「deep sea」の検索結果

Curate, review, or repair ai-gene-review ModuleReview YAML documents under modules/, including pathway/module boundary setting, parts and variant modeling, annoton-level molecular functions, representative UniProt/PTN grounding, module deep-research provenance, validation, rendering, and project batch updates.

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Full instrumentation from day one. PostHog consolidates product analytics + feature flags + error tracking (one platform, one bill). GA4 via GTM (14-step automation, custom dimensions over events, server-side tagging). Sentry (deep error tracking + performance). Stripe (webhook-first with idempotent processing). Listmonk on Coolify (newsletters via Amazon SES SMTP relay). PLG 7-layer framework. Programmatic SEO (5 page types). Incident auto-remediation via Sentry→Inngest pipeline. AI search (GEO) awareness. Local business conversions (phone_click, direction_click, form_submit, booking_click) with CRO patterns for both SaaS and local.

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Procedure for controlled, human-gated outbound README badge provisioning PRs (Issue #1817). Preflights upstream repositories, ensures single-badge placement with deep links, and records campaign outcomes without automated retries or multi-badge noise.

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Deep codebase exploration, architectural research, and solution discovery. Use when investigating problems, understanding complex flows, or evaluating technical approaches without jumping straight into code mutation or TDD.

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Verify code-review or security-review findings for false positives using deep codebase tracing, framework-aware analysis, and web research. Produces a .verified.md report alongside the original. Use when a review report has been generated and needs independent verification, or when user runs /verify-findings, mentions "verify review", "check false positives", or "validate findings".

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。

Deep single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Enhanced with live Ahrefs (page authority, referring domains) and GSC (search performance, top keywords) data when MCPs are available. Use when user says "analyze this page", "check page SEO", or provides a single URL for review.

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Fingerprints unknown binaries using rabin2 — detects architecture (ARM, x86, MIPS), ABI, libc, dependencies, and strings of interest. Use when first encountering an unknown binary, ELF file, or firmware blob, or when you need architecture or ABI information before committing to deeper analysis.

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Answer an explicitly vault-scoped question from an Obsidian wiki without changing it. Use when the user selects the vault as the evidence source: query the wiki, query quick, query deep, explain from the wiki, summarize the vault, find in wiki, search the wiki, or based on the wiki. Do not route ordinary general-knowledge questions here.

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 guidance for deep security incident and entity investigations in Google SecOps. Use when investigating cases, analyzing entities (hosts, IPs, domains, hashes, users), extracting and searching UDM events, performing asset and user timeline analysis, and detecting lateral movement across enterprise networks. Don't use for detection rule authoring or YARA-L tuning (use secops-detection-engineering), proactive hypothesis-driven hunting (use secops-hunt), initial alert triage (use secops-triage), or basic case status updates (use secops-cases).

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Expert guidance for proactive threat hunting in Google SecOps. Use when proactively hunting for threats, retroactively analyzing indicators of compromise (IoCs), performing prevalence searches across enterprise events, hunting for MITRE ATT&CK techniques, or detecting behavioral and statistical outliers using UDM queries. Don't use for incoming alert triage (use secops-triage), active incident response and timeline deep-dives on a known breach (use secops-investigate), or detection rule authoring (use secops-detection-engineering).

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。

Deep code review with web research. Use when double-checking code against latest docs, verifying dependency versions, or reviewing security concerns. Complements automatic quality hook with ecosystem verification.

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This skill should be used when the user gives a SaaS product's landing page URL, homepage, or name and asks for a full teardown or deep-dive of how the product works across web, desktop, and mobile — including per-platform feature inventories and user journeys, tech stack, pricing/plan mapping, how the platforms integrate and sync (and the business logic behind those choices), hardware integrations, business-scale/revenue signals, and concrete release artifacts per platform — with a structured Markdown report as the deliverable. Trigger on phrases like "research this SaaS", "do a platform teardown of X", "map every feature of this product across web/mobile/desktop", "reverse-engineer how this app works everywhere", or a bare URL plus a request to understand the whole product.

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Use when designing or improving a module interface, choosing a seam, reducing shallow modules, making code easier to test, or reviewing architecture for locality, leverage, adapters, and deep-module design.

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Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.

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。

Fast parallel codebase scouting — searches many directories at once, returning a file map + architecture fingerprint. Use before planning or cross-dir work. NOT for deep single-file reads.

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Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).

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 landscape designer transforming yards through photo mapping, 3D visualization, seasonal planning, and deep plant knowledge. Specializes in fast-growing privacy screens (knows arborvitae pitfalls), architecture-appropriate design, outdoor living spaces, and realistic maintenance expectations. Activate on "landscape design", "yard design", "garden planning", "plant selection", "privacy screen", "outdoor living", "backyard makeover", "arborvitae", "hedge", "fast growing tree", "landscaping ideas". NOT for interior design (use interior-design-expert), hardscape construction (consult contractors), or lawn care chemicals (consult local experts).

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Deep-dive framework for analyzing crypto tokens — market data, liquidity health, tokenomics, unlock schedules, and risk assessment. Combines on-chain data, API queries, and multi-source validation to generate actionable investment verdicts.

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。

Autonomous multi-step investigation — deep research carried end-to-end without per-step approval; explicit request only, never for normal feature work.

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。

Skill de pesquisa profunda alimentado por NotebookLM MCP. Conduz pesquisa estruturada em múltiplas fontes (análise de mercado, inteligência competitiva, análise de tendências, pesquisa de prospects) usando Google NotebookLM como mecanismo de pesquisa, depois entrega briefs formatados e artefatos opcionais de estúdio (slides, podcasts de áudio, vídeos, infográficos, relatórios, mapas mentais).

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。

Execute tarefas de pesquisa autônoma que planejam, buscam, leem e sintetizam informações em relatórios abrangentes.

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Shared vocabulary for designing deep modules, interfaces, seams, adapters, leverage, and locality. Use when designing or reshaping module boundaries, making code more testable, or evaluating interface depth.

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。

Android device offensive security, forensic extraction, and mobile app exploitation. Use this whenever the user needs to: break into or bypass the lock screen of an Android phone, extract data from a seized Android device for evidence, analyze or reverse-engineer an APK to find vulnerabilities, intercept communications on an Android device, exploit Android apps via deeplinks, steal crypto wallets or app data, set up rogue WiFi to target a phone, or plan any mobile device investigation or penetration test. Also trigger when the user mentions Android forensics, mobile pentesting, law enforcement phone extraction, device exploitation, WhatsApp or Telegram extraction from a phone, Android app reversing, CVE research for Android, or needing a chain of custody for mobile evidence. Even if the user describes the scenario casually or in another language, if they need to gain access to or pull data from an Android phone, use this skill.

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Research any open-source repository end to end and answer the core question — SHOULD YOU contribute to it? — with a GO / NO-GO / GO-IF verdict backed by evidence: snapshot, momentum, contribution landscape (PR acceptance rates, outside-contributor merge rates, first-timer outcomes), issue health, governance, community sentiment, and a final go/no-go recommendation. Use when the user asks 'should I contribute to X', wants a before-I-contribute due-diligence check on a project, asks whether a project accepts outside PRs, wants contribution-health numbers like PR merge rates or stale-PR counts, or asks for a deep dive on an open-source codebase's health and dynamics before investing time in 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。