Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
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。
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Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
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 code review of current git changes with a senior engineer lens. Detects SOLID violations, security risks, and proposes actionable improvements.
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 Scenario generation is blocked, refused, or returns a moderation or sensitive-content error, when a video is rejected after rendering or its audio track is flagged, when an output is flagged for likeness to a real person or for IP and copyright detection, when the same prompt passes on one model and fails on another, or when a team's own characters, weapons, or props get flagged. Keywords: blocked prompt, content moderation, refused generation, provider filter, false positive.
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 generating or editing video with Seedance models on Scenario via MCP: text-to-video, image-to-video, first and last frame anchors, reference-to-video from identity, product, or world references, prompt-based editing, extending a clip, audio-conditioned motion or voice, shot sound without music, first-frame versus reference conditioning, or an output blocked by moderation. Keywords: Seedance 2.5 and 2.0, ByteDance, T2V, I2V, V2V, native audio, OutputVideoSensitiveContentDetected.
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。
Creates an ALM (Application Lifecycle Management) plan for deploying a Power Pages site across environments. Gathers your promotion strategy, target environments, and approval requirements upfront, then generates a visual HTML plan document for your review and approval. **plan-alm does not deploy anything itself** — it is a planner. After you approve the plan, run the individual ALM skills (setup-solution, setup-pipeline, deploy-pipeline, or export-solution/import-solution); each detects the approved plan and executes the right step in order, keeping the plan updated as it runs. Use when asked to: "plan my alm", "set up alm", "create deployment plan", "plan my deployments", "help me deploy to multiple environments", "set up promotion strategy", "create cicd plan", "plan site promotion", "help me go to production", "set up pipeline for my site".
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 and verify a third-party `.ppmplugin` end to end. This recommended entry point detects Android and iOS source, confirms target platforms, then invokes the manifest, platform build, assemble, and audit stages in order. Advanced users can invoke a stage directly for focused reruns or debugging. Produces a native-only bundle (`manifest.json` plus an Android DEX and/or iOS framework) and stops on any failed stage rather than emitting an unverified partial build. Run after the native module exists.
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 existing table permissions on a Power Pages site by analyzing them against site code and Dataverse metadata, then GRADES the configuration with deterministic 1–5 scores across 3 security/usability/correctness categories (9 dimensions). Detection is anchored by a recurring-issue catalog so findings and severities stay consistent run-to-run. Generates an HTML report with an at-a-glance verdict and per-category scores in the Overview plus a single list of **major/minor** issues (each major issue labelled permissions-only or upstream-caused with a short justification), and suggested fixes. Use when the user wants to review, verify, check, score, or grade table permissions for security issues.
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。
Ensures the tenant has a usable Power Platform Pipelines host environment before any pipeline operation runs. Detects host state via the same resolution order as the Power Apps UI (org-db setting → BAP env metadata → default-custom-host setting); if any existing host (Platform or Custom) is found, uses it. If no host is bound to the source env, provisions a new **Platform Host** (recommended, idempotent) or a **Custom Host** via the BAP env-create API with the `D365_ProjectHost` template, or guides the user through PPAC install / `New custom host` (manual fallbacks). Polls lifecycle operations, verifies the host responds to Pipelines API calls, writes a host-check artifact other ALM skills consume. Use when asked to: "set up pipelines host", "ensure pipelines host", "no pipelines host", "install pipelines", "create pipelines host", "provision platform host", "provision custom host". Also invoked transparently by /power-pages:setup-pipeline when its host discovery step finds nothing.
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。
Manage active production incidents through detection, triage, mitigation, communication, and resolution with structured roles and decision-making. Use this skill whenever the user has an active incident, a production issue, a service outage, a security incident, or needs to plan incident response procedures. Triggers on incident response, production incident, outage, service down, site down, P0, P1, severity, downtime, on-call, incident commander, status page, postmortem prep. Also triggers when something is actively broken in production and the user is figuring out what to do.
