「bec」の検索結果

Control and interact with a live browser session on any scraped page — click buttons, fill forms, navigate flows, and extract data using natural language prompts or code. Use when the user needs to interact with a webpage beyond simple scraping: logging into a site, submitting forms, clicking through pagination, handling infinite scroll, navigating multi-step checkout or wizard flows, or when a regular scrape failed because content is behind JavaScript interaction. Also useful for authenticated scraping via profiles. Triggers on "interact", "click", "fill out the form", "log in to", "sign in", "submit", "paginated", "next page", "infinite scroll", "interact with the page", "navigate to", "open a session", or "scrape failed".

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 about phishing campaigns, social-engineering techniques, BEC (business email compromise), pretexting, AiTM (adversary-in-the-middle) kits, or specific phishing-kit families. Self-updating knowledge cell.

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 a graph gap or Evidence Need should become a targeted paper-search contract or candidate lead list.

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。

Write a client update — identify what was done, what's next, and surface risks before they become surprises. End of day, start of day, or end of week.

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。

"Apply Actor-Network Theory (Latour, Callon) to trace how human and non-human actors (actants) form networks through translation processes. Use this skill when the user needs to map sociotechnical assemblages, analyze how innovations stabilize or fail through network-building, trace the four moments of translation (problematization, interessement, enrollment, mobilization), or when they ask 'how did this technology become accepted', 'who and what holds this network together', or 'why did this innovation fail to gain traction'.".

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。

Container and Kubernetes security assessment. Tests container escape vectors, Docker/containerd socket exposure, K8s RBAC misconfigurations, pod security violations, exposed API servers, etcd access, service account token abuse, image layer secrets, private registry attacks, SSRF to metadata services, cross-namespace network bypass, CIS benchmarks, crypto miner detection, resource exhaustion, and admission controller gaps. Both external and internal (compromised pod) perspectives. Uses trivy, kube-bench, kubectl, nuclei, dive, amicontained, and docker-bench-security. Covers OWASP Kubernetes Top 10 and all 22 Kubernetes Goat attack scenarios.

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。

Drive the user's real, logged-in browser from the shell via the reins CLI. Because it's their actual browser, every site is already authenticated — so you can scrape behind logins, read cookies/tokens/localStorage, watch and replay live API traffic, call a site's own API as the signed-in user, and click/type/screenshot. Use whenever asked to interact with, test, scrape, extract data from, or automate a live webpage or authenticated site in the user's browser.

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。

Regenerate and commit Class S site-served artifacts after registry changes, then sync documentation across the main repo and the adjacent wiki. Use this skill when: you've changed registry/nodes/, registry/named/, or named-skills.json and need to regenerate docs/graph/* so the live website reflects the update; CI Guard E (docs-cohesion.yml) is failing because Class S artifacts are out of date; someone says "sync the docs", "regenerate docs", "run gaia dev docs", "update the site graph", "commit the Class S artifacts", "website is stale", or "update the wiki"; or you're finishing a registry PR and need to ensure docs/graph/gaia.json, docs/graph/gaia.gexf, docs/graph/gaia.svg, and docs/graph/named/index.json are committed alongside the source change. Also covers updating README.md, docs/index.html, and mirroring changes to the adjacent wiki repo at ../gaia-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。

Defeats control-flow-flattening obfuscation by identifying the dispatcher/state- variable structure and reconstructing the original control flow so the logic becomes readable. Activates for requests to defeat control-flow flattening, deobfuscate an OLLVM-flattened function, or recover original control flow from a dispatcher loop.

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Apply a formal state machine to issues — assign category (bug/enhancement/question/spike) and state (needs-triage → needs-info → ready-for-agent → ready-for-human → wontfix). Issues marked ready-for-agent become inputs to supergraph:plan. Use when processing a backlog, reviewing new issues, or preparing work for automation.

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Use when a task needs the judgment of a Computer, ATM, and Office Machine Repairer — triaging a down ATM, PC, or copier against a contracted SLA clock, deciding board-swap vs. component-level repair, handling cash-dispenser faults under dual-custody rules, or reading fleet MTBF/MTTR data to catch a reliability problem before it becomes a run of service calls.

