1,335 件 ・ 関連度順
概要と使いどころ
Pick the right lean-ctx tool for reading, searching, understanding and running code with fewer tokens and fewer round trips. Use when lean-ctx MCP tools (ctx_*) are available or when a command output, file read or search would be large.
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。
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
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。
Autonomous multi-project orchestration engine. Audits all registered projects, structures work into dependency-wired tasks, dispatches parallel agents (subagents or Agent Teams), audits completions, and ships PRs. Registry-driven — works for any user with a configured project registry.
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。
Direct-invocation specialist for self-contained HTML diagrams whose layout, notation, and interaction clarify relationships, sequence, topology, state, hierarchy, or quantitative structure. Use when the user explicitly invokes html-diagram or the broad html skill routes a diagram request here. Do not activate independently from a general request.
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。
Model and publish semantic definitions in Auto Ontology. Use for terms, relationships, measures, imports, and governed results—not deployment or querying.
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。
Run the PAIDF Orchestration Event Video Generation DAG on Kubernetes - image-to-video anomaly generation, auto-labeling, and anomaly dataset generation. Select for requests about event video generation, anomaly video generation, image-to-video synthesis, Cosmos3 image2video, anomaly dataset creation, safety/surveillance SDG, or generating person-falling, person-climbing, person-running, fighting, smoking/vaping, fire/smoke, or shoplifting video clips from a seed image. Runs environment setup first when controller readiness is unknown. Not for person-crop clothing/attribute augmentation (that is image-attribute-augmentation-workflow) and not for video style transfer.
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 this skill when the user is invoking `doca_telemetry_utils` on a host with DOCA installed — discovering the diagnostic-counter schema, translating counter names to binary Data IDs, validating per-device counter support before committing a DOCA Telemetry exporter config, or reverse-resolving a captured Data ID. Trigger even when the user does not explicitly mention "doca_telemetry_utils" or "Data ID" — typical implicit phrasings include "my exporter ships but the collector sees nothing", "this metric silently drops downstream", "which counters does this BlueField expose", "translate this 0x... back to a counter name", "what do node / pcie_index / depth mean here", or "is this counter supported on this device before I commit it". Refuse and route elsewhere for developer-side collector / exporter library programming, DTS deployment, or DOCA install / repair — those belong to doca-telemetry, doca-public-knowledge-map, and doca-setup.
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。
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
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。
Find connections between notes and update MOCs. Requires semantic judgment to identify genuine relationships. Use after /reduce creates notes, when exploring connections, or when a topic needs synthesis. Triggers on "/reflect", "/reflect [note]", "find connections", "update MOCs", "connect these notes".
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。
Onboard a new model generation or sibling into oh-my-hermes: probe router recognition, research the official contract, write trait-to-counter calibration, place routing in both lanes, price from documented list only, gate machine config on a served route, prove with the gates, close with a benchmark pair. Use when a model family ships a new generation (for example "Fable 5.1 is out", "GLM 5.4 shipped", "onboard new model", "add model to chains").
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。
[omh] Onboarding a newly released model generation: when a model family ships a new generation or changes its serving contract, walk the recognition, research, calibration, routing, and measurement process that keeps model handling honest and current. Use when the user says: model-optimization, model optimization, optimize for model, onboard new model, calibrate new model, new model calibration, model calibration.
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。
The reference presentation-tool contract, covering server-side enrichment, suggestion chips, the event stream, progressive rendering, both roles' built-in components, and adding a vertical component. Load when building or reviewing how a commerce agent's output reaches a frontend.
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。
Converts VitePress/GFM wiki markdown to Azure DevOps Wiki-compatible format. Generates a Node.js build script that transforms Mermaid syntax, strips front matter, fixes links, and outputs ADO-compatible copies to dist/ado-wiki/.
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。
Eufy camera integration — local RTSP streaming and event clips
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。
Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.
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。
Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools. Use when studying cell identity genes, cancer-associated regulatory elements, or master transcription factor binding regions that cluster into large enhancer domains.
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。
ChIP-seq peak calling using MACS3 (or MACS2). Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports input control, fragment size modeling, and various output formats including narrowPeak and broadPeak BED files. Use when calling peaks from ChIP-seq alignments.
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。
Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.
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。
ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks), cross-correlation analysis (NSC/RSC), library complexity, and IDR (Irreproducibility Discovery Rate) for replicate concordance. Use to assess experiment quality before downstream analysis. Use when assessing ChIP-seq data quality metrics.
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。
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
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。
De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.
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。
Runs a declarative strategy spec over OHLCV bars with the manifoldbt Rust engine, pairs the fill log into round trips, and emits the eight inputs the backtest-expert skill scores. Use when the user wants to execute a backtest, measure a rule they have described, obtain win rate / average win / average loss / max drawdown from real bars, or feed backtest-expert with measured numbers instead of estimates.
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 the user asks to "build our social posting calendar", "set weekly slots and queue depth per channel", or "plan the evergreen recycle rotation"; produces the always-on brand calendar — pillar allocation with hero/hub/help balance and give:ask targets (all labeled Estimated starting heuristics to calibrate against own analytics, never scored rules), per-channel recurring slots with queue depth, a batching workflow, an evergreen recycle cycle with freshness re-checks, deliberate open slots for realtime/trend moments fed by trend-spotter go/skip verdicts, and first-comment/publish-order ops — cadence commitments filed to the channel registry, and no batch ships to the queue without a SHIP verdict from social-quality-auditor pre-publish mode. Not for repurposing an existing asset or the paid-amplification calendar — use content-amplifier. 社媒排期/内容日历/栏目配比/常青循环复用
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 the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates), competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro (intended vs actual pull-through, evidence-labeled) — feeding the TALE L drift-audit sub-items and the narrative-whiplash guardrail fact base. Not for the first-time consistency check before a surface ships — use narrative-cascade-planner; not for computing the TALE profile result or running the vetoes — use narrative-quality-auditor; not for echo-rate / AI-answer resonance measurement — use narrative-resonance-monitor. 自漂移监测/竞品重定位告警/重定位触发/叙事漂移复盘
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。