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Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.

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。

Automate Abstract tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。

Route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.

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 you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.

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 to "check technical SEO"; audits crawlability, indexing, Core Web Vitals, robots.txt, sitemaps, canonicals, redirects, and migrations. Not for on-page tags or content — use on-page-seo-checker. 技术SEO/网站速度

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 to "build our pitch deck narrative", "write a fundraising story", or "structure the sales pitch narrative"; derives from the narrative canon a company pitch/deck narrative — problem → the undeniable shift → product-as-hero → proof → the ask — as a slide-beat outline (one narrative beat per slide, with the claim ID each proof beat rests on), for both a sales pitch and a fundraising deck. Every proof beat is labeled Measured / User-provided / '[needs source]' and unverified ones route to the claims candidates. Not for the launch-window battle cards and talk track — use sales-enablement-kit; not for the durable message hierarchy or the arc itself — use message-system-architect / strategic-narrative-designer; not for finished deck visual design — out of scope; not for claim adjudication — use offer-claims-registry. 路演叙事/融资故事/销售 pitch/幻灯节拍

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 to "package the launch assets", "build a press kit", or "prep the store listing and go-live checklist"; produces a tier-scoped launch asset manifest with production status — a press kit spec (factsheet, description, history, features, videos, images, logo and icon, awards, contact), demo script and screenshot specs, a launch FAQ, dual-store listing metadata drafts against the official character budgets (per App Store Connect / Play Console documentation), and a technical go-live checklist manifest (robots flip, sitemap, OG tags, analytics verification — execution stays with technical-seo-checker and serp-markup-builder). Not for the message copy itself — use message-house-builder or content-writer; not for landing page UX — use landing-optimizer; not for keyword research beyond the store surfaces — use keyword-research. 发布资产打包/press kit/商店listing规格/上线检查清单

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.

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。

Implements declarative Flutter routing using GoRouter v17 with URL-based navigation, deep linking, and authentication guards. Activates when configuring GoRoute path patterns, setting up StatefulShellRoute for persistent bottom navigation state, implementing ShellRoute for shared nested layouts, adding redirect guards with loop prevention (idempotent redirects), configuring deep links for iOS (FlutterDeepLinkingEnabled) or Android (intent-filters), debugging redirect loops or navigation stack issues, handling 404 error routes with errorBuilder, using named routes with path/query/extra parameters, or migrating from imperative Navigator.push to declarative routing. Ideal for Flutter web apps requiring URL bar synchronization, mobile apps with universal links or app links, or multi-level navigation hierarchies.

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。

Implements type-safe Flutter routing using AutoRoute v11 with code generation and @RoutePage annotations. Activates when setting up AutoRoute navigation, configuring AutoRouterConfig or RootStackRouter, generating route files with build_runner, using @RoutePage, @PathParam, or @QueryParam annotations, implementing AutoTabsRouter for bottom navigation tabs, adding navigation guards with AutoRouteGuard, setting up nested or child routes, handling type-safe route parameters, or migrating from string-based Navigator routing to compile-time-verified routes. Ideal for large apps requiring refactoring confidence and zero string-based routing errors.

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。

Inspect, debug, and inject custom code into a running Steam client — desktop (macOS, Linux, Windows) or Steam Deck over the network — through its Chromium Embedded Framework (CEF) Chrome DevTools Protocol endpoint. Use when developing Steam UI plugins, mods, themes, or integrations — injecting CSS/JS to try a change, locating webpack modules and obfuscated class names, reading React or SteamUIStore state, inspecting Big Picture / Quick Access Menu windows, screenshotting what actually rendered, streaming console logs, or diagnosing runtime errors in Steam's UI. The same commands run against either device, or both at once. Injection is per-session and for development only. Not for building a plugin loader or making changes persist across restarts, Steamworks game SDK work, game development, or scraping the public Steam web store.

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。

解释火山 AI 音视频互动的产品能力、适用边界与最新官方文档；生成或修改 VoiceChat/Aibot 配置；并帮助用户搭建、运行和分阶段排查最小 InteractAI VoiceChat Web Demo。

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。

Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since 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。

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

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 distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed 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。

Generate comprehensive C4 architecture documentation for an existing repository/codebase using a bottom-up analysis approach.

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。

Cross-session progress tracking, pattern extraction, and session resumption. Gives agents memory across sessions using markdown files in `.memory/pipeline/progress/`. Subsumes self-improving-agent — handles both implementation tracking AND experiential learning in one skill. Workflows invoke specific protocols by name.

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 trail register: timestamp, user, module, record, action, field changed, old and new value, and the reason for the change. Use for change history and control evidence.

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 an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without

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。

Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.

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 audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

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 centralized audit logging and SIEM integration. Configure log retention and security monitoring. Use when implementing audit trail requirements.

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。

Multi-source research synthesizer. Takes a question, runs 3-5 parallel web searches with varied phrasings, deduplicates, and returns a cited, concise answer. For Hebrew questions, searches in both Hebrew and English. Output is always under ~400 words.

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 wants to humanize text, make writing sound less like AI, or rewrite content to feel more natural. Triggers on "humanize", "make it sound natural", "too robotic", "sounds like AI", or similar. Works in Hebrew and English. Always output only the rewritten text — no explanations.

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。