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 a task needs the judgment of a Conveyor Operator — diagnosing a mistracking belt before it becomes edge damage or spillage, verifying every lockout/tagout isolation point on a conveyor with a gravity take-up or incline before entering the nip zone, calculating each conveyor's actual delivered capacity in a multi-conveyor line to find the true bottleneck, deciding whether a mechanical or vulcanized splice fits a given tension rating, or judging whether a pinch-point guarding gap needs an immediate stop versus a scheduled fix.

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 a task needs the judgment of an auto-glass installer/repairer — deciding whether a chip or crack qualifies for resin repair versus full replacement, calculating a safe drive-away time for the adhesive and conditions on a job, determining whether a windshield replacement triggers ADAS camera recalibration, or diagnosing a cut-out/pinch-weld defect before it becomes a leak or corrosion claim.

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。

Invoke a Rubber Duck Reviewer subagent to independently critique plans and implementations before proceeding. Use when the agent is about to implement a non-trivial plan (multi-file changes, architectural decisions, security-sensitive logic, database schema changes), after completing a self-contained unit of work (module, endpoint, feature), when stuck or facing repeated failures (same test fails 2+ times, unexpected results), or when the agent wants independent validation of assumptions and design decisions. Triggers on any non-trivial implementation task where independent critique would catch blind spots before they become costly mistakes.

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。

Reach and sustain inbox zero: triage workflows, filters, templates, batching, and unsubscribing systems. Use when email eats your day or your inbox has become an anxiety source.

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。

Generate a production-grade Industrial IoT protocol bridge — OPC UA ↔ MQTT Sparkplug B, Modbus → MQTT, or direct PLC (Rockwell/Siemens/Beckhoff) → cloud (AWS IoT Core / Azure IoT Hub / GCP IoT / self-hosted HiveMQ)..

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 psychological safety in your team using Simon Sinek's Leaders Eat Last Circle of Safety model and Amy Edmondson's research. Use when a leader says "my team doesn't speak up", "people are afraid to fail", "nobody challenges bad ideas", "people only tell me what I want to hear", "I want my team to take more risks", "there's a blame culture", "people hide mistakes", or "I want to create a safe environment". Also trigger when someone describes a team where postmortems turn into blame sessions, where people stay quiet in meetings and then vent privately, where mistakes are hidden until they become crises, or where the same person speaks 80% of the time in every meeting. Psychological safety is not about comfort - it is the condition that makes high performance possible.

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。

Minimal personal narrated-video pipeline — a topic becomes a talking-head-free explainer MP4 (1080p or 4K) via script → Azure TTS (SSML) → Remotion. Use when the user wants a quick narrated video from a topic without the full video-podcast-maker machinery (no extra skills, no thumbnails/shorts/publish matrix). Do NOT trigger for heavy production needs — use video-podcast-maker for those.

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 dialyzer over the Yuzu Erlang gateway. Sources the Erlang toolchain and runs rebar3 dialyzer. Use after any .erl change in gateway/ — CLAUDE.md mandates dialyzer be clean before commit because compilation success does not imply type correctness.

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 dialyzer over the Yuzu Erlang gateway. Sources the Erlang toolchain and runs rebar3 dialyzer. Use after any .erl change in gateway/ — AGENTS.md mandates dialyzer be clean before commit because compilation success does not imply type correctness.

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 to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server is available. Don't use for general Google Compute Engine instance management, VPC configuration, or standard IAM auditing.

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Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to self-hosted inference on GKE, or asks follow-up questions during such a migration (hardware sizing, model staging, manifest generation, validation, traffic cutover). DO NOT use for brand new GKE inference deployments with no existing workload to migrate (use gke-inference instead). DO NOT use if the user intends to automate the migration via the Gemini Cloud Assist MCP server.

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。

Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, configuring Workload Identity, or deciding between Autopilot and Standard modes. Don't use for specialized GKE networking (use gke-networking), advanced security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).

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。

Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).

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。