「recommend」の検索結果

Use this skill to run a four-panel adversarial advisory review on any pull request that touches the OpenAPM specification artifact (docs/src/content/docs/specs/openapm-*.md), its inline / sidecar JSON Schemas (docs/src/content/docs/specs/schemas/*.schema.json), or the conformance fixture seed (tests/fixtures/spec-conformance/**). The panel fans out to four spec-ecosystem reviewers (swagger-openapi-editor, oci-distribution-editor, pkgmgr-registry-contract-editor, w3c-tag-architect), each running in its own agent thread, and a spec-editor synthesizer that produces a fold-now / defer-v0.1.1 / defer-v0.2 / reject list plus a ship decision keyed off a 1..10 shocked_meter scale. The orchestrator is the sole writer to the PR: ONE consolidated comment, no verdict labels, no merge gating. The panel is advisory -- it surfaces findings, prioritizes folds, and renders a ship recommendation that the maintainer weighs.

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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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Assess a JS SDK release candidate or release plan against the previous release and recommend ship or block.

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调查研究：在下判断、做决策、给建议之前，先用第一手材料弄清现状，产出「事实 / 推断 / 未知」三栏调查结论。当上下文不完整、证据薄弱、领域陌生、用户说"先别动手"、或你发现自己在凭印象作答时触发；已充分调查后的执行阶段不要重复触发。 English: Investigate before judging. Gather firsthand facts and produce a fact / inference / unknown ledger before any recommendation. Trigger when context is incomplete, evidence is thin, the domain is unfamiliar, the user says "look first", or you notice yourself answering from impression; skip when investigation is already done and the task is pure execution.

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Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

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This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

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Generate comprehensive market research reports and industry/competitive analysis in the style of top consulting firms. Use for market sizing, competitive landscape, market entry, investment thesis, strategic recommendations, and consulting-style business reports. Do not use for scientific reports, paper writing, LaTeX submission/PDF builds, EDGAR/FRED/Treasury/Data Commons data retrieval, or biomedical literature evidence work.

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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Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.

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Use when the user wants a social listening report about what people are saying about a brand, person, product, topic, category, or niche across public social platforms. Produces cited themes, sentiment caveats, notable posts, and recommended actions.

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Gather phase context through adaptive questioning before planning. Use --all to skip area selection and discuss all gray areas interactively. Use --auto to skip interactive questions (Claude picks recommended defaults). Use --chain for interactive discuss followed by automatic plan+execute. Use --power for bulk question generation into a file-based UI (answer at your own pace).

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Convene an AI executive board of directors (CEO, CFO, COO, CLO, CISO sub-agent personas) to vet a business idea, product concept, new service offering, M&A target, or operational initiative — and deliver an integrated board memo with a Go/No-Go recommendation. Use this skill whenever the user wants an idea vetted, stress-tested, or reviewed from multiple executive perspectives; asks to "present this to the board," "run this by the boardroom," "vet this idea," "poke holes in this plan," or "prep me for a board meeting"; or shares a business plan, pitch, proposal, or initiative document and asks for structured executive feedback. Also trigger when the user asks for a Go/No-Go decision, risk review across finance/legal/security/operations, or preparation for presenting an initiative to real leadership.

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Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.

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。

Persistent task management and productivity intelligence via MCP. Captures tasks from conversation, plans your day, tracks patterns, and gives personalized recommendations that improve over time.

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Guides Holoscan SDK installation: inspects the host, assesses platform compatibility, recommends an install method, and delegates to the matching install skill.

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Use this skill when the user is invoking doca_flow_dpa_perf on DPA-capable hardware (ConnectX-7 minimum supported, ConnectX-8 recommended, or BlueField-3) to measure rule update / disable rates on the DPA-offloaded DOCA Flow path — picking the active / passive device split, choosing workload-shape axes (burst, queue, completion threshold, workers, hash pipe algo, PSL tables), or reading Kops/sec iteration stats and the optional self-test. Trigger even when the user does not explicitly mention "doca_flow_dpa_perf" or "DPA Provider" — typical implicit phrasings include "how fast can the DPA program path-selector entries", "baseline rule-update rate on ConnectX-8", "tool reports zero ops on my BlueField", "self-test sentinel never shows on tcpdump", or "is my BlueField-2 DPA-capable". Refuse and route elsewhere for the host / DPU-CPU Flow path (doca-flow-perf), Flow pipeline tuning (doca-flow-tune), writing doca-flow / doca-dpa applications, or DOCA install — those belong to other skills.

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Use this skill whenever the agent is about to recommend or apply a change that touches DPU / NIC hardware state on a live system — mlxconfig firmware-parameter write, NIC firmware burn, BFB reflash, NIC ↔ DPU mode flip, SR-IOV or device-emulation slot enable, kernel boot-parameter change (IOMMU, hugepages, VFIO), PCIe rebind / rescan / link-state flip, or BlueField cold reboot. Wraps the change in pre-flight inventory, OOB reachability, a maintenance window, the mlxconfig cold-power-cycle rule, replica rehearsal, and rollback. Trigger even when the user does not say "hardware safety" — implicit phrasings: "flip BlueField mode over SSH", "enable SR-IOV and reboot", "burned firmware but mlxconfig shows old value", "reflashed BFB and lost representors", "reflash during business hours", "vendor says this is one-way". Refuse for general DOCA orientation (doca-public-knowledge-map), install or env debug (doca-setup), and program-side debug (doca-debug, doca-programming-guide) — those belong to other skills.

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Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog search, UniRef30/ColabFold env searches, A3M or FASTA alignments, paired MSA search for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment. For local deployment, download the databases in parallel with aria2c and launch via NIM_MODEL_NAME (the recommended default fast path, ~14 min vs over 80 min for the built-in downloader); a plain docker run uses the slow built-in downloader.

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Draft CCB workflow task packets, readiness recommendations, and candidate clarification questions without mutating authoritative state.

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Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals. Recommends one specific action with rationale. Triggers on "/next", "what should I do", "what's next".

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Audit openai-go during scheduled or repeated maintainer runs for correctness bugs, security weaknesses, performance problems, rough edges, non-idiomatic Go, test gaps, code organization, and architecture, then report one prioritized recommendation with evidence. Use for recurring proactive repository maintenance and rotating whole-codebase review; do not use for feature work, code changes, autonomous publishing, or pull-request stewardship.

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[omh] Team or business decision between options: tradeoffs, a recommendation, and a decision note you can act on. Use when the user says: strategy-brief, strategy brief, strategy memo, product strategy, strategic options, decision note, leadership strategy, next strategy.

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Create text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot.

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Price changes within the store's guardrails, rate recommendations from demand, occupancy, or sell-through pace, promotions with their scope, depth, and dates, the markdown option for stock or capacity that is not selling, and the margin preview before any of it is staged. Not needed for explaining why revenue moved (performance-insights) or writing promotional copy (marketing-campaigns).

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。