「number」の検索結果

Catalog and classify materials using standard library systems. Covers descriptive cataloging, subject headings, call number assignment using Dewey Decimal and Library of Congress Classification, MARC record basics, shelf organization, and authority control for consistent access points. Use when organizing a personal, institutional, or community library from scratch, assigning call numbers and subject headings to new acquisitions, reclassifying a collection that has outgrown its original system, or establishing authority control for authors, series, or subjects.

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Scaffold a new Tip in the ai-product-dev-tips repository following the existing directory/format conventions, and register its link in the root README.md. Use this whenever the user wants to add, create, or write a new Tip in this repo — e.g. "nlp_processing に〜の Tip を追加して", "新しい Tips を作って", "〜の解説 Tip を書いて", "this repo に〜のネタを追加". It picks the right category and next sequential number, matches the README format of nearby Tips, generates code scaffolding (Dockerfile / requirements.txt / *_cpu.sh / *_gpu.sh / *.py) only when the Tip needs runnable code, and inserts the link into the correct <details> section of the root README. Trigger this even if the user doesn't say the word "skill" — any request to add a new entry to this Tips collection should use it.

APSA style checker for Quarto (.qmd) files. Checks numbers, capitalization, abbreviations, italics, in-text citations, titles of works, neutral and unbiased language, and APSA-specific terminology against the APSA Style Manual for Political Science (2018, updated 2023). Produces a structured markdown report organized by document section — never modifies the source file. Use when asked to check APSA style, fix citations, review capitalization, check number formatting, or flag biased language in a .qmd document. For grammar, spelling, and punctuation, use the proofread skill instead. Supports an optional output-file argument and an optional @sec-label argument to restrict checking to one section.

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Write Prisma Next queries for Postgres, SQLite, or Mongo — pick a lane (Postgres/SQLite `db.orm.<Model>` + `db.sql.<table>`; Mongo `db.orm.<root>` + `db.query.from(...)` pipeline builder), filter / project / sort / paginate, eager-load with `.include(...)`, Postgres/SQLite `db.transaction(...)`, Postgres/SQLite ORM `.aggregate(...)`, Mongo aggregations via query builder, namespace-aware accessors (`db.orm.<ns>.<Model>`, `db.sql.<ns>.<table>`). Triggers: query, where, match, select, project, orderBy, take, skip, include, lookup, first, all, count, aggregate, group, create, update, delete, upsert, returning, transaction, db.close, script teardown, variant, polymorphism, drizzle-style, kysely-style. Notes: `.all()` is a Thenable (just `await` it), iterators are single-use (`RUNTIME.ITERATOR_CONSUMED`), Postgres `count` is `number` while sum/avg/min/max are `number | null`, ranges use chained `.where()` or `and(...)` (no `.between(...)`).

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Phase 2 of the feature-pipeline suite. Use this after /fp-explore has filed issues and printed the M1 stop hook — when you need to turn a set of filed GitHub issues into an approved implementation plan and a draft PR. Triggers on: "plan the fix", "create the branch and PR", "draft the implementation plan", "ready to plan", "fp-plan", "next step after explore", "convert issues to a plan", "plan phase". Reads issueNumbers from state, writes a Problem Statement, opens a feature branch + draft PR, spawns a HEAVIER sub-agent for the numbered plan, and blocks on explicit user approval before writing plan to state. Does NOT write any code — that is /fp-implement.

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N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.

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Use when a task needs the judgment of an Energy Engineer working on building and industrial energy efficiency — running an ASHRAE Level 1/2/3 energy audit, calculating envelope heat loss/gain with the degree-day method, comparing HVAC equipment on SEER/EER/IEER/COP rather than nameplate alone, designing an IPMVP measurement and verification (M&V) plan, or ranking energy conservation measures (ECMs) by savings-to-investment ratio for a capital plan. Distinct from a wind-energy-engineer or solar/PV engineer (own generation-asset design and yield) and from an electrical-engineer (owns utility-scale power delivery and protection) — this role owns the demand side: how much energy a building or process actually needs, and which retrofit reduces that number with numbers that hold up after the fact.

