Agent Skills

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name: wiki-mode

description: Read or configure the vault filing methodology and suggest destinations for planned knowledge creation under Generic, LYT, PARA, or Zettelkasten. Use for wiki mode, methodology mode, what is my vault mode, set vault mode, switch to PARA, use LYT, Zettelkasten setup, change mode, configure mode, or methodology routing. This skill does not save content or migrate notes.

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name: wiki-query

description: Answer an explicitly vault-scoped question from an Obsidian wiki without changing it. Use when the user selects the vault as the evidence source: query the wiki, query quick, query deep, explain from the wiki, summarize the vault, find in wiki, search the wiki, or based on the wiki. Do not route ordinary general-knowledge questions here.

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name: wiki-retrieve

description: Build and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta, remote egress requires explicit consent, and unavailable reranking falls back deterministically.

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name: authoring-patch-sets

description: Declare a new firmware patch in a vphone patch set, add a patch set, or write a patch preset. Use when adding or changing a patch in FirmwarePatcher, when a patch needs a version gate, when a patch should be selectable or off by default, or when building an out-of-tree .vphonepatchset.

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name: kernel-analysis-vphone600

description: Analyze vphone600 kernel artifacts using the local symbol database and XNU source tree. Use when working on kernel reverse engineering, address-to-symbol lookup, release-vs-research kernel comparison, or patch analysis for vphone600 variants in this repository.

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name: vphone-guest-control

description: Install vphone-launchpad and VPhone.bundle, create and run vphone virtual iPhones, and drive a running guest from the command line with vphone-launchpad-cli and the per-machine vphone.sock. Make sure to use this skill whenever the user mentions vphone, vphone-launchpad, vphone-launchpad-cli, VPhone.bundle, vphoned, vphone.sock, or a virtual iPhone on their Mac, even if they do not name the tool. That includes setting vphone up, creating/starting/stopping a machine, tapping/swiping/typing/taking screenshots in the guest, calling a vphoned method (apps, files, logs, processes, UI tree), installing an IPA into a guest, installing the roothide/rootless bootstrap (Irisin), ssh into a guest, installing a .deb, or debugging a guest that shows a black screen or will not boot to the UI.

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name: skill-builder

description: Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.

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name: skill-builder

description: Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.

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name: biology-biopython

description: Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.

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name: chemistry-rdkit

description: Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.

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name: cv-classification

description: Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.

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name: cv-detection

description: Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.

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name: data-loading

description: Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.

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name: distributed-training

description: Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.

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name: experimental-design

description: Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.

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name: fba-simulator

description: Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.

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name: flux-analyzer

description: Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.

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name: gsmm-builder

description: Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.

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name: gsmm-validator

description: Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.

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name: hypothesis-formulation

description: Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.

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name: literature-search

description: Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.

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name: meta-analysis

description: Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.

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name: metabolic-study-planner

description: Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is generated.

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name: mfa-pipeline-orchestrator

description: Orchestrate the full metabolic flux analysis pipeline from model loading to phenotype prediction and publication figures. Triggers when the user provides an organism name, BIGG model ID, or custom reaction list and wants end-to-end metabolic modelling run automatically.