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。
A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segment analysis, running discipline, matching a result to a pre-committed decision rule, and the common failure modes that produce confidently wrong shipping decisions. Use this skill whenever the user is planning a test that has not run yet: framing a hypothesis, sizing the sample, setting duration, choosing guardrails, or deciding whether something is worth testing at all. Triggers on design an experiment, experiment plan, A/B test, split test, multivariate test, holdout, experiment hypothesis, sample size, minimum detectable effect, MDE, test duration, guardrail metric, no peeking, pre-committed decision rule, is this worth testing. Use `experimentation-analytics` instead when the test has already run and the question is how to read the result panel.
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。
How to produce ad creative that converts at performance scale. Hook patterns, format selection, video pacing, variation systems, sequential testing methodology, fatigue detection, brand-voice alignment without conversion dilution, and platform-specific creative norms. Triggers on ad creative, ad design, hook patterns, ad video pacing, creative testing, ad variations, creative refresh, creative fatigue, refresh ad creative, video ads for Meta, TikTok creative, LinkedIn ad creative, ad asset library. Also triggers when a team is producing creative at scale, planning a creative test cycle, or auditing why creative is not converting.
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。
Optimize Kubernetes costs through resource right-sizing, unused resource detection, and cluster efficiency analysis. Use for cost optimization, resource analysis, and capacity planning.
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 SWIFT Customer Security Programme (CSP) advisor covering the Customer Security Controls Framework (CSCF v2026). Use this skill whenever a user asks about SWIFT CSP, CSCF controls, SWIFT security attestation, KYC-SA portal, SWIFT architecture types (A1/A2/A3/A4/B), mandatory vs advisory controls, independent assessment, SWIFT secure zone, secure flow zone, MFA for operators, SWIFT messaging security, payment fraud prevention on SWIFT, gap analysis for CSCF, or compliance with SWIFT's 32 controls (26 mandatory, 6 advisory in v2026) across the three objectives: Secure Your Environment, Know and Limit Access, Detect and Respond. Control 2.4 (Back-Office Data Flow Security) is now mandatory in v2026. v2026 attestation window is July 1– December 31, 2026. Trigger for any SWIFT CSP or CSCF compliance question.
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 NIST Cybersecurity Framework (CSF) advisor covering CSF 2.0 and CSF 1.1. Use this skill whenever a user asks about NIST CSF, cybersecurity risk management, the six CSF functions (Govern, Identify, Protect, Detect, Respond, Recover), CSF profiles, implementation tiers, gap assessments, organizational profiles, community profiles, CSF core subcategories, informative references, or mapping to other frameworks (NIST SP 800-53, ISO 27001, CIS Controls, COBIT). Also trigger for questions like "how do I implement NIST CSF?", "what does CSF 2.0 change?", "help me build a CSF profile", "how do I assess my cybersecurity posture?", or any request involving organizational cybersecurity risk strategy or framework alignment.
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。
Categorize invoices and receipts by vendor, expense, and tax bucket, detect duplicates, and produce a tax-ready monthly summary. Use during bookkeeping, tax prep, or expense reimbursement.
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 templated responses for common legal inquiries with escalation detection. Use when drafting legal responses.
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。
Transform AI-generated content into human-sounding writing via AI pattern detection, rhythm restoration, and authenticity scoring. Use when content sounds robotic, uses AI cliches, or the user wants to humanize or fix AI writing.
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。
Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. Use when building agents that get better over time, managing auto- memory, or designing self-correcting feedback loops.
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。
Security audit and vulnerability scanning for AI agent skills before install. Detects prompt injection, dangerous code, exfiltration, credential harvesting, and supply chain risks. Use when evaluating untrusted skills or gating installs.
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。
STRIDE threat modeling, DREAD risk scoring, secret detection, and secure architecture design. Use when conducting threat models, reviewing code for vulnerabilities, designing defense-in-depth, or scanning for hardcoded secrets.
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。
Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow endpoints, memory growth, large bundles, or traffic spikes.
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。
Environment and secrets management lifecycle: .env scaffolding, validation, leak detection, and rotation across Vault, AWS SSM, 1Password, and Doppler. Use when setting up projects, scanning for leaked secrets, or rotating credentials.
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。
MLOps across model deployment, ML pipelines, monitoring, and feature stores. Use when deploying models to production, building training pipelines, setting up drift detection, configuring feature stores, or automating ML CI/CD workflows.
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 data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.
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。