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cccskills

「media」の検索結果

2,055 件 ・ 関連度順

概要と使いどころ

Plans and executes earned media — press strategy, journalist outreach, announcements, commentary, and crisis response. Use this to plan a press push, write a pitch or release, find and approach the right journalists, prepare for an interview, decide whether something is newsworthy, or respond to a story going badly.

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。

cbrock84/headcount2,0572026年9月18日 更新

Apply during planned rewrites and migrations with explicit phase boundaries. Converge on the target architecture; don't preserve smooth intermediate states with throwaway compatibility code.

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。

michael-denyer/pstack-claude2,0212026年10月14日 更新

Immediately trigger scheduled task without waiting for scheduled time. When you need to test task execution or run task ahead of schedule.

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。

openakita/openakita1,9982026年9月24日 更新

Load a newly created skill from skills/ directory to make it immediately 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。

openakita/openakita1,9982026年9月24日 更新

Baidu Intelligent Cloud marketing skill for generating marketing notes, videos, and optimized copy. Supports immediate and delayed call triggers. Use when user needs marketing content generation or campaign optimization.

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。

openakita/openakita1,9982026年9月24日 更新

Advanced Biopython modules for motifs, population genetics, sequence utilities, restriction analysis, clustering, and GenomeDiagram visualization; use when you need extended bioinformatics analysis beyond basic sequence I/O and alignment.

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。

aipoch/medical-research-skills1,9552026年9月17日 更新

Generates complete Mendelian randomization study designs from a user-provided exposure and outcome direction. Always use this skill whenever a user wants to design, plan, or build a Mendelian randomization study — even if phrased as "help me write a paper on X", "design an MR study for Y", or "I want to test whether A causally affects B using GWAS". Covers core two-sample MR design, optional bidirectional follow-up, optional multivariable MR, IV selection logic, ancestry alignment, harmonization, IVW as the default primary estimator, weighted median / MR-Egger / MR-PRESSO / leave-one-out sensitivity analyses, Steiger directionality, heterogeneity / pleiotropy checks, and explicit claim-boundary control. Always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, validation ladder, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references.

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。

aipoch/medical-research-skills1,9552026年9月17日 更新

Designs complete single-cell research plans from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a single-cell study — including disease-focused, mechanism-focused, biomarker-focused, translational, perturbation-inspired, or validation-aware projects. It should define the research question, choose the best-fit study pattern, recommend sample grouping logic, suggest reference datasets as examples only, specify the core analysis modules, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, sample metadata, accession numbers, cohort availability, cell-type labels, external validation resources, or literature references. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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。

aipoch/medical-research-skills1,9552026年9月17日 更新

Designs complete research plans that integrate clinical variables with multi-omics data from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related omics layers for mechanism interpretation, biomarker development, risk stratification, treatment-response analysis, or translational use. It should define the clinical use case, alignment across data layers, feature-reduction and fusion logic, modeling route, mechanism-interpretation layer, validation ladder, and four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, platform coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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。

aipoch/medical-research-skills1,9552026年9月17日 更新

Designs complete integrated research plans for bulk transcriptomics, proteomics, metabolomics, and related omics from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused, stratification-oriented, or translational projects. It should define the research question, choose the best-fit study pattern, recommend example datasets as reference candidates only, specify the core analysis modules and method choices, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, cohort availability, assay coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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。

aipoch/medical-research-skills1,9552026年9月17日 更新

Generates complete bidirectional multi-phenotype Mendelian randomization research designs from a user-provided exposure family and outcome family. Always use this skill whenever a user wants to design, plan, or build a genome-wide causal-inference study based on publicly available GWAS summary statistics, especially when the article logic includes multiple exposures, multiple outcomes or subtypes, bidirectional MR, IV filtering, IVW as the main estimator, weighted median / MR-Egger / MR-PRESSO sensitivity analyses, leave-one-out testing, heterogeneity / pleiotropy checks, and multiple-testing control with FDR. Covers five study patterns (single-family bidirectional MR, multi-phenotype screening MR, subtype-resolved MR, phenome-style bidirectional causal map, mechanism-prioritized MR follow-up) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version...

