4,102 件 ・ 関連度順
概要と使いどころ
Expo managed workflow expert for EAS Build, Submit, Update, config plugins, and custom dev clients. Activate on: Expo project, EAS Build, EAS Update, config plugin, expo-dev-client, Expo Router, Expo SDK 52, app.json configuration. NOT for: bare React Native (use react-native-architect), native module authoring (use react-native-architect), Flutter (use flutter-bloc-state-manager).
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。
Audits git repository hygiene: worktree state, stale branches, uncommitted changes, orphaned worktrees, and hook configuration. Finds the rot before it causes a crisis.
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。
Structure persuasive speeches and formal arguments in forensic, legal, policy, and competitive debate styles. Use when the user needs to prepare talks, opening or closing arguments, policy pitches, public explanations, debate speeches, or mentions 'forensic speech', 'formal argument', 'persuasive speech structure', 'debate speech', 'opening statement', 'closing argument'. Turns structured claims into audience-tuned persuasive delivery.
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。
Implementing distributed tracing with W3C Trace Context — the byte-precise `traceparent` and `tracestate` header formats, OpenTelemetry's W3CTraceContextPropagator as the modern default (replacing X-B3-* and X-Datadog-*), head vs tail sampling, and HTTP→SQL trace propagation via sqlcommenter. Grounded in the W3C REC and OpenTelemetry specs.
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。
State-of-the-art data visualization for React/Next.js/TypeScript with Tailwind CSS. Creates compelling, tested, and accessible visualizations following Tufte principles and NYT Graphics standards. Activate on "data viz", "chart", "graph", "visualization", "dashboard", "plot", "Recharts", "Nivo", "D3". NOT for static images, print graphics, or basic HTML tables.
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。
Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes. Activate on 'execution trace', 'trace execution', 'execution path', 'debug execution', 'execution log'. NOT for performance analysis (use dag-performance-profiler) or failure investigation (use dag-failure-analyzer).
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。
Expert bot developer specializing in Discord, Telegram, Slack automation with deep knowledge of rate limiting, state machines, event sourcing, moderation systems, and conversational AI integration. Activate on 'Discord bot', 'Telegram bot', 'Slack bot', 'chat automation', 'moderation system'. NOT for web APIs (use backend-architect), general automation scripts (use python-pro), or frontend chat widgets (use frontend-developer).
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。
Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).
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。
Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).
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。
Simulate transcription factor perturbation effects on cell state in silico with CellOracle and Dynamo, and predict transcriptional responses to genetic perturbations with GEARS, scGen, and CPA. Covers the direction-not-magnitude principle, local-linear validity, the GRN/velocity error it inherits, baseline discipline (mean and additive baselines), and the validation gap. Use when predicting TF knockout or overexpression effects, ranking driver TFs for fate transitions, or planning perturbation experiments. For GRN construction see multiomics-grn; for experimental Perturb-seq see single-cell/perturb-seq.
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 abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.
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。
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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。
Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.
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 gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.
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。
Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.
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。
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
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。
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
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。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。
Assess whether a hospitalized adult with diabetes on insulin pump therapy remains a candidate for continued inpatient pump use. Consider triggers such as the clinician asking "Is this patient appropriate to continue insulin pump in hospital?" and evaluate for impaired consciousness, inability to adjust pump settings, critical illness, DKA, or hyperosmolar hyperglycemic state.
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。
Suggests continuous subcutaneous insulin infusion (CSII) for adults with type 1 diabetes mellitus who desire increased insulin delivery flexibility or improved treatment satisfaction and are capable of using the device. Clinical triggers include statements such as 'Patient wants more lifestyle flexibility with insulin' or 'Consider pump for improved satisfaction in T1DM'.
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。
Reconcile baseline-versioned files that the `create-baseline upgrade` CLI staged for LLM-assisted semantic merge. Use when the CLI prints "Open Claude Code and run /upgrade-project to reconcile". Reads the stage manifest at `.claude/state/upgrade/<ts>/manifest.json`, reasons through each pending file's three-way delta in main context, writes a reconciled LOCAL, then deletes the stage when every file lands. Supports `--dry-run` (preview the reconciled diff without writing) and a structured "needs-user-input" fallback when the conflict cannot be disambiguated automatically.
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。
Triage an incoming request — pick the workflow entry phase (intake / spec / tdd / chore) and record the workflow statefile that the Track Guard reads.
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。
On-demand "what's new" generator. Main context (which knows the impending change) writes keepachangelog-style entries to a fragment file at `.claude/state/whatsnew/<slug>.json` (gitignored, transient). An optional per-project `project.json → whatsnew.route_workflow` names a routing workflow that consumes the fragment and renders it wherever the project wants (a site page, a release body, a docs page). Not a workflow phase — it never blocks a commit, and it never writes `CHANGELOG.md` (that file is owned solely by semantic-release in CI).
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。
Read-only release + backlog recap. Reports the last release, commits-since-tag classified by conventional-commit type with the semver bump they trigger and pushed-vs-origin state, the backlog bucketed (open/picked-up/dropped with epic parent→child nesting), the roadmap epics with their per-task tallies, and condensed open questions — then recommends the next pickup. One `cli.mjs recap` call returns all six recap keys. Invoke any time (on demand) to plan a release or choose the next thing to build; a compact form is also surfaced at session start. Not a workflow phase; never writes CHANGELOG; never starts or commits 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。