depmap
by K-Dense-AIdepmap helps analyze the Cancer Dependency Map for cancer cell line gene dependency scores, drug sensitivity, and gene effect profiles. Use it to identify cancer-specific vulnerabilities, synthetic lethal interactions, and validate oncology drug targets with a reproducible depmap guide for Data Analysis.
This skill scores 78/100, which means it is a solid listing candidate for directory users: it has a real, domain-specific workflow for DepMap lookups and target validation, and it should help agents do more than rely on a generic prompt. Users should still expect some adoption friction because the repository is strong on explanation but light on executable integration details.
- Clear biomedical use cases: gene dependency, synthetic lethality, and drug sensitivity are explicitly named in the frontmatter and overview.
- Substantial operational content: the body is long, well-structured, and includes multiple headings plus workflow-oriented sections rather than placeholder text.
- Good triggerability for oncology tasks: the skill points users toward specific DepMap resources, including portal, downloads, and API references.
- No install command, scripts, or support files are provided, so agents may need manual setup or interpretation beyond the skill text.
- The repository appears to be documentation-heavy rather than tool-integrated, so execution may still require external navigation to DepMap resources.
Overview of depmap skill
What depmap is for
The depmap skill helps you work with the Cancer Dependency Map to answer practical oncology questions from cell line data: which genes are essential, which dependencies are cancer-selective, and which features predict drug sensitivity or gene effect. If you need depmap for Data Analysis, this skill is aimed at turning a biological question into a reproducible query plan instead of a vague prompt.
Who should use it
Use the depmap skill if you are validating targets, looking for synthetic lethal pairs, comparing mutation-defined groups, or trying to connect CRISPR dependency signals with drug response. It is a good fit for researchers, analysts, and agents that need structured DepMap interpretation rather than generic literature search.
What makes it useful
The main value is decision support: depmap helps you move from “is this gene interesting?” to “is this gene selectively essential in a specific cancer context, and what evidence supports that?” It is especially helpful when you need to distinguish broad pan-essential genes from context-specific vulnerabilities.
How to Use depmap skill
Install depmap
Install the depmap skill with npx skills add K-Dense-AI/claude-scientific-skills --skill depmap. After install, confirm the skill is available in your workspace before you rely on it for analysis or prompt routing.
Start with the right inputs
For strong depmap usage, give the skill a concrete biological question, a gene or gene set, a disease context, and any filter you care about. Better inputs look like: “Use depmap to test whether KRAS-mutant lung adenocarcinoma lines show dependency on SLC1A5, and summarize gene effect patterns and likely caveats.” Weak inputs like “analyze cancer genes” leave too much ambiguity.
Read the file in the right order
Start with SKILL.md to understand the intended workflow, then inspect any linked examples or adjacent repository context if present. In practice, the most useful reading path is the overview first, then the sections that explain when to use the skill, core concepts, and dependency score interpretation so you do not confuse essentiality with expression or correlation.
Use it in an analysis workflow
Treat depmap as a query-and-interpretation skill, not a standalone answer engine. First define the question, then identify the relevant dataset type, then ask for a compact readout: strongest dependencies, subgroup differences, directionality, and any confounders such as lineage effects or broad essential genes. This keeps depmap results usable for downstream Data Analysis.
depmap skill FAQ
Is depmap only for oncology work?
Yes, primarily. depmap is designed around cancer cell lines and dependency data, so it is best used for oncology target validation, vulnerability discovery, and related hypothesis testing rather than general biomedical retrieval.
How is depmap different from a normal prompt?
A normal prompt may summarize DepMap concepts, but the depmap skill is meant to guide a structured analysis workflow around dependency scores, mutation context, and interpretation. That usually produces clearer, more actionable output than asking a model to “look up DepMap” without context.
Is depmap beginner-friendly?
It is usable for beginners if you can name a gene, cancer type, or response question. The main limitation is not the skill itself but the quality of the input: if you do not specify the biological context, depmap cannot reliably narrow the result.
When should I not use depmap?
Do not use depmap when you need patient-level evidence, wet-lab validation, or non-cancer biology. It is also a poor fit if your question depends on a very specific external dataset that is not represented in DepMap.
How to Improve depmap skill
Give the skill the analysis frame
The best depmap results come from questions that specify gene, context, and decision goal. Include the exact gene or pathway, the cancer subtype, and whether you care about essentiality, synthetic lethality, or drug sensitivity. For example: “Compare dependency of POLR2A across ovarian, lung, and colorectal lines, and flag whether the signal looks lineage-driven or mutation-linked.”
Ask for interpretable outputs
Request the output you will actually use: ranked candidates, subgroup contrasts, key caveats, and a short recommendation. If you only ask for “results,” the answer may be too broad for depmap for Data Analysis. If you ask for “top dependencies in BRAF-mutant melanoma with brief interpretation and known confounders,” you get a more decision-ready readout.
Iterate on the first pass
If the first depmap answer is too broad, narrow by lineage, alteration type, or assay type; if it is too narrow, expand to adjacent genes or related lineages. The most useful iteration pattern is: broad screen, subgroup check, then interpretation against essentiality and selectivity.