Feature discovery agent — mines project docs, memory, user feedback, and competitor gaps to surface the highest-leverage features to build next. Outputs a prioritized feature backlog.
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Feature discovery agent — mines project docs, memory, user feedback, and competitor gaps to surface the highest-leverage features to build next. Outputs a prioritized feature backlog.
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 and run a post-interview candidate debrief. Use when the user says "run the debrief", "how do we decide on this candidate", "let's calibrate", "we need to make a hire/no-hire call", "prep the debrief meeting", or wants to structure the decision-making process after interviews are complete - even if they don't explicitly say "debrief". Also use when a recruiter wants to prevent groupthink or HiPPO effect (highest paid person's opinion) from dominating the decision.
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Allocate testing effort based on risk and failure likelihood using Google's SET/SWE/TE model. Use when user says "risk-based testing", "where should I focus testing", "test coverage strategy", "prioritize what to test", "high risk areas", or needs to decide how to distribute QA effort across a codebase - even if they don't explicitly say "risk-based".
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。
Diagnose why a landing page, homepage, pricing page, or signup form is not converting, and produce a ranked fix list with a test plan. Runs a structured audit across motivation, value clarity, friction, anxiety, and CTA hierarchy rather than giving generic design opinions. Use when the user says "why isn't this page converting", "improve our conversion rate", "CRO", "conversion audit", "this page isn't working", "high traffic low signups", "people bounce", "review our landing page", or shares a URL and asks what is wrong with it. For rewriting the words only, see copy-review. For writing a new page from scratch, see landing-page-writer. For the statistics of running the test afterwards, see growth-experiment.
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。
Structure and run effective 1:1 meetings with direct reports using Camille Fournier's Manager's Path and Andy Grove's High Output Management frameworks. Use when a manager says "help me structure my 1:1s", "my 1:1s feel like status updates", "I don't know what to talk about in 1:1s", "my reports don't open up", "how do I make 1:1s useful", "what questions should I ask in 1:1s", or "I need a 1:1 template". Also trigger when someone mentions their 1:1s feel transactional, that they keep canceling them, or that they're not sure if their report is happy.
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 high-performing LinkedIn post in the user's brand voice using Hook-Story-Offer (Russell Brunson) and the Curiosity Gap hook method (George Loewenstein). Every post starts by selecting a named hook formula - specific number + surprising result, contrarian statement, personal confession, direct challenge, or before/after. Use when the user asks for a LinkedIn post, LinkedIn update, LinkedIn content, "post for LinkedIn", thought-leadership post, founder post, or short professional social content. Reads brand voice and ICP from knowledge/ so output sounds like the company, not generic AI. For long-form opinion pieces, see thought-leadership-writer. For the monthly posting plan, see social-calendar. For turning one asset into many posts, see content-repurposer.
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。
De-escalate a customer crisis using a structured acknowledge-triage-DRI-cadence-resolution framework. Use when user says "customer escalation", "angry customer", "exec escalation", "customer threatening to leave", "fire drill", "critical customer issue", "customer is livid", "customer complaint escalated", or "how do I handle this" about a tense situation - even if they don't say "escalation" explicitly. Applies to CSMs and CS managers managing high-stakes customer crises.
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 reference photographs and visually dense images into sparse, recognizable, high-saturation flat-vector-style illustrations through semantic compression rather than literal tracing. Use when the user asks for photo-to-flat illustration, photo-to-vector-style redraw, 照片转扁平插画, 无渐变高饱和插画, iconification, visual simplification, composition-preserving abstraction, a consistent minimal illustration set, or revisions that remove gradients, glow, texture, blur, clutter, text, logos, numbers, or UI. Default to human-perceived semantic complexity 6 out of 10 or lower, fixed-HSB solid fills, crisp boundaries, and no gradients or light-halo 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。
Generate TikTok/Shorts-style karaoke captions using MLX Whisper, ASS subtitles, and FFmpeg libass. Use when burning word-level highlight captions into a video, matching caption style from a screenshot, rendering ASS karaoke subtitles, or when the user runs /karaoke-captions.
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。
Explain, draft, or validate Obsidian Flavored Markdown syntax: properties, wikilinks, embeds, callouts, tags, comments, highlights, block references, math, and Mermaid. Use when the user explicitly requests Obsidian note formatting or syntax help, not for general Markdown or broad vault operations.
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。
One-command multilingual blog creation. Writes a blog post, translates it into user-specified languages, applies cultural adaptation, and emits hreflang tags, sitemap entries, and a CMS-ready language map. The complete write-to-publish pipeline for international content. Orchestrates blog-write, blog-translate, blog-localize, and (optionally) seo-hreflang. Use when user says "multilingual blog", "blog multilingual", "write in multiple languages", "international blog", "mehrsprachiger Blog", "blog multilingue", "blog multilingue", "create blog in German and French".
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。
Score one blog post (or a folder of posts) on a 5-category 100-point scale covering content quality, SEO, E-E-A-T, technical elements, and AI citation readiness. Runs the deterministic analyzer first, then adds judgment and prioritized Critical/High/Medium/Low fixes. Advisory style diagnostics never infer authorship or affect the score. Exports markdown, JSON or table; works with MDX, markdown, HTML or a URL. Use when the user asks to analyze, score, rate or check the quality of specific posts ("analyze blog", "blog score", "check blog quality", "rate this post"). For a site-wide health check (orphan pages, cannibalization, stale content, schema across all posts) use blog-audit 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。
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
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。
Plan, implement, audit, and review C#/.NET SwiftlyS2 plugins. Use when working with Commands, Core Events, Game Events, GameHooks Pre/Post, raw native hooks, Modules, Workers, Services, high-frequency runtime loops, NetMessages, Schema access, entity handling, thread safety, performance, or IPlayer lifecycle behavior.
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Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
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Convene the Council of High Intelligence — multi-persona deliberation with historical thinkers for deeper analysis of complex problems.
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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
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Build feature work with high overbuilding risk. Use for new behavior, product slices, or integrations where repository reuse, strict scope, and an explicit stop condition matter.
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, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (`audit_cluster.sh`), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (`gVisor`), enforcing Pod Security Standards (`restricted` labeling), and mounting Secret Manager secrets via CSI (`SecretProviderClass`). Use when auditing cluster security posture, isolating namespaces, applying pod security standards, setting up Workload Identity, or configuring network policies and secret volume mounts. Don't use for cluster-wide control plane security, RBAC hardening, Binary Authorization, Shielded Nodes, or enabling platform-level GKE add-ons (use gke-platform-security 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。
Configures best-practice, high-signal alerting policies for Google Cloud Run resources (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.
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。
This file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance that Google Cloud manages. Cloud SQL handles backups, high availability, and secure connectivity for relational database workloads.
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Diagnoses and prevents vbar_control_agent segfaults, out-of-memory (OOM) errors, and TPU device initialization failures on TPU v6e nodes in GKE caused by race conditions during TPU device resets or high-frequency metrics polling. Use when troubleshooting vbar_control_agent crashes, memory cgroup OOMs in serial console logs, tpu-device-plugin metrics checksum corruption errors, or custom TPU metrics collection conflicts on GKE TPU v6e nodes. Don't use for general non-TPU container OOM troubleshooting or standard GKE node lifecycle operations.
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。