132 件 ・ 関連度順
概要と使いどころ
Plan vault restructuring from config changes. Compares config.yaml against derivation.md, identifies dimension shifts, shows restructuring plan, executes on approval. Triggers on "/refactor", "restructure vault".
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。
Analyze how a private SaaS company's ARR valuation multiple changed across funding rounds, and attribute the compression or expansion to rate cycles and macro selloffs, growth deceleration, narrative shifts (including an AI premium), competition, and investor demand, benchmarked against private-market medians and peers. Use this skill whenever the user asks about valuation compression, ARR multiples, round-to-round valuation or multiple changes, down rounds, or wants to compare a VC-backed software company's funding rounds. Research the rounds rather than answering from memory.
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。
Analyzes isoform switching events and functional consequences using IsoformSwitchAnalyzeR. Predicts protein domain changes, NMD sensitivity, ORF alterations, and coding potential shifts between conditions. Use when investigating how splicing changes affect protein function.
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。
Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation to identify regime shifts between Concentration, Broadening, Contraction, Inflationary, and Transitional states. Run when user asks about macro regime, market regime change, structural rotation, or long-term market positioning.
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。
Optimize Core Web Vitals (LCP, INP, CLS) for better page experience using field and lab evidence. Use when asked to "improve Core Web Vitals", "fix LCP", "reduce CLS", "optimize INP", "page experience optimization", or "fix layout shifts".
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。
When the user wants to track App Store chart rank changes, find top gainers and losers, detect breakout apps entering the top 100, or identify apps dropping out of charts. Also use when the user mentions "chart movers", "rank changes", "who's rising", "who's falling", "new chart entries", "top gainers", or "market shifts". For broader market overview, see market-pulse. For competitive keyword analysis, see competitor-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。
When the user wants to monitor competitor apps on an ongoing basis — tracking metadata changes, keyword shifts, screenshot updates, rating trends, or new features. Use when the user mentions "competitor monitoring", "track competitors", "competitor alert", "competitor changed their title", "watch a competitor app", "competitor weekly report", "competitive intelligence", or "what changed in competitor's listing". For a one-time deep competitive analysis, see competitor-analysis. For market-wide chart movements, see market-movers.
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。
The period feel comes from camera defects and one tiny everyday incident. Write the camcorder's autofocus hunting, exposure shifts and handheld shake as an explicit list, keep the story small, and the clip reads as a real old tape. Use when the user wants a Seedance 2.5 / 2.0 video prompt for this kind of clip (Early-2000s DV home video; 中文触发词: 早年 DV 家庭录像, DV, 家庭录像, VHS, 复古, 年代感), or asks how published cases of this type were prompted. Read references/cases.md before drafting.
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。
Use when evaluating macro dislocations, reflexive feedback loops, policy shifts, positioning squeezes, and asymmetric bets in a George Soros style.
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。
Model OR-Tools paired pickup-and-delivery routing problems, including PDPTW, dial-a-ride, paratransit, patient transport, and courier jobs with same-vehicle pairing, precedence, capacity, optional connected service, rider time windows, and vehicle shifts.
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。
**IMPORTANT**: Any change to React or Next.js code must read through this skill first. React and Next.js guidelines from Vercel Engineering covering visual instability, layout shifts, CLS, flickering, hydration issues, and font loading.
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。
Help users evaluate new opportunities, manage professional identity shifts, and transition into roles that maximize both impact and personal fulfillment.
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。
Subscribe to and scan industry newsletters for buying signals, competitor mentions, ICP pain-point language, and market shifts. Parses incoming newsletter emails via AgentMail, matches against keyword campaigns, and delivers a weekly digest of actionable signals. Use when a marketing team wants to turn newsletter subscriptions into an ongoing intelligence feed without manual reading.
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。
Monitor competitor pricing pages via live web scrape and Web Archive snapshots. Track plan changes, tier restructuring, new pricing models, and feature gating shifts. Produces a pricing comparison matrix and flags when a competitor changes packaging. Use when a product marketing team needs to stay current on competitive pricing or when preparing for a pricing change of their own.
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。
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).
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。
Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.
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。
Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.
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。
Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.
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。
Calls ChIP-seq peaks with MACS3, MACS2, HOMER, or SPP across narrow (TF) and broad (histone) modes. Handles input control matching, fragment-size modeling vs --nomodel, effective genome size, ENCODE-style IDR vs naive overlap, hyper-ChIPable artifacts, and aligner-specific shifts. Use when calling peaks from ChIP-seq alignments, choosing between narrow vs broad mode for a histone mark, deciding model vs nomodel for low-depth data, applying ENCODE pseudoreplicate IDR, or reconciling MACS vs HOMER vs SPP 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。
Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.
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。
Analyzes differential transcript usage (DTU) and isoform switches with functional consequence prediction (NMD via 50nt rule, ORF disruption, protein domain loss/gain, signal peptide changes, IDR alterations, coding-potential shifts). Tools include IsoformSwitchAnalyzeR v2 (auto-selects satuRn for >5 reps else DEXSeq), the manual DRIMSeq -> DEXSeq/satuRn -> stageR DTU pipeline, and fishpond/swish for inferential-uncertainty-aware DTE. Distinguishes DTU from DGE and DTE; integrates external annotators (CPC2, Pfam, SignalP, IUPred2A or DeepTMHMM). Use when investigating how splicing differences alter protein function or trigger NMD-mediated degradation.
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。
Compile a month-over-month retrospective from Agent Monitor data — sessions, cost, token volumes, completion rate, top projects by working directory, and notable shifts versus the prior month. Uses daily_sessions/daily_events (365d) from analytics, the session list, and the pricing cost breakdown. Use when doing a monthly retrospective or planning the month ahead.
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。
Keeps the engagement record for client work. Use when they name a client or stakeholder. Use when they debrief a meeting or paste notes. Use when they ask what was agreed. Use when they run a POC, change the client's codebase, prove it on their staging, go live, or need evals before a model acts. Use when they prep a readout, when trust shifts, or they say @fde. Route and run the local fde CLI (or npx --yes fdeops). Never ask them to type commands. Not for ordinary code edits in an unbound repo.
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。
Optimize content and site structure for AI-driven search experiences including AI overviews, large language model citations, generative answer engines, and AI assistants. Use this skill whenever the user wants to optimize for AI search, get cited by language models, appear in AI overviews, build llms.txt, structure content for AI extraction, or future-proof their SEO for the shift from blue links to AI answers. Triggers on AEO, GEO, AI search, AI SEO, AI overview, generative search, LLM optimization, llms.txt, AI citation, ChatGPT search, Perplexity, Gemini, Claude search, AI assistant optimization, answer engine. Also triggers when the user expresses concern about AI eating their organic traffic or wants to understand how to remain visible as search shifts.
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。