Development Skills

Automate GitHub repositories, issues, pull requests, branches, CI/CD, and permissions via Rube MCP (Composio). Manage code workflows, review PRs, search code, and handle deployments programmatically.

Turn error logs, screenshots, voice notes, and rough bug reports into crisp, developer-ready GitHub issues with repro steps, impact, and evidence.

Patterns for automating GitHub workflows with AI assistance, inspired by [Gemini CLI](https://github.com/google-gemini/gemini-cli) and modern DevOps practices.

Master Go 1.21+ with modern patterns, advanced concurrency, performance optimization, and production-ready microservices.

Expert patterns for HubSpot CRM integration including OAuth authentication, CRM objects, associations, batch operations, webhooks, and custom objects. Covers Node.js and Python SDKs.

Stage all changes, create a conventional commit, and push to the remote branch. Use when explicitly asks to push changes (\"push this\", \"commit and push\"), mentions saving work to remote (\"save to github\", \"push to remote\"), or completes a feature and wants to share it.

Use when working with the WordPress Abilities API (wp_register_ability, wp_register_ability_category, /wp-json/wp-abilities/v1/*, @wordpress/abilities) including defining abilities, categories, meta, REST exposure, and permissions checks for clients.

Use when the user asks about WordPress codebases (plugins, themes, block themes, Gutenberg blocks, WP core checkouts) and you need to quickly classify the repo and route to the correct workflow/skill (blocks, theme.json, REST API, WP-CLI, performance, security, testing, release packaging).

Use when investigating or improving WordPress performance (backend-only agent): profiling and measurement (WP-CLI profile/doctor, Server-Timing, Query Monitor via REST headers), database/query optimization, autoloaded options, object caching, cron, HTTP API calls, and safe verification.

Use for WordPress Playground workflows: fast disposable WP instances in the browser or locally via @wp-playground/cli (server, run-blueprint, build-snapshot), auto-mounting plugins/themes, switching WP/PHP versions, blueprints, and debugging (Xdebug).

Use when developing WordPress plugins: architecture and hooks, activation/deactivation/uninstall, admin UI and Settings API, data storage, cron/tasks, security (nonces/capabilities/sanitization/escaping), and release packaging.

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

INVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response, single_step, trajectory, RAG), CLI management commands, SDK-based creation, and example management. Uses the langsmith CLI tool.

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Comprehensive guide for creating software diagrams using Mermaid syntax. Use when users need to create, visualize, or document software through diagrams including class diagrams (domain modeling, object-oriented design), sequence diagrams (application flows, API interactions, code execution), flowcharts (processes, algorithms, user journeys), entity relationship diagrams (database schemas), C4 architecture diagrams (system context, containers, components), state diagrams, git graphs, pie charts, gantt charts, or any other diagram type. Triggers include requests to "diagram", "visualize", "model", "map out", "show the flow", or when explaining system architecture, database design, code structure.

> Use this skill when the user asks about Worktrunk (wt), git worktree management, running parallel AI agents with worktrees, setting up wt hooks, configuring wt.toml, using `wt switch`, `wt list`, `wt merge`, `wt remove`, `wt step`, LLM commit messages, or integrating Worktrunk with your AI agent. Also trigger when the user wants to run multiple AI agent sessions in parallel across isolated git branches, or automate dev server / database / dependency setup per worktree. Always use this skill for any question involving the `wt` CLI, worktree lifecycle automation, or the worktrunk plugin.

Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.

Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.

Molecular docking with AutoDock Vina (Python API). Receptor/ligand prep (Meeko + RDKit), grid box, docking, pose and binding energy analysis, and batch virtual screening.

Infer and visualize intercellular communication from scRNA-seq with CellChat (R). Build CellChat from Seurat/counts → subset CellChatDB ligand-receptor pairs → over-expressed genes per group → communication probabilities → pathway signaling → network centrality (senders/receivers/influencers) → chord/heatmap/bubble plots → cross-condition compare. Human, mouse. Use liana for pure-Python.

Query ChEMBL (2M+ compounds, 19M+ bioactivity measurements, 13K+ targets) via the public REST/JSON API with plain `requests` — no SDK install required. Search compounds, retrieve IC50/Ki/EC50 bioactivities, find target inhibitors, run SAR, access drug mechanism/indication data.

Detect somatic CNVs from WES/WGS/targeted BAMs (CNVkit v0.9.x). Bin coverage in target/antitarget regions, normalize vs reference, segment with CBS/HMM, call amps/dels, scatter/diagram plots, purity/ploidy, VCF/SEG export. CLI plus Python API (cnvlib). Use GATK CNV for deep WGS with population controls; use CNVkit for targeted/exome where antitarget bins matter.

>- Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.

ENA REST API for sequences, reads, assemblies, and annotations. Portal API search, Browser API retrieval (XML/FASTA/EMBL), file reports for FASTQ/BAM URLs, taxonomy, cross-refs. For multi-DB Python use bioservices; for NCBI-only use pubmed-database or Biopython Entrez.

Unified CLI/Python interface to 20+ genomic databases. Gene lookups (Ensembl search/info/seq), BLAST/BLAT, AlphaFold, Enrichr enrichment, OpenTargets disease/drug, CELLxGENE single-cell, cBioPortal/COSMIC cancer, ARCHS4 expression. Spans genomics, proteomics, disease. For batch/advanced BLAST use biopython; for multi-DB Python SDK use bioservices.

Harmony batch correction for scRNA-seq and other omics. Removes batch effects from PCA embeddings while preserving biology. Run after PCA, before UMAP. Scales to millions of cells. Python (harmonypy, scanpy) and R (Seurat).

