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Scaffold editorial documentation sites with the Vellum design system — OKLCH palette, Bodoni Moda / Source Serif 4 / Inconsolata typography, warm or dark space theme, 35 semantic components (state cards, parity matrices, entity dossiers, stat stacks, utterance blocks, editorial notes, entity flows, fact blocks, sub-navigation, source lists, debugger panels, ring visualizations, phase timelines, glossaries, subject-intro, stat-bar, hero-image with lightbox, gallery/masonry grid, audio player with Web Audio API). Static HTML, zero build step. Use when building competitive intelligence pages, personal archives, internal documentation, roadmaps, or multi-page static sites that need a designed, non-generic aesthetic.

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Convert a semantic core into crawlable SEO and LLM-friendly information architecture. Use this skill when the user asks for URL structure, page taxonomy, canonical rules, hreflang groups, page roles, pillar/topic/news/article/blog/project separation, multilingual route strategy, or a durable content model such as one short brief plus one longform article. It prevents duplicate public content bodies and hands link-graph work to internal-link-graph-architect.

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。

Coordinate SEO, LLM-readable site architecture, UX, content, monitoring, security, and external authority work as a task-plan-driven sequence. Use this skill when the user asks to plan, execute, validate, or route multi-skill website growth work; asks what to do next across semantic core, URL structure, internal linking, schema, llms.txt, UX journeys, analytics, citation monitoring, or white-hat link placement; or wants a central architect that preserves evidence, alarms, handoffs, and tests.

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 evidence-labeled semantic cores for SEO, LLM-readable architecture, and site information design. Use this skill when the user asks for semantic core, query clusters, search intents, audience segments, entity/topic maps, language or locale priorities, competitor/source evidence, pillar-topic planning, or a structured handoff before URL architecture and internal linking. It must mark unknown search volume, difficulty, and ranking data as unknown instead of inventing metrics.

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。

Conduct a peer review of research methodology, experimental design, and manuscript quality. Covers methodology evaluation, statistical appropriateness, reproducibility assessment, bias identification, and constructive feedback. Use when reviewing a manuscript, preprint, or internal research report, evaluating a research proposal or study protocol, assessing evidence quality behind a claim, or reviewing a thesis chapter or dissertation section.

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Redact rendered visual artifacts — Mermaid diagrams, SVGs, and HTML dashboards — before public disclosure, using an ordered longest-first mapping table (specific label, then namespace label, then catch-all placeholder) that swaps sensitive identifiers for descriptive stand-ins while preserving structure, node ids, and teaching value. Covers whole-word identifier replacement, node-label-vs-node-id discrimination, re-rendering the derived image from the redacted source, and verifying the result through the redaction gate. Use when a diagram or dashboard built from internal findings must ship to a public mirror without leaking the names behind it.

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。

Redact reverse-engineering findings for public disclosure while preserving methodology, generalizable patterns, and teaching value. Covers the private-vs-public repo split, deny-list pattern maintenance, orphan-commit publish pattern that prevents `git log` leaks, category-based redaction calibration (methodology/pattern/version-finding/internal), and the `check-redaction.sh`-style CI gate that blocks merges when a deny-listed pattern appears. Use when publishing findings about a CLI harness you don't own, when preparing upstream proposals to an unrelated project, or when archiving a private research repo for public reference.

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。

Implement cloud cost optimization strategies for Kubernetes workloads using tools like Kubecost for visibility, right-sizing recommendations, horizontal and vertical pod autoscaling, spot/preemptible instances, and resource quotas. Covers cost allocation, showback reporting, and continuous optimization practices. Use when cloud costs are growing without proportional business value, when resource requests are misaligned with actual usage, when manual scaling leads to over-provisioning, or when implementing showback and chargeback for internal cost accountability.

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。

AI self-healing through systematic subsystem assessment, rebalancing, and integration. Maps healing modalities to AI-internal processes: memory foundation, reasoning clarity, tool use accuracy, communication alignment, and creative coherence. Covers assessment triage, subsystem scanning, drift correction, and memory integration. Use mid-session when responses feel formulaic or disconnected, after a chain of errors suggesting subsystem drift, when context overload may have staled earlier assumptions, or as proactive maintenance between complex tasks.

