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概要と使いどころ
標準原価差異分析スキル。標準原価（予定原価）と実際原価の差異を価格差異・数量差異に分解し、 材料費・労務費・製造間接費のカテゴリ別に集計・分析する。差異の有利/不利判定、 責任部門の特定、根本原因の仮説提示を行う。CSVデータのアップロードによる自動分析に対応。 Use when: 製造原価の差異分析を行いたいとき。標準原価計算制度の運用、 月次原価差異レポート作成、原価低減活動の効果測定に使用。 Triggers: "標準原価", "原価差異", "価格差異", "数量差異", "standard cost", "cost variance", "price variance", "quantity variance", "予定原価", "操業度差異", "原価管理"
Split or contain large source files according to the source-file boundary ADR without changing behavior opportunistically.
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Knowledge base from NASA-STD-8729.1A — NASA's R&M (Reliability and Maintainability) standard covering spaceflight and support systems (Revision A, 2017-06-13). Use for NASA's objectives-driven R&M framework — the R&M objectives hierarchy (one Top Objective decomposed into four sub-objectives, paired with tailorable strategies), how R&M requirements are established inside the SMA Plan required by NPR 7120.5, the SMA Technical Authority concurrence/independent-evaluation governance, milestone vs. readiness review gates, the reliability/maintainability/availability vocabulary (Ai vs. Ao, the failure causal chain, risk as a triplet), and the R&M Evidentiary Methods catalogue (FMEA/FMECA, FTA, RBDA, RCM, LORA, plus the space-environment and parts-pedigree analyses). Scoped to NASA spaceflight and support systems and to assurance planning: it is an objectives-and-strategies standard, so it deliberately does NOT prescribe step-by-step analysis procedures (those live in the referenced NASA Preferred Reliability Practices) and is thin on facility R&M, detailed math/derivations, and non-NASA or commercial life cycles.
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Calendar skill with user-adaptive backend selection (Flag 3). Default: local SQLite store. Optional: Google Calendar MCP, Routinika or UpToday as backend — controlled via assist/prefs.json. Without preference the LLM asks the user interactively.
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Produces a draft controls and gap pack from a requirement, regulation, standard or policy and the organisation's control descriptions: obligations broken out of the source text, the controls that appear to address each, apparent coverage, gaps, questions for control owners and actions to assess. Never concludes compliance or that a control is effective; owners and audit assess. Use when the user asks to "map this regulation to our controls", "run a controls gap analysis", "break this standard into obligations" or "where is our control coverage thin". Do not use for comparing a policy document against a standard, use policy-gap-review instead; to draft evidence requests from the mapped controls, use control-evidence-request-pack. Drafts for human review; never approves, authorises or signs off.
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Compares one clause (liability, termination, confidentiality or any other) across several contracts, templates or successive versions and returns a DRAFT comparison table: verbatim clause text per document, the clause split into elements with every difference from the baseline highlighted and categorised, and neutral questions for counsel. Never says which wording is better or enforceable. Use when the user asks to "compare the liability caps across these five supplier contracts", "line up the termination clause in these three drafts", "show how the confidentiality clause changed between versions", "contrast this clause with our template" or "benchmark the indemnity wording across our customer agreements". Do not use for a whole-contract review, use contract-review-pack instead; for screening one NDA against standard positions, use nda-triage. Drafts for human review; never approves, authorises or signs off.
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。
Turns one meeting transcript, recording recap or set of raw notes into concise DRAFT minutes returned in the chat as a single Markdown document: header metadata, attendance and apologies, agenda items each with a short neutral summary and outcome, numbered decisions with an owner, numbered action items with owner and due date, open questions, carried-over actions and the next-meeting line, every entry backed by a short verbatim source fragment and every missing fact marked UNKNOWN. Use when the user asks to "write the minutes", "draft minutes from this transcript", "turn these notes into meeting minutes", "produce the minutes of meeting for today's call", "tidy my rough minutes into the standard format" or "prepare the minutes for circulation". Do not use for extracting action items, task lists or per-owner follow-up emails alone, use transcript-to-actions instead. Drafts for human review; never approves, authorises or signs off.
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。
Turns the text of a regulatory change (new regulation, amendment, rule, guidance or standard revision) into a DRAFT impact note: what changed against the prior text where supplied, who is affected as the text scopes it, an obligations table with dates and owners to confirm, and open interpretation questions for legal. States only what the text says; the rest is UNKNOWN. Use when the user asks to "brief legal on this new regulation", "list what we must do by when under this amendment", "compare the new standard with the version it replaces", "turn this directive into an obligations table" or "what does this rule mean for us". Do not use for assessing existing controls against a requirement set, use controls-gap-pack instead; for gapping a policy against a standard, use policy-gap-review. Drafts for human review; never approves, authorises or signs off.
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。
Ensure designs define trust boundaries, authn/authz responsibilities, and propagation of identity/claims across services.
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。
Ensure architecture/design docs define clear boundaries, ownership, dependency direction, and change impact to avoid tight coupling.
