「recommendation」の検索結果

Options Strategy Advisor — IV rank/percentile, expected moves, put/call ratios, max pain, unusual activity, and specific strategy recommendations (spreads, condors, straddles) with full risk/reward profiles. Triggered by "trade options <TICKER> [bullish/bearish/neutral]".

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Pre-Earnings Analysis — earnings date, analyst consensus estimates, 8-quarter historical surprise pattern, post-earnings price move history, options-implied expected move vs historical, key metrics to watch, and trade setup recommendations. Triggered by "trade earnings <TICKER>".

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。

Batch Resume Screening — score and rank candidates 0-100 against job requirements, flag red flags (job hopping, gaps, skill mismatches), output Pass/Phone Screen/Skip recommendation per candidate

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。

Head-to-Head Candidate Comparison — two candidates side-by-side across skills, experience, culture fit, salary expectations, references, hire-or-no-hire recommendation

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Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.

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Use this when setting up Nix for a development project (devShell + package build) and you care about `nix develop` being fast. Covers the zero-inputs flake.nix + npins + default.nix/shell.nix layout, sub-flakes for non-user-facing Nix, and language-specific recommendations.

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Use when prioritising CVEs, the user asks "should we patch X first?" / "is CVE-YYYY-NNNNN being exploited?", or wants weaponisation, EPSS, and KEV context combined into a patch-now-vs-later recommendation.

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。

Standardized output formats for research, planning, implementation, and review agents. Use when generating agent outputs or parsing agent responses.

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。

Generate a detailed Buy/Sell/Hold equity research report for Indian stock market (NSE/BSE listed companies). Use this skill whenever the user asks for a stock analysis, equity research, investment recommendation, target price, fundamental analysis, technical analysis, or buy/sell/hold opinion on any Indian company or stock. Triggers on phrases like "analyse [stock]", "should I buy [stock]", "research report for [company]", "target price for [stock]", "is [stock] a good buy", "NSE/BSE stock analysis", etc. Always use this skill for any Indian equity research request — even if the user just names a stock ticker or company and asks "what do you think?"

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Review a pull request or contribution deeply, explain it tutorial-style for a maintainer, and produce a polished report artifact such as HTML or Markdown. Use when asked to analyze a PR, explain a contributor's design decisions, compare it with similar systems, or prepare a merge recommendation.

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Use when a Research Pilot project needs a read-only recommendation for the next workflow action.

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。

Draft blog posts, social media, email newsletters, landing pages, press releases, and case studies with channel-specific formatting and SEO recommendations. Use when writing any marketing content, when you need headline or subject line options, or when adapting a message for a specific platform, audience, and brand voice.

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。

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

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Calculate and interpret Earned Value Management (EVM) metrics per PMBOK 8 Measurement Performance Domain. Compute SPI, CPI, EAC, ETC, TCPI, and generate variance narratives with corrective action recommendations. Use during project execution to assess schedule and cost performance.

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Build structured vendor/tool evaluation matrices with weighted scoring, TCO analysis, and recommendation memos. Covers criteria definition, deal-breaker identification, and negotiation points. Use when selecting SaaS tools, evaluating technology vendors, or making build-vs-buy decisions.

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Analyze survey and feedback data with statistical distributions, cross-tabulations, significance testing, and open-ended theme extraction. Produces executive-ready survey reports with segment comparisons and action recommendations. Use when processing NPS, CSAT, employee engagement, or market research surveys.

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Write one-page executive briefs and decision memos that turn messy inputs into a concise recommendation, options, risks, decision ask, and next steps. Use when leaders need a fast read before approving budget, policy, vendor, headcount, or strategy changes. Do not use for long-form reports, slide design, or generic summarization without a decision.

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。

Qdrant vector database -- collection management, point operations, payload filtering, named vectors, quantization, recommendations, snapshots

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Ask Daniel a blocking decision as options with a recommendation and honest pros/cons, rather than prose. Use whenever work is blocked on a judgment call, a product decision, an approval, or a choice between approaches — instead of writing "let me know which you prefer" at the end of a message.

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Generates a comprehensive milestone progress review including feature completeness, quality metrics, risk assessment, and go/no-go recommendation. Use at milestone checkpoints or when evaluating readiness for a milestone deadline.

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。

Structured performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.

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。

Security Operations Center skill for alert triage, detection engineering, incident response, log analysis, threat hunting, SIEM queries, EDR investigation, timeline building, IOC handling, escalation notes, containment recommendations, and analyst-ready reporting. Use for blue-team operations, suspicious event analysis, and defensive cybersecurity workflows.

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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 a task needs the judgment of a museum or gallery curator — evaluating whether to accept a donated collection or object, writing an acquisition or deaccession recommendation, resolving a provenance or repatriation question, deciding a loan request, or drafting exhibition label copy.

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。