Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.
日本語の概要は準備中です。原文の説明を表示しています。
peacezha/HPClaw☆ 32026年10月11日 更新
Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
日本語の概要は準備中です。原文の説明を表示しています。
peacezha/HPClaw☆ 32026年10月11日 更新
Calculates tumor mutational burden from WES/WGS/panel data with Friends of Cancer Research harmonization equations, per-assay calibration (FDA 10/Mb = 7.8 TSO500 = 8.4 OncomineTML), synonymous/indel/germline filtering, hypermutator tiering, blood TMB, and integration with HLA-LOH and neoantigen quality (Luksza 2017 fitness). Use when assessing ICI eligibility under tumor-specific cutoffs (McGrail 2021), comparing tissue vs bTMB, or auditing TMB-H reporting against ESMO 2024 and FDA pembrolizumab pan-tumor 2020.
日本語の概要は準備中です。原文の説明を表示しています。
peacezha/HPClaw☆ 32026年10月11日 更新
Constructs and validates polygenic risk scores using LDpred2-auto, SBayesRC, MegaPRS, PRS-CS, PROSPER, MUSSEL, BridgePRS, JointPRS, PRSmix, or PGS Catalog Calculator with ancestry-aware reference panels (HapMap3, UKB-LD), Pejaver-style calibration, and PRS-RS reporting standards. Use when computing PRS for cohorts, applying Whiffin-style absolute-risk transformation, assessing cross-ancestry portability (Martin 2017 / Ding 2023 continuous ancestry), or auditing PRS manuscripts against the 22-item PRS-RS reviewer checklist.
日本語の概要は準備中です。原文の説明を表示しています。
peacezha/HPClaw☆ 32026年10月11日 更新
Calculates molecular fingerprints (ECFP/Morgan, FCFP, MACCS, RDKit, AtomPair, TopologicalTorsion, Avalon, MAP4, MHFP6) and physicochemical descriptors (Lipinski, QED, TPSA, Crippen LogP, 3D shape) with explicit choice tables, bit vs count semantics, and partial-charge model selection. Use when featurizing molecules for similarity, QSAR, virtual screening, or ML, or selecting the correct fingerprint for a chemotype-aware task.
日本語の概要は準備中です。原文の説明を表示しています。
peacezha/HPClaw☆ 32026年10月11日 更新
Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.
日本語の概要は準備中です。原文の説明を表示しています。
peacezha/HPClaw☆ 32026年10月11日 更新
Generate pileup data for variant calling using samtools mpileup and pysam. Use when preparing data for variant calling, analyzing per-position read data, or calculating allele frequencies.
日本語の概要は準備中です。原文の説明を表示しています。
peacezha/HPClaw☆ 32026年10月11日 更新
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.
日本語の概要は準備中です。原文の説明を表示しています。
peacezha/HPClaw☆ 32026年10月11日 更新
Generate alignment statistics using samtools flagstat, stats, depth, coverage, and mosdepth. Use when assessing alignment quality, calculating coverage, or generating QC reports.
日本語の概要は準備中です。原文の説明を表示しています。
peacezha/HPClaw☆ 32026年10月11日 更新
Use this skill when the user asks about workers compensation for trucking — required by state law, classification of drivers, premium calculation (rate × experience modifier × payroll), Common Claim types in trucking, returning injured drivers to work, claims management, premium reduction strategies, and the difference between WC and Occupational Accident insurance for owner-operators. Reference state WC laws + IFTA / fleet insurance.
日本語の概要は準備中です。原文の説明を表示しています。
x3allamerican/skills☆ 32026年5月17日 更新
Use this skill to file your IFTA quarterly fuel tax return. Covers filing windows, calculation, payment, and refund situations.
日本語の概要は準備中です。原文の説明を表示しています。
x3allamerican/skills☆ 32026年5月17日 更新
Use this skill when the user asks about cargo securement requirements under 49 CFR 393 Subpart I — Working Load Limit (WLL), tiedown counts, tiedown types, commodity-specific securement rules (lumber, metal coils, paper rolls, concrete pipe, intermodal containers, vehicles, large boulders, dressed lumber, building materials), CVSA out-of-service criteria, and how to calculate aggregate WLL. Cite 49 CFR 393.100-136.
