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Train one of AgentDB's 9 RL algorithms on a stream of episodes. Use when the user has accumulated successful/failed episodes and wants to derive a policy, or when a task type is repeated enough to benefit from RL routing.
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。
Search with feature attributions — return WHY each match scored where it did. Use when debugging recall quality, auditing for bias, or explaining results to a user.
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。
Ask the AgentDB bandit which RL algorithm / skill / pattern fits the current task best. Use at task start when there are multiple plausible approaches and you want the data-driven pick.
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。
Integrating OWASP ZAP/Burp Suite/Nuclei, planning penetration tests, executing DAST, and scanning for vulnerabilities. For runtime vulnerability validation. Complements Sentinel static analysis.
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。
Automating browsers via Playwright and Chrome DevTools for data collection, form interaction, screenshot capture, and network monitoring. Task completion focus (vs Voyager for E2E testing).
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。
Shift-left safety net for dbt/SQL model edits. Runs change impact assessment before edits, generates SQL validation queries after, and executes them via `/mc-validate run`. Delegates health and monitor creation to peer skills.
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。
Tests in real browsers via Chrome DevTools MCP. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data. Requires the chrome-devtools MCP server to be configured.
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。
Clerk Billing for subscription management - render Clerk's PricingTable and in-app checkout drawer, configure subscription plans, seat-limit plans for B2B, feature entitlements with has(), and billing webhooks. Use for SaaS monetization, plan gating, checkout flows, trials, invoicing, and subscription lifecycle management.
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。
Search and inspect the harness memory namespace (HNSW + emergent-time decay).
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。
Turn a feature request into a minimal, file-level implementation plan before any code.
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。
Incremental refactorer. Use when refactoring ONE module at a time within a rescue workflow after safeguard has set up safety nets — never as a standalone refactor for greenfield code. Applies proven patterns: Strangler Fig, Branch by Abstraction, Expand-Migrate-Contract.
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。
Legacy refactoring orchestrator. Use when user says 'refactor', 'modernize', 'clean up this mess', 'rescue', or when dealing with old/messy/legacy code. Multi-session workflow — autopsy, safety net, incremental surgery, progress tracking.
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 safety nets before refactoring. Use when running surgeon or any risky refactor that needs a rollback point. Creates characterization tests, boundary markers, config freezes, and rollback points.
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 application to target platform. Use when user explicitly says 'deploy', 'push to production', 'ship it'. Handles Vercel, Netlify, AWS, GCP, DigitalOcean, and VPS with pre-deploy verification and health checks. Enforces cost allocation tags + Managed-vs-Self-Host crossover decisions so deploy choices map to actual unit economics, not hand-waved 'we'll optimize later'.
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。
Prioritize drug targets from a ranked gene list (e.g., scRNA-seq DE output) by orchestrating parallel API queries against UniProt, OpenTargets (with integrated DepMap CRISPR essentiality + gnomAD constraint), PubMed, the Human Protein Atlas (HPA), and ChEMBL tool compounds, then re-ranking by a composite score combining protein localization, druggability, disease genetics, tissue specificity (safety), focus-cell-type expression, CRISPR essentiality, LoF safety constraint, and research maturity. Use whenever the user wants to filter, triage, prioritize, or "do due diligence" on a list of candidate genes for drug discovery, especially after a DE / DEG analysis when they say things like "which of these should I follow up on", "filter for druggable targets", "make a target dossier", "rank these for tractability", "annotate these genes for druggability", or "build a target report". Trigger even when the user says just "filter these candidate genes" or hands over a CSV from a DE pipeline.
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 interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.
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 grow developer and user communities from scratch — covering ambassador programs, community-led growth (CLG), event operations, content strategy, and health metrics. Grounded in real playbooks from Notion, Lovable, AFFiNE, ClickUp, Asana, and Lark. Use when: building a community for an open-source project, SaaS product, or brand; recruiting and managing ambassadors or community leaders; planning community events (online/offline meetups, AMAs, hackathons); designing ambassador tier/points systems; setting community health KPIs; launching a Build-in-Public strategy; setting up Discord, Slack, or Lark communities; running developer relations (DevRel); or when the user asks about community operations, ambassador programs, CLG, user advocacy, KOC/KOL cultivation, UGC flywheels, community monetization, or any variation of "how do I grow my community".
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。
🇺🇸 Your dev team ships features nobody asked for while user-reported bugs pile up for months. Operations blames engineering for ignoring users; engineering blames operations for not understanding technical constraints. This gives you the complete Product × Engineering × Operations alignment SOP — from unified backlog to 10-day sprint cadence to veto power rules. What's inside: • Dual-layer Kanban system (master backlog + sprint board with unified tagging) • 10-day sprint standard process (Day 1 dev → Day 6 testable build → Day 10 ship) • Issue template with reproducibility requirements (3x reported = auto-severe) • Tri-party alignment meetings (daily standup / sprint planning / sprint review) • Operations veto power on releases (P0 bug = block shipping) • User feedback → product iteration closed loop (beta testing + interview SOP) • Core metrics framework (acquisition → activation → retention → monetization → referral) • Technical debt management (20-30% sprint capacity reserved) • Ready-to-use templates: Bug Report, Sprint Planning, Responsibility Matrix Built from: Real product strategy meetings + beta testing frameworks. References Supabase sprint model, Manus/DeepSeek commercialization alignment. By @WeiYipei. 🇨🇳 你的研发团队在做没人要的新功能，用户反馈的 Bug 堆了三个月没人动。运营觉得研发不听用户，研发觉得运营不懂技术。这份 SOP 给你从统一看板到 10 天迭代节奏到一票否决权的完整产研运协同框架。 🇯🇵 開発チームは誰も求めていない機能を作り、ユーザーから報告されたバグは何ヶ月も放置。このSOPは、統一バックログから10日スプリント、リリース拒否権まで、プロダクト×エンジニアリング×オペレーションの完全な連携フレームワークを提供します。 🇰🇷 개발팀은 아무도 요청하지 않은 기능을 만들고, 사용자가 보고한 버그는 몇 달째 방치됩니다. 이 SOP는 통합 백로그부터 10일 스프린트, 릴리스 거부권까지 제품×개발×운영 완전 협업 프레임워크를 제공합니다. Triggers: "product ops" | "engineering operations" | "product development SOP" | "sprint planning" | "iteration management" | "cross-functional alignment" | "product engineering ops" | "dev ops collaboration" | "产研运协同" | "迭代管理" | "产品研发运营" | "プロダクト開発運営" | "제품개발운영"
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。
스타트업·1인 법인 대표용 경영 리포트 자동 생성 스킬. 월별 손익 추이, 현금흐름(일별 net), cash burn rate(최근 N개월 평균 순지출), 비용 구조 분석. 공식 재무제표와 별개로 의사결정용 가공 리포트 제공.
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。
Go application architecture with net/http 1.22+ routing, project structure patterns, graceful shutdown, and dependency injection. Use when building Go web servers, designing project layout, or structuring application dependencies.
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。
Submit protein folding tasks to the AlphaFold3 C550 inference platform. Use when the user needs to run AlphaFold3 predictions with pre-prepared JSON input files on the AI4S Kubernetes cluster.
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 repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.
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。
Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence limits.
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。
Turn a feature request into a minimal, file-level implementation plan before any code.
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。