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概要と使いどころ
Pre-sales-call intelligence composite. Deep dives on the person AND their company before a sales call, then maps findings to your product to generate talking points, objection prep, and a call strategy. Goes beyond generic attendee research — this is sales-specific intelligence that helps SDRs, AEs, and founders walk into calls with a plan. Tool-agnostic — works with any CRM, research source, and calendar.
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End-to-end Google Search Ads campaign builder. Performs deep keyword research (competitor SEO, review language mining, Reddit/HN community terminology, site audit), builds keyword architecture with funnel mapping and intent classification, creates ad group structure, generates headline/description variants, builds negative keyword lists, recommends bid strategy, and exports a campaign-ready CSV for Google Ads Editor import.
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Scrape competitor ads from Meta, TikTok, Google, and LinkedIn ad libraries, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad landscape, find new creative directions, or identify weaknesses in a competitor's paid strategy.
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。
Research competitors - products, pricing, team, funding, and strategy
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。
Front door and loop owner of a Meta (Facebook/Instagram) ad harness. Reads the brand's ads/ docs, works out which campaign the user means and which stage it is at, hands off to the stage skill that runs it (intake, strategy, creative, launch, checks, fixes, answers), and writes every stage change back to the docs. Use it when someone wants to run ads on Meta, comes back to ads they started earlier, a stage skill hands back, or a scheduled check fires.
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。
Competitor intelligence system. Research competitors across web, Reddit, Twitter/X, LinkedIn, and blogs. Build deep competitor profiles, monitor content and positioning changes, track what gets traction, and identify competitive gaps. Covers data collection, content tracking, and strategy analysis. Pure research skill — uses web search, web fetch, and optionally Apify for social scraping. No scripts required.
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Decides how to preprocess plasma cfDNA sequencing data so the recoverable signal survives - library-prep-aware fragment expectations (dsDNA vs ssDNA/adaptase prep), UMI/duplex consensus with fgbio (ExtractUmisFromBam, GroupReadsByUmi --strategy paired for duplex, CallMolecularConsensusReads vs CallDuplexConsensusReads, FilterConsensusReads min-reads "total s1 s2"), the align->group->consensus->RE-align ordering, and the cfDNA dedup trap where naive coordinate dedup collapses nucleosome-coincident independent molecules. Covers when single-strand consensus suffices vs when duplex is mandatory, the singleton/sensitivity tax at low input, and reading the insert-size histogram as a pre-analytical QC instrument. Use when processing plasma cfDNA reads before fragmentomics, ctDNA mutation calling, or tumor-fraction estimation.
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Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.
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Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.
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Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.
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Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.
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Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.
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Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.
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Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.
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Design, validate, and plan a startup from scratch. Covers market research, competitive analysis, business model, brand identity, product definition, financial projections, and validation experiments. Trigger when the user has a startup idea to explore, wants to validate a business concept, needs a business plan or lean canvas, asks for market sizing or competitive landscape, wants brand positioning or go-to-market strategy, or says anything like "I have an idea for..." or "is this idea worth pursuing". Also handles resuming from a previous checkpoint.
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。
Deep competitive intelligence for any market. Analyzes competitors' products, pricing, customer sentiment, GTM strategy, and growth signals using real web data. Produces battle cards, pricing landscape, and feature matrix. Use when the user wants to understand their competitive landscape, analyze competitors, compare products in a market, or research who they're competing against. Triggers for "who are my competitors", "competitive analysis", "competitor research", "battle cards", "pricing comparison", "competitor pricing", "market players", "competitive intelligence", "competitive landscape", "who else is in this space", "competitive moat", or any request to profile, compare, or map competitors in a category. Works standalone — no prior startup-design session needed.
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。
Search runtime and scene management: verify queries, inspect scenes, debug app readiness, and diagnose recall or scene-config issues.
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。
Viking Search tuning for specified policy directions. Use this when the user provides specific queries, a type of query, or a business policy direction, and asks to boost, suppress, or fix a class of search results through request-parameter passthrough. You must only perform read-only baseline evaluation and request-level candidate testing; do not modify search scenes, app config, dictionaries, recommend scenes, or primary recall 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。
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
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。
eBay product differentiation strategy tool. Analyze competitor weaknesses, extract pain points from negative feedback, identify unique selling points, and generate actionable differentiation strategies. Includes eBay-specific seller feedback analysis. No API key required.
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Amazon product differentiation strategy tool. Analyze competitor weaknesses, extract pain points from negative reviews, identify unique selling points from positive reviews, and generate actionable differentiation strategies. Progressive L1-L4 analysis depth. No API key required.
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Shopify/DTC product differentiation strategy tool. Analyze competitor stores, extract pain points from reviews, identify brand positioning opportunities, and generate actionable differentiation strategies. Includes DTC-specific metrics like CAC, LTV, and brand story analysis. No API key required.
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TikTok Shop product differentiation strategy tool. Analyze viral product trends, competitor affiliate strategies, content performance, and identify differentiation opportunities. Includes TikTok-specific metrics like engagement rate, viral potential, and creator collaboration insights. No API key required.
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Amazon profit margin calculator for sellers. Calculate cost breakdowns, profit margins, break-even points, and get pricing recommendations. Supports single product analysis and batch calculations. Input product cost, shipping, FBA fees, and get instant profitability analysis. No API key required. Use when: (1) evaluating new product profitability before sourcing, (2) diagnosing existing product margins, (3) adjusting pricing strategy, (4) analyzing multiple SKUs at once.
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。