日本語の概要は準備中です。原文の説明を表示しています。
x3allamerican/skills☆ 32026年5月17日 更新
Use when a carrier asks how to calculate cost-per-mile (CPM) at the fleet, truck, lane, or driver level — separating fixed vs variable costs, allocating overhead, accounting for empty miles, and using CPM to negotiate rates, build pricing tiers, or evaluate lane profitability.
日本語の概要は準備中です。原文の説明を表示しています。
x3allamerican/skills☆ 32026年5月17日 更新
Recipe for building one focused UI section out for every consumer. Use when asked to 'make this section canonical', 'productize this widget', give it 'the full treatment', or set it up 'like the SEO metadata system', or when building a new analyzer, preview, or calculator section.
日本語の概要は準備中です。原文の説明を表示しています。
armanisadeghi/ai-matrx☆ 32026年10月13日 更新
Redux Toolkit selector rules against re-renders, recalculation warnings, and render loops. Use when writing useSelector/useAppSelector or createSelector, deriving state in a connected component, or seeing 'Selector returned a different result when called with the same parameters'.
日本語の概要は準備中です。原文の説明を表示しています。
armanisadeghi/ai-matrx☆ 32026年10月13日 更新
(core) route header and body-height conformance via PageHeader. Use when fixing a (core) page's header, title bar, breadcrumbs, sub-route nav, body height (h-page, calc(100dvh…)), mobile header, or buttons hidden behind the avatar, or on a header-conformance assignment. NOT for /administration/* routes.
日本語の概要は準備中です。原文の説明を表示しています。
armanisadeghi/ai-matrx☆ 32026年10月13日 更新
Analyzes coupling and cohesion metrics in PHP codebases. Calculates Afferent/Efferent coupling (Ca/Ce), LCOM metrics, instability index, and abstractness. Identifies highly coupled modules and low cohesion classes.
日本語の概要は準備中です。原文の説明を表示しています。
MikeCheng1208/BattleTree☆ 22026年7月22日 更新
Transform academic course assignment PDFs into structured, actionable markdown checklists with dates, unique IDs, and custom tags. Asks when the user will start, assigns tasks only to weekdays (Monday-Friday), respects weekends automatically, and calculates forum deadlines 3 days before due date. Use this skill when the user uploads academic PDFs or asks to create a task plan from course assignments.
日本語の概要は準備中です。原文の説明を表示しています。
MikeCheng1208/BattleTree☆ 22026年7月22日 更新
Expert in statistical analysis for blind A/B and ABX audio testing. Validates randomization, calculates statistical significance, and ensures proper experimental design. Use when implementing A/B test features or analyzing test results.
日本語の概要は準備中です。原文の説明を表示しています。
MikeCheng1208/BattleTree☆ 22026年7月22日 更新
Design and analyze A/B tests, calculate statistical significance, and determine sample sizes for conversion optimization and experiment validation
日本語の概要は準備中です。原文の説明を表示しています。
MikeCheng1208/BattleTree☆ 22026年7月22日 更新
Master automated billing systems including recurring billing, invoice generation, dunning management, proration, and tax calculation.
日本語の概要は準備中です。原文の説明を表示しています。
MMEHDI0606/ai-agent-foundation-template☆ 22026年5月21日 更新
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
日本語の概要は準備中です。原文の説明を表示しています。
David-Li0406/meta-skill-evloving☆ 22026年7月14日 更新
Multi-channel demand generation, paid media optimization, SEO strategy, and partnership programs for Series A+ startups. Includes CAC calculator, channel playbooks, HubSpot integration, and international expansion tactics. Use when planning demand generation campaigns, optimizing paid media, building SEO strategies, establishing partnerships, or when user mentions demand gen, paid ads, LinkedIn ads, Google ads, CAC, acquisition, lead generation, or pipeline generation.
日本語の概要は準備中です。原文の説明を表示しています。
David-Li0406/meta-skill-evloving☆ 22026年7月14日 更新
This skill calculates key financial ratios and metrics from financial statement data for investment analysis
日本語の概要は準備中です。原文の説明を表示しています。
David-Li0406/meta-skill-evloving☆ 22026年7月14日 更新