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Search LCSC Electronics for electronic components — find parts by LCSC number (Cxxxxx) or MPN, check stock/pricing, download datasheets, analyze specifications. Sister company to JLCPCB, same parts library. Sync and maintain a local datasheets directory for a KiCad project. No API key needed — uses the free jlcsearch community API. Use this skill when the user mentions LCSC, JLCPCB parts library, JLCPCB assembly parts, production sourcing, Cxxxxx part numbers, needs to find LCSC equivalents for parts, is preparing a BOM for JLCPCB assembly, or wants to download datasheets and LCSC is available. For package cross-reference tables and BOM workflow, see the `bom` skill.

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Copy-number variant analysis — detection from sequencing/arrays, segmentation, and clinical interpretation.

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Review marketing KPIs and produce an executive summary with insights, anomalies, and recommended actions using the Storytelling with Data framework (Cole Nussbaumer Knaflic). Every review starts with a Big Idea - one sentence capturing the insight, its implication, and why it matters. Use when the user asks for a KPI review, monthly metrics review, marketing dashboard review, "review last month's numbers", quarterly review, performance review, or wants to analyze marketing data. Reads kpis.md and any uploaded data files. For planning a campaign against these numbers, see campaign-brief. For the post-campaign learning loop, see retro. For setting the baseline itself, see brand-context.

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Numerology expert covering life path numbers, name numbers, and Pythagorean to Chaldean systems

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Number theory expert covering primes, divisibility, modular arithmetic, Diophantine equations, and cryptographic applications

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。

Stand up reporting for a new audience — an external client, a business unit, a region, an executive team — end to end: what they need to see, whether the data exists, access and scoping, their place in the pipeline, the standard report set, and validation against numbers they can check themselves. Use when a new audience is onboarded, when an existing one adds a site, a region or a business unit, when their reporting is rebuilt after a change on their side, or when they say the numbers do not match their own records.

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Use when tuning the numbers in a game — economy curves, progression rates, difficulty pacing, drop tables, win/loss odds, time-to-X targets, and currency velocities. Triggers on "balance pass", "tune the economy", "progression curve", "difficulty curve", "drop rate", "XP curve", "TTK", "time-to-content", "balance the economy", "spreadsheet model", "economy sim", or a system spec from game-systems-designer with `TBD` placeholders. Produces an economy spreadsheet, balance pass plan, sim outputs, and per-system balance tables. Stops at the numbers — does not change system rules or pricing. For systems design see game-systems-designer; for store catalog see iap-manager.

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Implements one numbered sub-task from a task list at .ai/[feature]/tasks.md, running linters and tests, without staging, committing, or marking the task complete. Use when asked to carry out a specific sub-task by number, or as the inner step of a larger loop that handles commits separately. For the full loop including commits, use task-orchestration.

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Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.

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Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.

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Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the gene-independent DNA-damage / G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in head and neck), and CRISPRi/a alternatives that bypass the artifact. Use when screening cancer cell lines, diagnosing essentiality at amplified loci, choosing CRISPRcleanR / CERES / Chronos, deciding whether CN correction is needed before MAGeCK / BAGEL2 / drugZ, or switching from Cas9 to CRISPRi.

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Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.

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Build AI phone agents with AgentPhone API. Use when the user wants to make phone calls, send/receive SMS, manage phone numbers, create voice agents, set up webhooks, or check usage — anything related to telephony, phone numbers, or voice AI.

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INTERNAL/HELPER skill — do NOT invoke directly from a user query. This is the shared formatter/poster used by create-issue, create-issue-from-issue-number, and update-issue. It formats an implementation-ready GitHub Issue body, runs the pre-posting checklist, executes `gh issue create` or `gh issue edit`, and optionally posts a 確認事項 follow-up comment. Invoke this skill ONLY from one of the three parent skills via the Skill tool, after the parent has completed analysis. If a user asks to 'format an issue body' or similar, route them to the appropriate parent skill (/create-issue, /create-issue-from-issue-number, or /update-issue) rather than invoking this one directly.

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。