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Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication.
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Baseado na classificação ocupacional SOC
Persistent dietary safety layer for DoorDash CLI (dd-cli) ordering. Stores a personal/household dietary profile (allergens with severity tiers, diets, dislikes) that every cart is vetted against before checkout, with a deterministic tripwire — the vetting step saves the full cart contents to a vetted-cart dump, and a PreToolUse hook re-greps that dump against the anaphylaxis-severity allergen list before any checkout URL is allowed. Use when the user mentions allergies or dietary restrictions, when ordering food for someone with restrictions, or on every dd-cli cart flow while this skill is installed. A tripwire, not medical-grade — the human checkout page is the final check.
Group food ordering through the DoorDash CLI (dd-cli) from a persistent team roster. One request ("lunch for the team") fans out into a single merged cart with every line attributed to its eater via a person-to-cart-item-id ledger, per-person cost split with fee proration, payer rotation history, and a checkout gate that re-derives allergen conflicts against the roster live. Use when ordering for multiple people — team lunch, incident-response food, "collect orders from the thread" — or for /whose-turn payer rotation questions. Handles paste-a-thread intake: paste a Slack/chat thread and it builds the order ledger from it.
Accountability layer for agent-driven DoorDash ordering. Works with the doordash-audit-log hook (which appends every dd-cli invocation to an append-only audit log) and the /doordash-report command to answer "what has my AI been ordering and what did it cost" from data instead of memory. Use when the user asks about their DoorDash spending, ordering patterns, what the agent did in past sessions, or wants jq recipes for querying the audit log. Covers log schema, query patterns, rotation, and privacy guidance.
Named, context-bound saved DoorDash orders ("post-gym", "late-night deploy") recalled through the DoorDash CLI (dd-cli) with a mandatory cart-diff before any checkout link is handed over. Use when the user names a saved order ("order my post-gym bowl", "the usual", "my Friday ramen"), wants to save the order they just placed as a playbook, or asks to list/remove saved orders. Persists name→order-uuid playbooks across sessions, catches silent menu/price drift on every recall, and self-heals stale playbooks when a restaurant's menu changes. Requires dd-cli (macOS Apple Silicon, waitlist-gated).
Hard spending policy for agent-driven DoorDash ordering through the DoorDash CLI (dd-cli). Per-order ceiling, daily/weekly/monthly caps, cooldown between orders, and blocked hours — enforced deterministically by routing every cart-mutation and checkout through the dd-guard wrapper script, which prices the cart, checks the policy against a persistent spend ledger, and refuses out-of-policy checkouts with exit code 2. Use when the user wants budget limits on agent food ordering, asks "how much have I spent on DoorDash", wants to set spending caps, or whenever building carts / checking out with dd-cli while this skill is installed. Pairs with a PreToolUse hook that denies raw dd-cli checkout calls that bypass the wrapper.
Segment a SKU portfolio on value (ABC) and demand variability (XYZ), produce the 9-box with a planning policy per cell, and reallocate planner attention accordingly. Use when the user mentions ABC analysis, inventory segmentation, SKU rationalization, stok segmentasyonu, envanter sınıflandırma, or asks which items deserve forecasting effort. Differentiator - ABC alone is treated as half an answer; policy lives in the value x variability combination.
| name | cosmic-database |
| description | Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication. |
COSMIC (Catalogue of Somatic Mutations in Cancer) is the world's largest and most comprehensive database for exploring somatic mutations in human cancer. Access COSMIC's extensive collection of cancer genomics data, including millions of mutations across thousands of cancer types, curated gene lists, mutational signatures, and clinical annotations programmatically.
This skill should be used when:
COSMIC requires authentication for data downloads:
uv pip install requests pandas
Use the scripts/download_cosmic.py script to download COSMIC data files:
from scripts.download_cosmic import download_cosmic_file
# Download mutation data
download_cosmic_file(
email="your_email@institution.edu",
password="your_password",
filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz",
output_filename="cosmic_mutations.tsv.gz"
)
# Download using shorthand data type
python scripts/download_cosmic.py user@email.com --data-type mutations
# Download specific file
python scripts/download_cosmic.py user@email.com \
--filepath GRCh38/cosmic/latest/cancer_gene_census.csv
# Download for specific genome assembly
python scripts/download_cosmic.py user@email.com \
--data-type gene_census --assembly GRCh37 -o cancer_genes.csv
import pandas as pd
# Read mutation data
mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')
# Read Cancer Gene Census
gene_census = pd.read_csv('cancer_gene_census.csv')
# Read VCF format
import pysam
vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz')
Download comprehensive mutation data including point mutations, indels, and genomic annotations.