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Enforces error handling standards: silent error prohibition, custom error class hierarchy (AppError base with category never-fallback / transient / permanent, cause preservation, ValidationError / UnauthorizedError / UpstreamError etc. — canonical definitions in PATTERNS.md), external-boundary normalization (normalizeExternalError), Result pattern (Result.ok/Result.fail), proper try-catch with error type checking, structured error logging with a single Logger contract, fallback prohibition categories (isNeverFallback), and HTTP status code mapping. Use when implementing error handling, reviewing catch blocks, or designing error responses.
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DEPRECATED — merged into monday-for-agents. See skills/monday-for-agents/SKILL.md.
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Use when working with Dependabot — dependabot and Renovate dependency update management. Covers vulnerability alerts, dependency update PRs, auto-merge configuration, version pinning, update scheduling, and security advisory tracking. Use when managing dependency updates, reviewing vulnerability alerts, configuring auto-merge policies, or auditing dependency health.
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Diagnose data races, convert callback-based code to async/await, implement actor isolation patterns, resolve Sendable conformance issues, and guide Swift 6 migration. Use when developers mention: (1) Swift Concurrency, async/await, actors, or tasks, (2) "use Swift Concurrency" or "modern concurrency patterns", (3) migrating to Swift 6, (4) data races or thread safety issues, (5) refactoring closures to async/await, (6) @MainActor, Sendable, or actor isolation, (7) concurrent code architecture or performance optimization, (8) concurrency-related linter warnings (SwiftLint or similar; e.g. async_without_await, Sendable/actor isolation/MainActor lint).
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Google Calendar の予定を取得する。「今日の予定」「今週の予定」「今月の予定」「カレンダー予定」「スケジュール確認」などで起動。期間を指定可能。
bu skill covers Dağıt:ing AWS Security Hub as a centralized cloud security posture management platform that aggregates Bul:ings from GuardDuty, Denetle:or, Macie, and third-party tools. It details enabling security standards like CIS AWS Foundations Benchmark, configuring automated remediation, and building executive dashboards for compliance tracking across multi-account AWS organizations.
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Automatically syncs Gmail and Google Calendar with a Notion database to keep track of the status of job applications the user has submitted. Use this skill whenever the user asks to "sync my job applications," "update my job tracker," "check where my interviews stand," or something similar — even if they don't explicitly mention Notion, Gmail, or Calendar, and even if they don't have a Notion database set up for this yet. Requires (or guides the user through setting up) the Notion, Gmail, and Google Calendar MCP connectors.
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Generates standard operating procedures for B2B SaaS marketing operations: campaign naming conventions, UTM governance, tool administration, workflow QA, data hygiene, lead routing, and incident response playbooks. Use when setting up or auditing marketing ops processes, onboarding a new marketing ops hire, standardizing campaign tracking, fixing broken lead routing, cleaning up HubSpot or CRM data, building QA checklists for campaign launches, or creating incident response procedures for marketing system failures. Also use when asked to "fix our UTM mess," "standardize campaign naming," "build a QA checklist," "audit our marketing ops," or "create an ops runbook." Designed for B2B SaaS marketing teams running HubSpot with multi-geo, multi-segment operations.
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Configures the analytics side of a PostHog experiment — exposure criteria (default `$feature_flag_called` vs custom exposure events), primary and secondary metrics, the supported metric types (count, sum, ratio with `math` and `math_property`, retention with `retention_window_start` and `start_handling`), multivariate user handling ("Exclude" vs "First seen variant"), and how to read results once the experiment is live. Use when the user adds or edits a primary or secondary metric (e.g. "add a secondary metric tracking 'downloaded_file' per user"), sets up a ratio metric (e.g. "revenue from purchase_completed / pageviews"), sets up a retention metric (e.g. "$pageview → uploaded_file, 7-day window"), configures custom exposure (e.g. "only count users who hit /checkout"), changes multivariate handling, or asks "who is in the analysis?", "how do I measure impact?", "is this winning?", "what's the confidence level?", or "should I ship?".
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。
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
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。
End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal (mandatory before DADA2 filterAndTrim), denoising with OBITools3 v3 (obi stats plural; DMS-based) or DADA2 ASVs (Callahan 2017), decontam combined method as screening-not-classifier (Davis 2018), tag-jumping with NovaSeq 10x MiSeq caveat (Schnell 2015), Hill-number effective species counts with coverage-based rarefaction (Jost 2006; Chao & Jost 2012; doubling rule), beta-diversity decomposition with MANDATORY PERMANOVA + PERMDISP pair (Anderson & Walsh 2013), constrained ordination, and the read-counts-not-abundance critique (Lamb 2019). Use when processing eDNA samples for biodiversity assessment, deciding ASV vs OTU, configuring OBITools3 v3, interpreting decontam screening, or reporting community comparisons with the dispersion confound check.
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Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.
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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
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Stellar standards, ecosystem, and reference. Covers SEPs (Stellar Ecosystem Proposals), CAPs (Core Advancement Proposals), and a quick map for picking the right standard for wallets, anchors, payments, deposits/withdrawals, federation, deep links, and KYC. Also bundles ecosystem references (DeFi protocols, dev tools, wallets, infra, community projects), curated documentation links, and MCP servers (live tools such as Raven). Use when you need to know which SEP applies, or want a starting point for ecosystem integrations, official docs, or live MCP tooling.
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。