public databases, which is materially useful for data lookup tasks.
- No install command, scripts, or support files are present, so users have little help validating setup or integration details.
- The repository appears to be a single SKILL.md without external references or assets, so trust in database coverage and query behavior rests mostly on the document itself.
Overview of database-lookup skill
What database-lookup does
The database-lookup skill helps you route a research question to the right public database API, then return the raw JSON results with the source databases named. It is built for Web Research tasks where the hard part is not “asking AI” but choosing the correct scientific, biomedical, regulatory, or economic dataset fast.
Best-fit use cases
Use the database-lookup skill when you need evidence from sources like PubChem, UniProt, ClinicalTrials.gov, FRED, US Census, or NASA rather than a generic web summary. It is especially useful for compounds, genes, proteins, variants, pathways, trials, patents, environmental data, and macroeconomic indicators.
Why it is different
The main value of database-lookup is source selection, not explanation. Instead of one broad prompt, it gives you a database-lookup guide for matching query type to database, which reduces guesswork and helps avoid wasting calls on irrelevant APIs.
How to Use database-lookup skill
Install the skill
For a local Claude Skills setup, install the database-lookup skill from K-Dense-AI/claude-scientific-skills and verify that the skill folder is present before you prompt it. If your environment uses a different skills manager, adapt the install step to that system rather than copying the command blindly.
Start with the right input
A strong database-lookup usage prompt names the entity, the question, and the expected output shape. For example: “Find public API records for the gene TP53, prioritize human annotation and disease association, and return the raw JSON plus source databases.” That is better than “Look up TP53” because it tells the skill what kind of database fit matters.
Read these files first
Start with scientific-skills/database-lookup/SKILL.md to understand the workflow and database selection logic. If your environment exposes them, also check README.md, AGENTS.md, metadata.json, and any rules/, resources/, references/, or scripts/ folders; this repository is compact, so the skill file is the primary source of truth.
Practical workflow tips
Use the skill to narrow the database first, then refine the query terms after you see which source is most likely to answer the question. If the request spans categories, such as a gene plus a pathway plus a clinical association, ask for multiple candidate databases up front rather than forcing one source to do everything. For better results, include species, organism, timeframe, identifier type, and any must-have filters in your prompt.
database-lookup skill FAQ
Is database-lookup good for Web Research?
Yes. database-lookup for Web Research is a strong fit when you need structured data from authoritative APIs instead of narrative search results. It is less useful when the goal is broad literature scanning or open-ended opinion synthesis.
Do I need biology or data skills to use it?
No. Beginners can use the database-lookup skill if they can describe the target clearly. You do not need to know every database in the ecosystem, but you do need to know whether you are asking about a compound, a gene, a trial, a patent, or an economic series.
When should I not use it?
Do not use database-lookup when you only need a plain English summary, when the answer is likely in a single document, or when the target data is not exposed through a public REST API. It is also a poor fit if you need heavy interpretation rather than source retrieval.
How does it compare with a generic prompt?
A generic prompt may guess the database or skip source specificity. The database-lookup skill is better when accuracy depends on selecting the right public dataset, preserving raw results, and keeping the lookup auditable.
How to Improve database-lookup skill
Give the skill fewer unknowns
The fastest way to improve database-lookup results is to specify the entity type, organism or region, and preferred identifier. For example: “Search for human BRCA1 variants with ClinVar significance” is much stronger than “find BRCA1 info,” because it removes ambiguity about database choice and output expectations.
Ask for a multi-database strategy when needed
If your question crosses domains, say so explicitly. A request like “Compare FDA labels, ClinicalTrials.gov entries, and PubChem safety notes for semaglutide” helps the skill avoid overfitting to one source and improves the chance of useful cross-checks.
Inspect and iterate on the first pass
Treat the first response as database discovery, not final analysis. If the first lookup returns too much noise, tighten the filters, switch identifier formats, or ask for a different database family. Good database-lookup skill usage is iterative: source selection first, query refinement second, interpretation last.