Guide to KEGG pathway enrichment for DEG results. Covers ORA vs GSEA, mandatory directionality splitting, KEGG organism codes, API failure handling with offline fallbacks, cross-condition comparisons, and answer-first reporting. Consult when running enrichment with clusterProfiler or gseapy.

Build, read, validate, modify SBML biological network models via the libSBML Python API. SBML Levels 1–3, reactions/kinetic laws, species, rules, FBC extension for flux balance, conversion. Interoperates with COBRApy, Tellurium/RoadRunner, COPASI. Use when programmatically constructing ODE or constraint-based metabolic/signaling models in SBML.

MaxQuant + Perseus proteomics pipeline: run MaxQuant for LFQ and SILAC; parse proteinGroups.txt in Python; filter contaminants/decoys; log2 + median-normalize; impute MNAR; t-test with FDR; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo-native processing; FragPipe/MSFragger for GPU-accelerated DB search.

Python API v2 for Opentrons OT-2/Flex liquid handlers: protocols as Python files with metadata and run(); control pipettes, labware, and modules (thermocycler, heater-shaker, magnetic, temperature). Simulate via opentrons_simulate then upload. Use PyLabRobot for vendor-agnostic scripts (Hamilton, Tecan).

Pure Python DICOM for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM, pixels as NumPy, edit tags, windowing (VOI LUT), PHI anonymization, build DICOM, series→3D volumes. Use histolab for WSI pathology; nibabel for NIfTI.

Hardware-agnostic Python liquid-handler library: portable scripts run on Hamilton STAR, Tecan Freedom EVO, Opentrons OT-2, or a simulator without vendor lock-in. For protocol automation, method dev, plate reformatting, serial dilutions, and Python lab workflows.

Python framework for single- and multi-objective optimization with evolutionary algorithms. Define vectorized objectives and constraints; solve with NSGA-II, NSGA-III, MOEA/D, GAs, or differential evolution. Analyze Pareto fronts, visualize trade-offs, customize operators and callbacks. For engineering design, hyperparameter search, and conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient), platypus, jMetalPy (Java).

Read/write SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ. Region queries, pileup, variant filtering, read groups. Python htslib wrapper exposing samtools/bcftools CLI. Use STAR/BWA for alignment; GATK/DeepVariant for variant calling.

CLI toolkit for SAM/BAM/CRAM: sort, index, convert, filter, QC alignments. Core commands: view, sort, index, flagstat, stats, depth, markdup, merge. Required between alignment and variant/peak calling. Use pysam for Python-native BAM access; deeptools for normalized coverage tracks.

Structure-activity relationship (SAR) analysis guide for drug discovery including molecular descriptor analysis, scaffold analysis, and activity cliff detection.

Process-based discrete-event simulation. Model queues, shared resources, timed events: manufacturing, service ops, network traffic, logistics. Processes are Python generators yielding events. Resources: capacity-limited (Resource/Priority/Preemptive), bulk (Container), objects (Store, FilterStore). For continuous use SciPy ODEs; for agent-based use Mesa.

Annotate and filter VCF variants with SnpEff and SnpSift. SnpEff predicts functional effects (HIGH/MODERATE/LOW/MODIFIER), genes, transcripts, AA changes, HGVS; SnpSift filters and adds ClinVar/dbSNP. Java CLI with Python subprocess integration. Use ANNOVAR for multi-database annotation; Ensembl VEP for REST API; SnpEff for fast CLI with pre-built genomes.

Unified Python framework for extracellular electrophysiology. Load 20+ formats (SpikeGLX, OpenEphys, NWB, Intan, Maxwell, Blackrock), preprocess, run 10+ sorters (Kilosort4, SpykingCircus2, Tridesclous, MountainSort5) via one API, compute quality metrics (SNR, ISI, firing rate), compare sorters, export NWB/Phy. For format-agnostic multi-sorter workflows. For Neuropixels-specific PSTH/decoding use neuropixels.

Symbolic math in Python: exact algebra, calculus (derivatives, integrals, limits), equation solving, symbolic matrices, ODEs, code gen (lambdify, C/Fortran). Use for exact symbolic results. For numerical use numpy/scipy; for stats use statsmodels.

Python library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extraction from fluorescence or brightfield video.

Predict RNA secondary structure, MFE folding, base-pair probabilities, RNA-RNA interactions via ViennaRNA Python bindings. Pipeline: sequence → MFE → partition function and pair-probability matrix → dot-bracket → duplex. Use for siRNA/sgRNA targeting, ribozyme design, RNA accessibility. Use RNAfold CLI for batch use without Python.

Detect IBD-style Distribution Days for QQQ/SPY (close down at least 0.2% on higher volume), track 25-session expiration and 5% invalidation, count d5/d15/d25 clusters, classify market risk (NORMAL/CAUTION/HIGH/SEVERE), and emit TQQQ/QQQ exposure recommendations. Use after market close, before TQQQ exposure changes, or as input to FTD/market-state frameworks. Does not execute trades.

Design new Claude skills from structured idea specifications. Use when the skill auto-generation pipeline needs to produce a Claude CLI prompt that creates a complete skill directory (SKILL.md, references, scripts, tests) following repository conventions.

Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason, thesis type, sector, and mechanism. No API required; pure local calculation.

> (1) setting up Apollo Client in a React project, (2) writing GraphQL queries or mutations with hooks, (3) configuring caching or cache policies, (4) managing local state with reactive variables, (5) troubleshooting Apollo Client errors or performance issues.