「scoring」の検索結果

Comprehensive B2B demand generation and lead acquisition skill. Use when building lead generation systems, creating demand acquisition campaigns, optimizing CAC, designing lead magnets, conducting lead research, or planning multi-channel demand strategies. Covers ICP definition, funnel strategy, channel optimization, lead scoring, attribution, and pipeline generation for B2B SaaS.

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Comprehensive sales and revenue operations skill. Use when building a sales team, doing founder-led sales, hiring first sales reps, navigating enterprise deals, implementing product-led sales, designing sales compensation plans, defining ICP, mapping buyer personas, or optimizing the revenue engine (RevOps). Activates for: sales strategy, rev ops, revenue operations, sales enablement, sales compensation, ICP, ideal customer profile, buyer persona, sales process, deal execution, lead scoring, lead routing, lead lifecycle, MQL, SQL, pipeline management, CRM automation, sales qualification, BANT, MEDDIC, founder sales, enterprise sales, product-led sales, startup sales, SDR, AE, quota, ramp, commission plan.

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Comprehensive customer success and retention expertise combining onboarding, health scoring, churn prevention, retention strategies, and LTV maximization. Use when designing customer onboarding flows, reducing churn, building health scores, creating retention systems, designing upsells, or improving customer lifetime value. Covers proactive engagement, cancel flows, dunning, re-engagement, and the full customer lifecycle.

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Spawns the decision-scorer agent after architect proposes 2+ variants in an ADR. Produces a weighted scoring table and recommended choice saved to docs/decisions/.

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Prospect discovery, enrichment, scoring, and reporting against an Ideal Customer Profile

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Quantifying priority by scoring competing items with ICE/RICE/WSJF/MoSCoW/Cost of Delay/Kano. No code. Use to prioritize features/bugs/initiatives or arbitrate Must vs Should at MVP scoping.

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Navigating delivery status read-only: reconciles planned scope (specs/roadmap/PRD) against implemented code for what's built vs left. Not for priority scoring (Rank) or AC conformance (Attest).

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Enumerating failure modes via pre-mortem analysis. Systematically identifies failure scenarios for plans, designs, and features, scoring them with RPN/AP. Does not write code.

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Detect image copyright infringement risks by comparing against a database of registered copyrighted works with similarity scoring, TRO litigation history, and radar-based infringement assessment. Triggered when users mention copyright detection, image infringement check, copyright risk analysis, TRO risk, copyright similarity search, rights owner lookup, or copyright compliance verification. Even if the user does not explicitly say "copyright," this skill should be triggered whenever the need involves checking whether an image may infringe registered copyright works.

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When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see emails. For pricing decisions, see pricing.

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Socratic spec refinement — clarify WHAT a phase delivers with ambiguity scoring before discuss-phase. Produces a SPEC.md with falsifiable requirements locked before implementation decisions begin.

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Audits and optimizes websites for search engine visibility (SEO) and AI search citation (GEO), covering technical health, E-E-A-T content scoring, domain authority, structured data, rich results, and entity signals. Use when running SEO audits, diagnosing traffic drops or ranking losses, generating Schema.org JSON-LD, checking Core Web Vitals, crawlability, robots.txt, sitemaps, hreflang, backlinks, planning content strategy or site migrations, fixing indexing issues, or optimizing for AI Overviews, ChatGPT, and Perplexity. NOT for paid ads (PPC/SEM), social media strategy, email marketing, or general web development unrelated to search.

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amazon-listing-doctor是由buluslan（公众号：新西楼.AI）研发的亚马逊Listing质检Skill，他会基于 CDQ算法 / A9 / COSMO / Alexa 四大底座知识，来给Listing进行全身体检和打分，输出一份诊断报告，帮你揪出"流量加倍、弯道超车"的机会点。 更多跨境电商 AI 实战内容，请关注公众号「新西楼.AI」。 Audits Amazon listing health across CDQ / A9 indexability / COSMO intent-coverage / Alexa discoverability, plus 2026 compliance rules (title 75-char, highlights 125-char, image defects). Pure diagnosis, zero dependencies, works offline. Trigger whenever the user mentions checking/auditing/scoring/diagnosing an Amazon listing's quality — in any language; 中文卖家说 listing 体检 / 质检 / 打分 / 诊断 / 健康度 / 质量分 时同样适用。

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Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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Reference for the ripdpi-dns-resolver crate: DoH/DoT/DNSCrypt/DoQ/ODoH, health scoring, bootstrap IPs, tamper diagnostics. Use when touching encrypted DNS resolution, failover, or tamper detection.

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Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".

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Conduct Fundamental Rights Impact Assessments (FRIA) for high-risk AI systems as required by EU AI Act Article 27. Use when (1) deployers of high-risk AI systems (Annex III) need to assess fundamental rights impacts before first use, (2) public bodies or private entities providing public services are deploying AI systems, (3) credit scoring or insurance risk assessment AI systems are being deployed (Annex III 5b, 5c), (4) evaluating impact on natural persons' fundamental rights (dignity, liberty, privacy, data protection, non-discrimination, children's rights, etc.), or (5) creating EU AI Act Article 27 compliance documentation with market surveillance notification.

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Verifies provider credentials via NPI MCP, searches Medicare coverage policies via CMS Coverage MCP, and maps clinical evidence to policy criteria with MET/NOT_MET/INSUFFICIENT assessment, per-criterion confidence scoring, and documentation gap analysis.

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Customer Success engineer — onboarding optimization, health scoring, expansion revenue, churn prevention, and NRR growth. Use when asked to "improve onboarding", "score account health", "prevent churn", or "grow NRR".

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Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.

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用户想理解X推荐机制/为什么帖子被推荐或不被推荐/小号能不能被推荐时使用。触发词: For You算法, 推荐机制, OON, 为什么没流量, 小号能起吗, algorithm, recommendation, why no impressions。不用于: 纯写内容不问机制/心态焦虑"帖子没爆怎么办"(那是longtail-strategy)/怎么互动获曝光(那是comment-engagement)。

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Format and score completed plugin assessment findings without starting another assessment workflow. Use when a plugin assessment needs the standard report structure, scoring criteria, severity breakdown, or marketplace-readiness result.

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Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.

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Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies. Covers single-stock diagnostics and batch screening for both Graham cigar-butt and Buffett quality-compounder criteria. Runs cross-statement reconciliation before scoring. Data from Longbridge CLI first, MCP fallback, WebSearch only for genuine gaps. Triggers: "格雷厄姆", "巴菲特", "捡烟蒂", "烟蒂股", "NCAV", "净流动资产", "护城河", "价值投资", "安全边际", "深度价值", "撿煙蒂", "煙蒂股", "淨流動資產", "護城河", "安全邊際", "Graham", "Buffett", "cigar butt", "net-net", "NCAV screen", "moat", "value investing", "margin of safety", "deep value", "quality compounder", "價值投資", "深度價值", "防御型投资者", "防禦型投資者"

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