13c-metabolic-flux
Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps,โฆ
Retrieves and analyzes Cancer Dependency Map (DepMap) release data, including CRISPR Chronos gene effects, cancer model annotations, omics biomarkers, and PRISM drug sensitivity. Supports cancer-selective dependency, co-essentiality, and candidate synthetic-lethality analyses
Claude Code
$ npx -y skills add K-Dense-AI/scientific-agent-skills --skill depmap --agent claude-code
Codex
$ npx skills add K-Dense-AI/scientific-agent-skills --skill depmap -a codex -y
Cursor
$ npx skills add K-Dense-AI/scientific-agent-skills --skill depmap -a cursor -y
Gemini CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill depmap -a gemini-cli -y
GitHub Copilot
$ npx skills add K-Dense-AI/scientific-agent-skills --skill depmap -a github-copilot -y
OpenCode
$ npx skills add K-Dense-AI/scientific-agent-skills --skill depmap -a opencode -y
Windsurf
$ npx skills add K-Dense-AI/scientific-agent-skills --skill depmap -a windsurf -y
name: depmap description: Retrieves and analyzes Cancer Dependency Map (DepMap) release data, including CRISPR Chronos gene effects, cancer model annotations, omics biomarkers, and PRISM drug sensitivity. Supports cancer-selective dependency, co-essentiality, and candidate synthetic-lethality analyses with release-aware identifiers and statistical checks. license: CC-BY-4.0 compatibility: Requires Python 3.10+ with numpy, pandas, and scipy>=1.11 for the bundled helpers. Network access is needed for release discovery; downloading current data may require browser verification through the DepMap portal. metadata: version: "1.2" skill-author: Kuan-lin Huang last-reviewed: "2026-09-30"
Use this skill to rank dependencies in cancer models, compare prespecified biomarker cohorts, inspect co-essentiality, or relate genetic dependencies to PRISM compound response. These analyses generate target and synthetic-lethality hypotheses; they do not establish clinical efficacy, a therapeutic window, or a causal interaction.
DepMap integrates Broad and Sanger CRISPR screens and hosts independent RNAi and drug-screen datasets. Match each question to its assay and pinned release. RNAi DEMETER2 is a different perturbation modality, not an older CRISPR scoring method.
Use [DepMap Downloads](https://depmap.org/portal/data_page/) and the selected release's README and release notes. On review, the live catalogue's latest `DepMap Public` release was **26Q1**, dated 2026-04-01. Rediscover before new work; a release name or expected quarter is not evidence a release is available.
`GET https://depmap.org/portal/api/no-captcha/download/files` returns a complete CSV catalogue, with no request body, API key, or pagination parameters. Required metadata columns are `release`, `release_date`, `filename`, and `md5_hash`. The live response also has `url`: **older files can have links, while recent release rows have blank links**. All 85 entries for 26Q1 had blank `url` values at review. Treat availability per row, not per endpoint.
The [staff announcement](https://forum.depmap.org/t/provide-an-open-endpoint-for-latest-version-retrieval/4652) introduced this metadata route in July 2026. The older `https://depmap.org/portal/api/download/files` catalogue can supply download links but returned an HTML verification page during review, including with HTTP 200. Validate content before parsing. Obtain current files through the portal when verification is required. Refresh expiring signed links immediately before use; do not fabricate storage URLs or assume every new release is on Figshare.
No supported contract was verified for the former `/api/gene?gene_id=...` or `/api/data/gene_dependency` examples. Use local release matrices for gene slices. Do not assume a Python package named `depmap` is an official portal client.
From the skill root, use the bundled [helpers](scripts/depmap_data.py):
from scripts.depmap_data import fetch_catalogue, select_file, verify_md5
catalogue = fetch_catalogue()
public = catalogue[catalogue["release"].str.startswith("DepMap Public ")]
print(public[["release", "release_date"]].drop_duplicates()
.sort_values("release_date", ascending=False).head(12))
# Explicit example pin; inspect discovery output before selecting your release.
release = "DepMap Public 26Q1"
record = select_file(catalogue, release, "CRISPRGeneEffect.csv")
print(record[["release", "filename", "md5_hash"]])
# After obtaining this file from the portal:
# verify_md5("CRISPRGeneEffect.csv", record["md5_hash"])Save the selected metadata, retrieval date, release citation, local checksum, and README alongside analysis outputs. Record source dataset terms separately from this skill's license; newer release terms differ from older Figshare releases. For a missing published checksum, retain a local SHA-256 and explicitly state that it was not compared with a publisher checksum.
The following names were verified in the 26Q1 catalogue. Check that release's README for units and identifier level before treating a file as a numeric matrix.
| File | Use and important contract | |---|---| | `CRISPRGeneEffect.csv` | Integrated, model-level Chronos effects; rows are ModelID, columns preserve `Symbol (EntrezID)` | | `CRISPRGeneEffectUncorrected.csv` | Uncorrected effects; not interchangeable with corrected effects | | `CRISPRGeneDependency.csv` | Release-specific dependency statistics; confirm probability/FDR definition and direction in the README | | `Model.csv` | Model metadata keyed by `ModelID` | | `ModelCondition.csv` | Growth/treatment conditions keyed by `ModelConditionID`, mapping to `ModelID` | | `ScreenGeneEffect.csv`, `ScreenSequenceMap.csv`, `CRISPRScreenMap.csv` | Screen-level effects and mappings; multiple screens can belong to one model | | `OmicsExpressionTPMLogp1HumanProteinCodingGenes.csv` | Expression output with sequencing/model metadata and default-entry flags; exclude metadata columns from expression calculations | | `OmicsSomaticMutationsMatrixDamaging.csv`, `OmicsSomaticMutationsMatrixHotspot.csv` | Distinct mutation annotations; damaging and hotspot calls answer different biological questions | | `OmicsCNGeneWGS.csv`, `OmicsCNGeneMC_WES.csv` | Separate WGS/WES copy-number products; do not concatenate as one uniformly processed cohort | | `PortalOmicsCNGeneLog2.csv` | Transformed portal copy-number product; establish the exact transform before inversion | | `OmicsProfiles.csv`, `Gene.csv` | Omics provenance/mappings and gene annotations | | `AchillesScreenQCReport.csv`, `AchillesSequenceQCReport.csv` | Screen/sequence QC; apply documented eligibility rules rather than invented universal cutoffs |
Current model annotations include `CellLineName`, `OncotreeLineage`, `OncotreePrimaryDisease`, and `OncotreeSubtype`. Le
๐ Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility โ now works with any AI agent that supports the open Agent Skills standard, not just Claude.
Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps,โฆ
Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences,โฆ
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting,โฆ
AlphaGenome API key, free for non-commercial use from deepmind.google.com/science/alphagenome. ALPHA_GENOME_API_KEY is acceptedโฆ
Plans, executes, and documents validation, verification, and transfer of analytical procedures under the governing framework -โฆ
Handles annotated matrices in single-cell analysis, .h5ad and Zarr files, and integration with the scverse ecosystem. This is theโฆ
Use this skill to extract structured Markdown/JSON from PDFs and document imagesโtables with cell-level precision, formulas asโฆ