Results for “base”

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Use when converting RNA connectivity-table (`.ct`) files into extended FASTA with dot-bracket structures, optionally removing pseudoknots or modified bases.

Use when performing pairwise structural alignments of RNA sequences that incorporate both sequence and structure information through base pair propensity vectors.

Generates personalized greetings based on the time of day and the person's name.

Enforces a verification gate before any completion claim, requiring fresh command output as evidence.

Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Kotlin testing patterns with Kotest, MockK, coroutine testing, property-based testing, and Kover coverage. Follows TDD methodology with idiomatic Kotlin practices.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Proposal pricing workflow producing defensible line-item, tiered, or value-based pricing with calibration and sanity checks. Use when the user needs a quote, estimate, tier packages, pricing strategy, rate calibration, or discount modeling for a proposal brief. Triggers: 'price this proposal', 'build tiered pricing', 'line-item estimate', 'value-based price', 'calibrate rates', 'sanity check this quote'.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Build and maintain a personal knowledge base (wiki) using LLMs. Instead of RAG-style retrieval, the LLM incrementally compiles, cross-references, and maintains a persistent structured wiki from raw sources. Use when user wants to create a knowledge base, build a personal wiki, organize research notes, ingest documents into a structured wiki, or maintain a living knowledge repository.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Scans frontend files for common design system violations using grep-based checks.

Creates a detailed, phased implementation plan with documentation discovery and subagent-based fact gathering.

Generates comprehension questions and language exercises based on analyzed content to create a complete learning loop.

Tauri framework for building cross-platform desktop and mobile apps. Use for desktop app development, native integrations, Rust backend, and web-based UIs.

Apply operations across multiple files simultaneously. Pattern-based bulk modifications, search-and-replace across codebases, consistent changes to many files at once.

Minimal, unopinionated state management library for React with simple hook-based API, no providers, and minimal boilerplate for global state without Redux complexity.

Volume-based PUMP token reward system with day-indexed epochs, pro-rata distribution, accumulator tracking, and cross-program claiming on Solana.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

Based on FeiShu(飞书) / Lark's OpenAPI MCP server, manage user information, chats, emails, cloud documents, multidimensional tables, tasks, calendars, etc.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

Karpathy's LLM Wiki — build and maintain a persistent, interlinked markdown knowledge base. Ingest sources, query compiled knowledge, and lint for consistency.

Kotlin testing patterns with Kotest, MockK, coroutine testing, property-based testing, and Kover coverage. Follows TDD methodology with idiomatic Kotlin practices.

Countdown component for displaying time countdown. Invoke when user needs to implement countdown timers, deadlines, or time-based displays in Naive UI.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

Kotlin testing patterns with Kotest, MockK, coroutine testing, property-based testing, and Kover coverage. Follows TDD methodology with idiomatic Kotlin practices.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

Use when predicting RNA secondary structures, calculating minimum free energy (MFE) folds, or computing partition functions and base pairing probabilities for RNA sequences.

6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.

CLI-team runtime for claude, codex, gemini, antigravity, grok, or cursor workers in tmux panes when you need process-based parallel execution