697 skills · sorted by relevance
Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.
The English summary is being prepared. Showing the original description.
Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
The English summary is being prepared. Showing the original description.
View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.
The English summary is being prepared. Showing the original description.
Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.
The English summary is being prepared. Showing the original description.
Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.
The English summary is being prepared. Showing the original description.
Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.
The English summary is being prepared. Showing the original description.
Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.
The English summary is being prepared. Showing the original description.
View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.
The English summary is being prepared. Showing the original description.
Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
The English summary is being prepared. Showing the original description.
Train language models on pairs of preferred and rejected answers without a separate reference model. The skill guides training setup, model-specific configurations, parameter tuning, and troubleshooting for unstable training or memory limits.
Build evolutionary trees from related nucleotide or protein sequences using MAFFT with IQ-TREE or FastTree. Review branch support, a measure of evidence for each grouping, and produce tree summaries and optional visualizations.
Turn small-molecule structures into numerical features for similarity searches and models that predict activity or properties. Using Molfeat, the skill covers representation choices, invalid inputs, alignment with experimental labels, and saving feature-generation settings for reuse.
Support product strategy with market and competitor analysis, vision setting, team design, and performance measures. Includes a Python script that turns company objectives into linked product and team goals and calculates alignment scores.
Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.
Prepare text for language models using HuggingFace Tokenizers, which splits text into model-readable units. Train custom vocabularies, process batches, track original text positions, and configure input lengths for use with transformers.
Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.
Prepares medical device quality systems for ISO 13485 audits using six focused questions. Reviews design records, corrective actions, process validation, risk files, post-market monitoring, and management reviews, then summarizes readiness gaps and three priority actions.
Refine interface details such as rounded corners, shadows, typography, icon alignment, and animations. Use concrete implementation rules to improve visual consistency and interaction feedback, with changes documented in before-and-after tables.
Maintain personal goals and beliefs with timestamped backups, or analyze project documents to connect problems, goals, strategies, and projects. Turn findings about progress, bottlenecks, and alignment into reports, presentation points, or interactive dashboards.
Prepare large text collections for AI models with fast tokenizers, which split text into model inputs. Train custom vocabularies, trace tokens to original text positions, adjust input lengths, and integrate the results with transformers.
Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.
Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.
Refine interface surfaces, icons, and motion with specific values for corners, shadows, alignment, and transitions. Preserve the project's existing design conventions while identifying visual inconsistencies, touch interaction problems, and missing alternatives for reduced motion.
Review and improve interface grouping, alignment, spacing, and responsive structure. Check whether content and actions remain accessible when screens resize, text grows in translation, zoom increases, or the layout switches to right-to-left reading.