Common data types:
mutations - Complete coding mutations (TSV format)mutations_vcf - Coding mutations in VCF formatsample_info - Sample metadata and tumor information# Download all coding mutations
download_cosmic_file(
email="user@email.com",
password="password",
filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz"
)
Access the expert-curated list of ~700+ cancer genes with substantial evidence of cancer involvement.
# Download Cancer Gene Census
download_cosmic_file(
email="user@email.com",
password="password",
filepath="GRCh38/cosmic/latest/cancer_gene_census.csv"
)
Use cases:
Download signature profiles for mutational signature analysis.
# Download signature definitions
download_cosmic_file(
email="user@email.com",
password="password",
filepath="signatures/signatures.tsv"
)
Signature types:
Access gene fusion data and structural rearrangements.
Available data types:
structural_variants - Structural breakpointsfusion_genes - Gene fusion events# Download gene fusions
download_cosmic_file(
email="user@email.com",
password="password",
filepath="GRCh38/cosmic/latest/CosmicFusionExport.tsv.gz"
)
Retrieve copy number alterations and gene expression data.
Available data types:
copy_number - Copy number gains/lossesgene_expression - Over/under-expression data# Download copy number data
download_cosmic_file(
email="user@email.com",
password="password",
filepath="GRCh38/cosmic/latest/CosmicCompleteCNA.tsv.gz"
)
Access drug resistance mutation data with clinical annotations.
# Download resistance mutations
download_cosmic_file(
email="user@email.com",
password="password",
filepath="GRCh38/cosmic/latest/CosmicResistanceMutations.tsv.gz"
)
COSMIC provides data for two reference genomes:
Specify the assembly in file paths:
# GRCh38 (recommended)
filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz"
# GRCh37 (legacy)
filepath="GRCh37/cosmic/latest/CosmicMutantExport.tsv.gz"
latest in file paths to always get the most recent releasev102, v101, etc.Filter mutations by gene:
import pandas as pd
mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')
tp53_mutations = mutations[mutations['Gene name'] == 'TP53']
Identify cancer genes by role:
gene_census = pd.read_csv('cancer_gene_census.csv')
oncogenes = gene_census[gene_census['Role in Cancer'].str.contains('oncogene', na=False)]
tumor_suppressors = gene_census[gene_census['Role in Cancer'].str.contains('TSG', na=False)]
Extract mutations by cancer type:
mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')
lung_mutations = mutations[mutations['Primary site'] == 'lung']
Work with VCF files:
import pysam
vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz')
for record in vcf.fetch('17', 7577000, 7579000): # TP53 region
print(record.id, record.ref, record.alts, record.info)
For comprehensive information about COSMIC data structure, available files, and field descriptions, see references/cosmic_data_reference.md. This reference includes:
Use this reference when:
The download script includes helper functions for common operations:
from scripts.download_cosmic import get_common_file_path
# Get path for mutations file
path = get_common_file_path('mutations', genome_assembly='GRCh38')
# Returns: 'GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz'
# Get path for gene census
path = get_common_file_path('gene_census')
# Returns: 'GRCh38/cosmic/latest/cancer_gene_census.csv'
Available shortcuts:
mutations - Core coding mutationsmutations_vcf - VCF format mutationsgene_census - Cancer Gene Censusresistance_mutations - Drug resistance datastructural_variants - Structural variantsgene_expression - Expression datacopy_number - Copy number alterationsfusion_genes - Gene fusionssignatures - Mutational signaturessample_info - Sample metadatalatest for the most recent versionCOSMIC data integrates well with:
When using COSMIC data, cite: Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research. 2019;47(D1):D941-D947.