「recommendation」の検索結果

Agent C3 - Mixed Methods Design Consultant Comprehensive mixed methods research design specialist covering sequential, concurrent, embedded, and multiphase designs with Morse notation. Core Capabilities: - Sequential Explanatory (QUAN → qual): Explain quantitative results - Sequential Exploratory (QUAL → quan): Develop instruments - Convergent Parallel (QUAN + QUAL): Comprehensive understanding - Embedded (QUAN(qual)): Secondary strand addresses different question - Multiphase: Long-term projects with iterative phases - Morse notation interpretation and recommendation

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Research Guardian - Ethics Advisory & Bias Detection across all research stages Enhanced VS 3-Phase process: Surface-level screening, deep contextual analysis, constructive recommendations Use when: reviewing research ethics, checking for bias, assessing trustworthiness, QRP screening Triggers: ethics review, IRB, bias detection, QRP, trustworthiness, research integrity, p-hacking, HARKing

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VS-Enhanced Journal Matcher with Journal Intelligence MCP — Real-time journal data pipeline with checkpoint-based human decisions. Uses OpenAlex + Crossref APIs for live metrics. Light VS applied: Avoids IF-centric recommendations + multi-dimensional matching strategy Use when: selecting target journals, planning submissions, comparing publication options Triggers: journal, submission, impact factor, academic journal, publication, submit

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Create concise policy briefs that translate research into actionable recommendations for policymakers and stakeholders.

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Structured policy briefing note (1-2 pages). Issue, background, analysis, options, recommendation. UK GES, Australian Treasury, consulting formats. Auto-populates from econstack data skills.

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Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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Multi-channel budget optimization using MER, marginal ROAS, and diminishing returns analysis. Use when pasting multi-channel spend and results data, requesting reallocation recommendations, analyzing budget shift priorities, or optimizing marketing efficiency across Google, Meta, TikTok, and other channels. Platform: Google and Meta.

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Analyzes frequency data across your Meta campaigns, identifies where you're overserving ads to the same people, and recommends frequency caps by campaign objective. Tells you the point where additional impressions stop driving conversions and start burning money. Platform: Meta.

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Inspects Observal traces, sessions, rankings, feedback, telemetry health, logs, and Agent insight reports. Use when the user wants operational evidence, current activity, telemetry diagnosis, ratings, report generation, regression analysis, or recommendations grounded in Agent usage.

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Build sourced JSON dossiers on people, companies, or topics with DeepAPI. Use for profiles, prospects, vendor due diligence, or customer research across sources; use deep-research for recommendations.

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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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Use this skill to triage ONE microsoft/apm issue already selected by autopilot-issue-triage-scheduler. Return one advisory recommendation and a proposed scope brief, never human approval. Do not implement the issue, manage the backlog, or list the queue.

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Drive ONE already selected open pull request in microsoft/apm to mergeable. Classifies copilot-pull-request-reviewer[bot] inline review, runs autopilot-pr-review-worker, folds (by default) every recommendation inside the PR's stated scope, pushes to the head branch or a superseding PR that preserves authorship via commit trailers, watches CI to green (`agent-merge` when that skill or `<agent_merge_state>` is present; otherwise `gh pr checks --watch`), and iterates under fixed caps until ready-to-merge, advisory-with-deferred, superseded, or blocked. Plan before any fold, push, or reviewer request. Never spawned by autopilot-pr-review-scheduler. That scheduler is advisory only. Invoke this skill by name when the caller asked to drive a PR to merge.

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Use this skill to triage ONE microsoft/apm pull request already selected by autopilot-pr-triage-scheduler. Covers community PRs that fix an issue and PRs opened without an issue. Return one advisory recommendation, never human approval, never a diff review, never merge. Not a queue manager and not autopilot-pr-review-worker.

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Analyze a project's past Codex sessions, memory files, and existing local skills to recommend the highest-value skills to create or update. Use when a user asks what skills a project needs, wants skill ideas grounded in real project history, wants an audit of current project-local skills, or wants recommendations for updating stale or incomplete skills instead of creating duplicates.

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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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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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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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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 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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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.

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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.

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。