Results for “value”

Map observed site vegetation, species lists, land-cover descriptions, or plant management PFTs to EcoSIM vegetation codes using templates/ecosim_pftpar_20260303.nc.cdl. Use when choosing or validating EcoSIM pft_type values, pfts entries, pfts_short categories, pfts_long plant type meanings, or the combined short-code plus Koppen climate numerical code used by plant management inputs.

The English summary is being prepared. Showing the original description.

54 production-quality design systems extracted from real websites. Load a template to generate HTML/CSS that matches the visual identity of sites like Stripe, Linear, Vercel, Notion, Airbnb, and more. Each template includes colors, typography, components, layout rules, and ready-to-use CSS values.

The English summary is being prepared. Showing the original description.

Query Solana blockchain data with USD pricing — wallet balances, token portfolios with values, transaction details, NFTs, whale detection, and live network stats. Uses Solana RPC + CoinGecko. No API key required.

The English summary is being prepared. Showing the original description.

Use when the user wants to evolve the features of a PRD roadmap with user stories — 'refinar as histórias da feature', 'escrever histórias de usuário', 'critérios de aceite', 'regras de negócio da feature', 'deixar a feature pronta', 'user stories for FT01' — one feature at a time, interviewing the human until each story meets the Definition of Ready: persona, value, INVEST, CA (Gherkin, happy and error paths) and RN from the PRD. Do NOT use to create the roadmap (prd-roadmap) or to change requirements without stories (prd-refine).

The English summary is being prepared. Showing the original description.

Use when a change is small and already understood - at most three files, such as a typo, a config value, a rename, or a one-line bugfix - and a plan file would cost more than the change

The English summary is being prepared. Showing the original description.

Create consistent, scalable design systems using Tailwind CSS utility classes with custom themes, design tokens, and responsive design patterns. Use when building design systems, implementing custom themes, creating reusable utility patterns, configuring Tailwind theme extensions, implementing dark mode, building responsive layouts, creating design tokens, using arbitrary values, or establishing consistent spacing and typography scales.

The English summary is being prepared. Showing the original description.

When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," "in-app pricing," "free users won't upgrade," "trial to paid conversion," or "how do I get users to pay." Use this for any in-product moment where you're asking users to upgrade. Distinct from public pricing pages (see page-cro) — this focuses on in-product upgrade moments where the user has already experienced value. For pricing decisions, see pricing-strategy.

The English summary is being prepared. Showing the original description.

When the user wants to write, rewrite, or improve marketing copy for any page — including homepage, landing pages, pricing pages, feature pages, about pages, or product pages. Also use when the user says "write copy for," "improve this copy," "rewrite this page," "marketing copy," "headline help," "CTA copy," "value proposition," "tagline," "subheadline," "hero section copy," "above the fold," "this copy is weak," "make this more compelling," or "help me describe my product." Use this whenever someone is working on website text that needs to persuade or convert. For email copy, see email-sequence. For popup copy, see popup-cro. For editing existing copy, see copy-editing.

The English summary is being prepared. Showing the original description.

When the user wants to plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," "free resource," "ROI calculator," "grader tool," "audit tool," "should I build a free tool," or "tools for lead gen." Use this whenever someone wants to build something useful and give it away to attract leads or earn links. For downloadable content lead magnets (ebooks, checklists, templates), see lead-magnets.

The English summary is being prepared. Showing the original description.

When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much should I charge,' 'my pricing is wrong,' 'pricing page,' 'annual vs monthly,' 'per seat pricing,' or 'should I offer a free plan.' Use this whenever someone is figuring out what to charge or how to structure their plans. For in-app upgrade screens, see paywall-upgrade-cro.

The English summary is being prepared. Showing the original description.

Comprehensive dimension analysis and reporting for CJA. Use this skill whenever the user wants to analyze one or more dimensions — including cardinality, distribution/skew, trends, anomalies, data quality errors, comparisons, and forecasting. Also trigger when someone asks "what are the top values for...", "dimension health", "explore this dimension", "dimension dashboard", "dimension statistics", "data quality check on a dimension", "dimension cardinality", "dimension trends", "dimension skew", "dimension anomalies", "compare dimensions", or any similar request to understand what's inside a CJA dimension. Produces an interactive HTML dashboard or a markdown report. Works with the CJA MCP server.

The English summary is being prepared. Showing the original description.

AEM as a Cloud Service Java/OSGi best practices, guardrails, and legacy-to-cloud pattern transformations. Use for Cloud Service–correct bundles, deprecated APIs, schedulers, ResourceChangeListener, replication, Replicator, JCR observation (javax.jcr.observation.EventListener), OSGi Event Admin (org.osgi.service.event.EventHandler), DAM AssetManager, BPA-style fixes, HTL (Sightly) Cloud SDK lint warnings (data-sly-test redundant constant value comparison), or any time you need the detailed pattern reference modules under this skill.

