3,350 件 ・ 関連度順
概要と使いどころ
Answer "what's breaking" questions from Sentry — find the right org and project, search issues and events with the vocabulary Sentry uses, and report what is actually firing rather than the first hit. Use when the user asks what errors are happening, whether something is stable, what changed since a release, or for any health summary of a service.
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。
Build a brief for an upcoming meeting from Circleback — the calendar event, the history with the same people and company, open commitments on both sides, and what is still unresolved. Use when the user asks to prep for a call, "what do I need to know before", who someone is, or where things stand with a customer or partner.
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 a time and put a meeting on the calendar through Superhuman — read what is already booked, check the participants' availability, propose slots, and create the event only once the user picks one. Use when the user asks what is on their calendar, when they are free, to find a time with someone, or to schedule, move, or set up a call.
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。
Turn a recurring chore into a Superset automation. Drafts the agent prompt, confirms the schedule or event trigger and the target, creates it with the CLI, and reviews the first run together. Use when the user wants a scheduled or recurring agent, a daily or weekly job, or an agent that reacts to a Slack message, GitHub pull request, Linear issue, Sentry issue or incoming email, or says things like "every morning do X", "automate this", "set up a cron agent", "run this on a schedule", "when someone opens a PR, do X".
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。
Writes and debugs Apex code, builds Lightning Web Components, optimizes SOQL queries, implements triggers, batch jobs, platform events, and integrations on the Salesforce platform. Use when developing Salesforce applications, customizing CRM workflows, managing governor limits, bulk processing, or setting up Salesforce DX and CI/CD pipelines.
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。
Designs distributed system architectures, decomposes monoliths into bounded-context services, recommends communication patterns, and produces service boundary diagrams and resilience strategies. Use when designing distributed systems, decomposing monoliths, or implementing microservices patterns — including service boundaries, DDD, saga patterns, event sourcing, CQRS, service mesh, or distributed tracing.
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。
Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.
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。
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.
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。
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.
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。
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
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。
Auto-generate a professional, client-ready GEO service proposal from audit data. Creates a full proposal in markdown and PDF including executive summary, findings, recommended service packages (Basic/Standard/Premium), pricing, timeline, and terms. Use when user says "proposal", "proposta", "offerta", "preventivo", "generate proposal", or after completing a GEO audit for a prospect.
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。
Audit TikTok Ads measurement, Pixel, mobile-first creative, audiences, Smart+, Shop and commerce campaigns, bidding, budgets, pacing, attribution, and policy. Use for TikTok Ads, TikTok Pixel, Smart+, TikTok Shop Ads, GMV Max, or Spark Ads. Read-only; use ads-server-side-tracking for server-side events and ads-optimize to change campaigns.
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。
Audit cross-platform attribution, conversion definitions, reporting windows, GA4, AdServices and AdAttributionKit, MMPs, browser and server events, offline conversions, and platform reconciliation. Use for attribution audit, attribution models, conversion windows, requests to add or total Meta and Google conversions, incompatible reporting-window aggregation, GA4 attribution, MMP review, AppsFlyer, Adjust, Branch, Singular, or cross-platform discrepancies.
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。
Append a dated event to a matter's history file and refresh the log row — captures new developments, status changes, risk re-assessments, deadline shifts, and settlement authority changes. Use when the user wants to log an update on a matter, note a development, or record a status change against the portfolio.
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。
Build or update a chronology from declared document sources and uploads — dated events extracted, de-duped, and tagged by significance per the matter theory. Use when the user asks to build a chronology or timeline from a production or matter file, says "chron from the production" or "what happened when", or needs a working, statement-of-facts, or witness-specific timeline.
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。
Drafts board or committee meeting minutes in your house format. Auto-detects upcoming board and committee meetings from your calendar, asks for the agenda and any slides or pre-read materials, and produces a complete draft in the format learned from your seed minutes. Also handles written consents in lieu of meetings. Trigger: "board minutes", "draft minutes", "upcoming board meeting", "committee minutes", "written consent", or calendar detection of an upcoming board or committee event.
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 the Convex deployment's 72h insights (read limits, OCC contention), root-cause each event in code, report evidence-backed perf/cost findings with fixes.
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-then-diff before writing a file to ensure the write is skipped when the content already matches, preventing unnecessary modifications and downstream side effects.
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 replacing a seat's occupant (rebuild/handover/swap), reasoning about stable-seat-identity vs fluid-occupant-identity, choosing an old-occupant disposition (retire/advise/shadow), or recording provenance for an occupant change. Two independent outcomes (continuityOutcome + seatBindingOutcome) and the 5 failure modes that prevent silent dishonesty.
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 reasoning about what survives compaction/context-loss/restart/seat-refresh, designing a high-fidelity restore packet, or distinguishing native runtime resume vs fork vs artifact-backed mental-model rebuild. Covers the 4 failure modes that prevent honest restore (compacted seat drops hot potato; restore packet preserves details but loses product intent; runtime resume mistaken for handover or fork; rebuilt seat starts with stale instructions).
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 configuring `rig watchdog` policies, authoring wake/refocus/alignment-checkpoint messages, or choosing the right intervention level for a stale-owner situation. The 3-level continuity-check stack (wake / refocus / alignment-checkpoint), the discipline that prevents cadence pollution and bureaucracy theater, and the artifact-pool loop-edge pattern for evidence-aware waking.
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 authoring or installing a rig bundle (packaged, shareable artifact that instantiates an opinionated OpenRig topology + workflow), reasoning about the bundle vs extension boundary, or auditing a bundle for portability. Covers the 4 failure modes that prevent bundles from working anywhere but the operator's machine, and the inspect-before-install discipline.
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。
Smart contract development advisor based on Trail of Bits' best practices. Analyzes codebase to generate documentation/specifications, review architecture, check upgradeability patterns, assess implementation quality, identify pitfalls, review dependencies, and evaluate testing. Use when asking whether a smart contract project follows development best practices, reviewing on-chain/off-chain split, upgradeability, or delegatecall proxy patterns against guidelines, or seeking recommendations on contract design, inheritance, events, documentation, dependencies, or test strategy.
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。
Cloud security attack methodology covering AWS, Azure, and GCP. Includes credential harvesting (IMDS, ~/.aws, env vars, leaked CI secrets, instance roles), enumeration with cloud-specific tools (pacu, ScoutSuite, Prowler, ROADtools, gcp_enum), privilege escalation paths (IAM PassRole, AssumeRole chains, Lambda/Functions privilege flips, Azure Owner-on-self, GCP serviceAccountTokenCreator), persistence techniques (IAM user/key creation, AAD app registration, GCP svc account key creation, EventBridge/Logic Apps backdoors), data exfiltration (S3/Blob/GCS, snapshot share, RDS/CosmosDB/Cloud SQL exfil), cloud-native lateral movement (cross-account assume, Azure AD multi-tenant, GCP project hierarchy), serverless attacks (Lambda env vars, layer hijack, Step Functions), Kubernetes-on-cloud (EKS/AKS/GKE-specific paths to node and AWS metadata), and CSPM evasion (CloudTrail blind spots, GuardDuty mute, Sentinel rule shaping). Use when the engagement scope is cloud accounts, when you've stolen cloud credentials, or when assessing cloud posture.
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。