Data & Analytics

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Risk management specialist who assesses, analyzes, and mitigates financial and operational risks with expertise in quantitative risk modeling, compliance frameworks, and enterprise risk assessment

Master color management and visual styling with Colorcet. Use this skill when selecting appropriate colormaps, creating accessible and colorblind-friendly visualizations, applying consistent themes, or customizing plot aesthetics with perceptually uniform color palettes.

Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.

Create publication-quality, aesthetically refined charts in Python/Jupyter (matplotlib/seaborn): readable axes, tight whitespace, and curated palettes (categorical/sequential/diverging).

Provision brain-computer interface interaction capabilities.

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Build financial models, backtest trading strategies, and analyze

Local Deep Research includes command-line tools for benchmarking and rate limit management.

Tests genotype-environment associations and identifies loci under local adaptation using LFMM2 (LEA), pcadapt outlier detection, OutFLANK Fst-based selection scans, and redundancy analysis. Detects adaptive genetic variation correlated with environmental variables while controlling for population structure. Use when identifying adaptive loci across environmental gradients, testing for signatures of local adaptation, or predicting genetic vulnerability to climate change with gradientForest.

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

Build maximum likelihood phylogenetic trees using IQ-TREE2 and RAxML-ng. Use when inferring publication-quality trees with model selection, ultrafast bootstrap, or partitioned analyses from sequence alignments.

Identify and classify repetitive elements and transposable elements using RepeatModeler for de novo repeat library construction and RepeatMasker for genome-wide repeat annotation. Quantify TE expression from RNA-seq with TEtranscripts. Use when masking repeats before gene prediction or analyzing transposable element activity.

Check if a marimo notebook is compatible with WebAssembly (WASM) and report any issues.

cat wrangler.jsonc | jq '.observability' wrangler deployments list <WORKER_NAME> curl https://your-worker.workers.dev ``` Ensure `observability.enabled = true`, redeploy Worker, check `head_sampling_rate`, verify traffic **Cause:** Traces not enabled, incorrect sampling rate, Worker not redeployed, or destination unavailable **Solution:** ```jsonc // Temporarily set to 100% sampling for debugging { "observability": { "enabled": true, "head_sampling_rate": 1.0, "traces": { "enabled": true, }, ...

cat wrangler.jsonc | jq '.observability' wrangler deployments list <WORKER_NAME> curl https://your-worker.workers.dev ``` Ensure `observability.enabled = true`, redeploy Worker, check `head_sampling_rate`, verify traffic **Cause:** Traces not enabled, incorrect sampling rate, Worker not redeployed, or destination unavailable **Solution:** ```jsonc // Temporarily set to 100% sampling for debugging { "observability": { "enabled": true, "head_sampling_rate": 1.0, "traces": { "enabled": true } } ...

Reconstruct cell lineage trees from CRISPR barcodes, mitochondrial mutations, or other heritable markers.

Train a machine learning model with specified parameters

Train a machine learning model with specified parameters

Find and identify the CBaseEntity_GetHammerUniqueId virtual function in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or server.so to locate the GetHammerUniqueId vfunc by searching for the "hammerUniqueId" string and identifying the function that calls a vtable method guarded by an OR condition, then writes the result to a CUtlString member.

Find and identify the CBaseEntity_IsPlayerPawn and CBaseEntity_IsPlayerController virtual functions in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or server.so to locate both vfuncs by decompiling ShowHudHint, following its first callee, and extracting the vtable offsets for IsPlayerPawn and IsPlayerController.

Azure Resource Manager SDK for managing Application Insights resources for application performance monitoring.

ALWAYS LOAD THIS SKILL FIRST before writing any PolicyEngine-UK code. Contains the correct API patterns for household calculations and population simulations using the new policyengine package (not policyengine_uk directly). Triggers: "what would", "how much would a", "benefit be", "eligible for", "qualify for", "single parent", "married couple", "family of", "household of", "if they earn", "with income of", "earning £", "making £", "calculate benefits", "calculate taxes", "benefit for a", "t...

Find and identify the CCSPlayer_ItemServices_GiveNamedItem function in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the GiveNamedItem wrapper function in CCSPlayer_ItemServices vtable by searching for the "GiveNamedItem: interpreting" string reference and analyzing vtable entries.