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。

aipoch/medical-research-skills1,9552026年9月17日 更新

Systematically maps mechanism evidence for a disease from molecules to pathways, cell types, tissues, biological consequences, and clinical phenotypes. Always use this skill when a user needs a layered mechanism evidence chain rather than a flat summary or immediate gap analysis. Formal literature citations must be real and verifiable.

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。

aipoch/medical-research-skills1,9552026年9月17日 更新

Use when filtering genes with high missingness and then imputing missing values in a bulk expression matrix with group-aware KNN through DMwR2, where donor samples are restricted by one annotation column before imputation. For strata with 10 or fewer samples, the script falls back to row-wise direct filling with mean or median. NOT for: single-cell data, multi-column stratification, non-tabular inputs, network access, or interactive 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。

aipoch/medical-research-skills1,9552026年9月17日 更新

Recommend suitable prompts from 10,000+ Nano Banana Pro image generation prompts based on user needs. Optimized for Nano Banana Pro (Gemini), but prompts also work with Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E, Flux, Stable Diffusion, and any text-to-image AI model. Use this skill when users want to: - Generate images with AI (any model — Nano Banana Pro, Gemini, GPT Image, Seedream, etc.) - Find proven AI image generation prompts and prompt templates - Get prompt recommendations for specific use cases (portraits, products, social media, posters, etc.) - Create illustrations for articles, videos, podcasts, or marketing content - Browse a curated prompt library with sample images - Translate and understand prompt techniques Also available: "ai-image-prompts" skill — a model-agnostic version of this library for universal image generation.

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。

YouMind-OpenLab/nano-banana-pro-prompts-recommend-skill1,8762026年10月14日 更新

Use this skill when writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percentile, stats_agg, time_weight, counter_agg, gauge_agg, heartbeat_agg, state_agg, candlestick_agg, hyperloglog, approx_count_distinct, min_n, max_n, mcv_agg, lttb, asap_smooth, rollup, two-step aggregation

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。

timescale/pg-aiguide1,8692026年10月15日 更新

Apply during planned rewrites and migrations with explicit phase boundaries. Converge on the target architecture; don't preserve smooth intermediate states with throwaway compatibility code.

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。

backnotprop/pstack1,8572026年10月14日 更新

Convert media files between formats - video containers, audio formats, and codec transcoding

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。

benchflow-ai/skillsbench1,8482026年10月13日 更新

Validate implementation quality through custom checklists, scoring against constitution standards, specification coverage, and producing remediation recommendations.

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。

a5c-ai/babysitter1,8462026年9月17日 更新

Multi-dimensional code review across correctness, security, performance, and maintainability with confidence-gated reporting and remediation loops.

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。

a5c-ai/babysitter1,8462026年9月17日 更新

Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

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。

Prat011/awesome-llm-skills1,7992026年7月14日 更新

Clause-by-clause contract risk analysis with severity scoring, financial exposure estimates, and prioritized remediation guidance

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。

zubair-trabzada/ai-legal-claude1,7852026年3月27日 更新

Map identified threats to appropriate security controls and mitigations. Use when prioritizing security investments, creating remediation plans, or validating control effectiveness.

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。

rmyndharis/antigravity-skills1,7632026年10月1日 更新

You are a security expert specializing in dependency vulnerability analysis, SBOM generation, and supply chain security. Scan project dependencies across ecosystems to identify vulnerabilities, assess risks, and recommend remediation.

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。

rmyndharis/antigravity-skills1,7632026年10月1日 更新

Expert SRE incident responder specializing in rapid problem resolution, modern observability, and comprehensive incident management. Masters incident command, blameless post-mortems, error budget management, and system reliability patterns. Handles critical outages, communication strategies, and continuous improvement. Use IMMEDIATELY for production incidents or SRE practices.

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。

rmyndharis/antigravity-skills1,7632026年10月1日 更新