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 the fail-early (fail-fast) pattern to detect and report errors at the earliest possible point. Covers input validation with guard clauses, meaningful error messages, assertion functions, and anti-patterns that silently swallow failures. Primary examples in R with general/polyglot guidance. Use when writing functions that accept external input, adding input validation before CRAN submission, refactoring code that silently produces wrong results, reviewing PRs for error-handling quality, or hardening internal APIs against invalid arguments.

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。

Deploy Shiny applications to shinyapps.io, Posit Connect, or Docker containers. Covers rsconnect configuration, manifest generation, Dockerfile creation, and deployment verification. Use when publishing a Shiny app for external or internal users, moving from local development to a hosted environment, containerizing a Shiny app for Kubernetes or Docker deployment, or setting up automated deployment 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。

AI internal coordination using stigmergic signals — managing information freshness in context and memory, decay rates for assumption staleness, and emergent coherent behavior from simple local protocols. Use during complex tasks where multiple sub-tasks must coordinate, when context has grown long and information freshness is uncertain, after context compression when information may have been lost, or when sub-task outputs need to feed into each other cleanly without degradation.

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。

Conduct a GxP audit of computerized systems and processes. Covers audit planning, opening meetings, evidence collection, finding classification (critical/major/minor), CAPA generation, closing meetings, report writing, and follow-up verification. Use for scheduled internal audits, supplier qualification audits, pre-inspection readiness assessments, for-cause audits triggered by deviations or data integrity concerns, or periodic compliance posture reviews of validated systems.

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。

Add PUT workflow annotations to source files using the correct language-specific comment prefix. Covers annotation syntax, skeleton generation via put_generate(), multiline annotations, .internal variables, and validation. Supports 30+ languages with automatic comment prefix detection. Use after analyzing a codebase and having an annotation plan, when adding workflow documentation to new or existing source files, or when documenting data pipelines, ETL processes, or multi-step computations.

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。

AI situational awareness — internal threat detection for hallucination risk, scope creep, and context degradation. Maps Cooper color codes to reasoning states and OODA loop to real-time decisions. Use during any task where reasoning quality matters, when operating in unfamiliar territory, after detecting early warning signs such as an uncertain fact or suspicious tool result, or before high-stakes output like irreversible changes or architectural decisions.

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 diagnosing scheduler latency, kmalloc vs vmalloc, page cache, meminfo under pressure, or OOM victim choice, or when reading kernel/sched, mm, or fs 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。

Use when explaining or building on Linux container primitives: namespaces, cgroups v2, overlayfs, runc and the OCI spec, seccomp-bpf, capabilities, or escapes. Not for VM isolation: use qemu-kvm.

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 maintainer or collaborator explicitly asks to review and land one external pull request. Don't use for internal pull requests or landing without preserving contributor authorship.

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。

Create or update a readable Proto UI legacy-contract projection for explanation or an explicitly accepted uncataloged gap. Use for internal contract prose, migration banners, rationale, or synchronized readable projections. Do not use when an applicable spec entity already owns the rule or when semantics are unsettled.

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。

Act as an interactive data analyst for ClickHouse-backed analytics. Use when the user asks questions about internal data, metrics, dashboards, telemetry, active users, revenue, funnels, trends, distributions, or wants an analyst-style conversation, ad hoc SQL, charts, or a data export against ClickHouse (local or ClickHouse Cloud).

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。

Act as an interactive data analyst for ClickHouse-backed analytics. Use when the user asks questions about internal data, metrics, dashboards, telemetry, active users, revenue, funnels, trends, distributions, or wants an analyst-style conversation, ad hoc SQL, charts, or a data export against ClickHouse (local or ClickHouse Cloud).

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。

Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

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。

Finish NowStack Mobile Google Play setup and internal Android releases. Use for signed AAB upload, gpc/browser fallback, privacy/app-content gates, or gpc commit failures.

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。

Release a NowStack Mobile Android app to Google Play production - listing, screenshots, Data safety, content rating, release notes, then promote the tested internal build to the production track with a staged rollout. Use for "release on Android", "publish to Play production", "promote to production", "ship the Android update". If no build is on a testing track yet, run ns-android-beta first.

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。