The English summary is being prepared. Showing the original description.

Orchestrator for the pre-decision business-idea lifecycle — generate ideas, model them, evaluate them, validate them with customers, and only then hand off to product-soul / brainstorming. Routes through `idea-generation`, `business-modeling`, `idea-evaluation`, and `customer-discovery`. Load when the user asks to explore business ideas, find a startup idea, evaluate a venture, validate an idea, says "what business should I start", "should I build this", "is this a good business", "I have a startup idea", "evaluate this venture", "model this business", "validate this idea", "Mom Test this", "Lean Canvas this", "Business Model Canvas", "Value Proposition Canvas", "go/no-go on this idea". Pre-decision suite — once one idea is committed, hands off to `product-soul`. Does NOT design features (use `brainstorming`) or audit built products (use `reality-check`).

The English summary is being prepared. Showing the original description.

Manage secrets using Azure Key Vault Secrets SDK for JavaScript (@azure/keyvault-secrets). Use when storing and retrieving application secrets or configuration values.

The English summary is being prepared. Showing the original description.

Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.

The English summary is being prepared. Showing the original description.

Azure App Configuration SDK for Java. Centralized application configuration management with key-value settings, feature flags, and snapshots.

The English summary is being prepared. Showing the original description.

Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

The English summary is being prepared. Showing the original description.

Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.

The English summary is being prepared. Showing the original description.

Detects A/B chromatin compartments from balanced Hi-C contact matrices via eigenvector decomposition of the distance-normalized, Pearson-correlated cis matrix with cooltools (eigs_cis), then orients (phases) the compartment eigenvector against a GC or gene-density track so the active (A) sign is not arbitrary. Covers the eigenvector-is-a-choice problem (per-arm view_df to remove the centromere gradient; picking the eigenvector by max correlation with activity, not by eigenvalue), GC phasing with bioframe.frac_gc, resolution choice (100kb-1Mb), saddle plots and saddle_strength for compartmentalization strength, the cohesin-loss-strengthens-compartments result, subcompartments (SNIPER/Calder/dcHiC), and cross-condition compartment switching. Use when calling A/B compartments, computing E1/eigenvectors, phasing the eigenvector, building saddle plots, choosing a compartment resolution, quantifying compartment strength, or comparing compartmentalization across conditions.

The English summary is being prepared. Showing the original description.

Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.

The English summary is being prepared. Showing the original description.

Generates, normalizes, and converts bedGraph signal tracks (4-column chrom/start/end/value, 0-based half-open) with bedtools genomecov, deepTools bamCoverage/bamCompare/bigwigCompare, bedtools unionbedg, and UCSC bedGraphToBigWig. Covers why a raw coverage bedGraph is not comparable across samples until normalized, the CPM/RPKM/BPM/RPGC normalization menu and the conserved-total assumption that makes them wrong under a global perturbation, the strict sorted-non-overlapping-chrom.sizes bedGraphToBigWig contract that silently corrupts a bigWig, effective-genome-size selection, and bin-size aliasing. Use when building or normalizing a coverage/signal track from a BAM, comparing tracks across samples or conditions, converting bedGraph to a browser-ready bigWig, or diagnosing a track that looks plausible but reports wrong heights.

The English summary is being prepared. Showing the original description.

Profiles a genome from raw reads BEFORE assembly with a k-mer spectrum (KMC or Jellyfish histogram), then models it with GenomeScope2 to estimate genome size, heterozygosity, repeat content, and ploidy, and Smudgeplot to infer ploidy from heterozygous k-mer pairs (diploid AB vs triploid AAB vs tetraploid AABB). Covers choosing k via Merqury best_k.sh, the k-mer-coverage vs sequencing-coverage confusion, reading het/repeat/contamination/organelle peaks, why noisy ONT must not be used for counting, and how the estimate becomes the NG50 denominator, the Flye -g value, the hifiasm --hom-cov/purge setting, and the 1.5-2x-too-big haplotig sanity check. Use when starting any de novo assembly, deciding whether short reads can work, estimating genome size for an unknown organism, diagnosing ploidy, or sanity-checking an assembly's size against expectation.

The English summary is being prepared. Showing the original description.

Add p-value brackets, significance asterisks, and effect-size annotations to distribution plots using ggpubr, ggsignif, and statannotations with correct test selection (parametric vs non-parametric vs paired), multiple-testing adjustment, and rendering of negative results. Use when a boxplot/violin/raincloud needs in-figure statistical comparisons between groups.

The English summary is being prepared. Showing the original description.

Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

The English summary is being prepared. Showing the original description.