874 件 ・ 関連度順
概要と使いどころ
Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new project.
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Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
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Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.
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Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use when prioritizing features, synthesizing user research, writing requirement documentation, or developing product strategy.
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Atlassian Jira expert for creating and managing projects, planning, product discovery, JQL queries, workflows, custom fields, automation, reporting, and all Jira features. Use when setting up or configuring Jira projects, writing JQL and advanced searches, creating dashboards, designing workflows, or performing technical Jira operations.
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Summarize a customer interview transcript into a structured template with JTBD, satisfaction signals, and action items. Use when processing interview recordings or transcripts, synthesizing discovery interviews, or creating interview summaries.
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Create a structured customer interview script with JTBD probing questions, warm-up, core exploration, and wrap-up sections. Follows The Mom Test principles — no leading questions, no pitching, focus on past behavior. Use when preparing for user interviews, creating interview guides, or planning discovery research.
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Guides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).
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Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.
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Diagnoses, remediates, and manages Identity and Access Management (IAM) access issues on Google Cloud. Supports two distinct operational modes: (1) Requester Flow for developers encountering access denials (capturing error context, self-service PAM JIT activations, elevated developer self-remediation, or logging structured tickets), and (2) Resolver Flow for privileged administrators (authoritative Policy Troubleshooter analysis, deny policy exemptions, least-privilege role discovery, and PAM/IAM provisioning).
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Discovers golden base images for creating GKE custom node images based on technical specifications or context clues. Use when finding the golden base image for custom GKE node creation, mapping cluster configuration parameters (GKE version, OS, architecture, accelerators, gVisor, cgroups) to image names, or querying GKE base image maps. Don't use for general GKE cluster creation (use gke-cluster-creation) or standard node pool management (use gke-basics).
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Generates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.
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Generates valid PromQL queries from Cloud Monitoring metric descriptors and resource parameters. Use when asked to create, generate, write, or format PromQL queries, PromQL strings, or PromQL aggregations for Cloud Monitoring metrics and resources. Don't use for raw metric discovery or metric selection.
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Drive Kortix itself from the terminal with the `kortix` CLI — preinstalled and pre-authenticated in every session sandbox. Use whenever a task means acting on THIS project's Kortix control plane rather than just editing files: manage secrets, list/spawn/watch/talk-to sessions, open or inspect change requests to land work on main, fire or manage triggers, set a reminder to check back on this task later (`kortix remind "…" --in 24h --every 1h`, when the project's `reminders` feature flag is on), call connectors, connect Slack, or read project info. This is a discovery stub — the full, always-current reference is served live via `kortix skills get kortix-system` and its reference files.
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KubeSphere extension management Skill. Use when user requests to install, configure, upgrade, uninstall extensions, or query extension info/troubleshoot issues. Includes extension discovery, dependency management, install configuration, version management.
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Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.
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Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
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Expert guidance for integrating and building applications with shadcn/ui components, including component discovery, installation, customization, and best practices.
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Discovery skill for the Native SDK, the complete toolkit for building native desktop applications. Apps are authored in TypeScript + declarative Native markup (.native) by default and compiled to native code with no JS runtime in the binary; Zig cores are an explicit alternative, and WebViews are the optional web-content path. Use when the user asks what the Native SDK is, how to build a Native SDK app, author native UI, scaffold an app, configure app.json or legacy app.zon, add bridge commands, embed web content, package an app, test a running app, or automate a Native SDK app.
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Hunts for the other instances of a bug already found — the variants of one root cause across a codebase. Use immediately after a vulnerability, logic bug, or bad pattern turns up in a specific file and the question becomes where else it occurs, including the bare conversational form ("are there others like this?", "is this the same bug?"). Also for generalizing one known instance into a CodeQL or Semgrep query for its whole pattern family, and for triaging a set of look-alike candidates against a known root cause. Not for initial discovery with no bug in hand.
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Kubernetes cluster attack techniques covering the full attack lifecycle from initial foothold in a pod to cluster-wide compromise. Covers service account token theft and impersonation, RBAC misconfiguration exploitation including wildcard permissions and privilege escalation via role binding, direct etcd access for secret extraction, kubelet API abuse on port 10250 and read-only port 10255, pod escape via hostPID hostNetwork and hostPath volume mounts, Kubernetes secrets enumeration and decoding, admission controller bypass techniques, network policy bypass and lateral movement, cloud metadata service access from pods for credential theft on AWS EKS GCP GKE and Azure AKS, CRD and operator abuse for persistence, and node compromise via DaemonSet deployment. Tools include kubectl, kube-hunter, peirates, kubeaudit, kdigger, kubeletctl. Maps to MITRE ATT&CK T1609 Container Administration Command, T1610 Deploy Container, T1613 Container and Resource Discovery. Use this skill when assessing Kubernetes clusters, attacking from within a compromised pod, exploiting RBAC or kubelet misconfigurations, or performing cloud-native lateral movement.
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。
Submit compact PRIDE Archive API requests for proteomics project discovery and project-level metadata. Use when a user wants concise PRIDE summaries
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Submit compact MetaboLights requests for study discovery and study-level metabolomics metadata. Use when a user wants concise MetaboLights summaries
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。