677 件 ・ 関連度順
概要と使いどころ
Universal archivist for personal file archives (Dropbox/B2/Gmail-takeout/local-mount/hard-drive-dump). Filters for high-value content (the user's own writing, ideas, relationships) and surfaces it interactively. REFUSES TO RUN without an explicit gbrain.yml `archive-crawler.scan_paths:` allow-list.
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。
Wins back customers who have quietly stopped buying — finds the ones whose gap has stretched past their own normal rhythm or who are showing churn signals, ranks them by what the relationship was worth, drafts a win-back message per customer in the owner's voice, and sends and logs only what the owner approves. Chains review-reputation, outreach-composer, and crm-autopilot, and keeps the rule that the first touch acknowledges the gap without pitching. Use this whenever the owner is worried about customers drifting away, including phrasings like "who hasn't ordered in a while," "win back my old customers," "which customers have gone quiet," "we lost some regulars," "reach out to the people who stopped calling," or "how do I get them back." Reach for it when the owner names a customer they have not heard from, as readily as for a full win-back campaign.
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。
Health check on a customer account and QBR-ready prep - relationship strength, engagement trend, risk signals, value delivered, and the agenda that makes the review worth their time. Use when the user asks "how healthy is [account]", "customer health check", "prep the [account] QBR", "business review prep", or "are we at risk with [customer]".
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 statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
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。
Draft a professional customer-facing response tailored to the situation and relationship. Use when answering a product question, responding to an escalation or outage, delivering bad news like a delay or won't-fix, declining a feature request, or replying to a billing issue.
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。
Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.
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 create ERD diagrams, normalize database schemas, design table relationships, or plan schema migrations.
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 design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.
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 reviewing, scoring, or auditing third-party SaaS / vendor relationships — running a vendor scorecard with industry tuning, tracking SLA compliance with credit-claim flags, classifying third-party risk across 4 risk vectors, preparing a tier-1 vendor review, or auditing the SaaS portfolio. Forks context so large vendor catalogs (50-500 line items) and SLA logs don't pollute the parent thread. Triggers on "vendor SLA", "vendor scorecard", "third-party risk", "TPRM", "vendor review", "supplier performance", "vendor health check", "renewal review".
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 professional, dark-themed SVG diagrams of any type — architecture diagrams, flowcharts, sequence diagrams, structural diagrams, mind maps, timelines, illustrative/conceptual diagrams, and more. Use this skill whenever the user asks for any kind of technical or conceptual diagram, visualization of a system, process flow, data flow, component relationship, network topology, decision tree, org chart, state machine, or any visual representation of structure/logic/process. Also trigger when the user says "画个图" "画一个架构图" "diagram" "flowchart" "sequence diagram" "draw me a ..." or uploads content and asks to visualize it. Output is always a standalone .svg file.
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。
Set up the owner profile so a personal agent knows who it works for (first-run setup, "get to know me"), or distill colleague, relationship, or celebrity source material into reusable Person Profiles for agents.
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。
Reconstruct a customer relationship from recorded interactions before answering or drafting a response.
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。
Summarizes data lineage graphs on Google Cloud to help users debug data quality issues and understand data provenance for BigQuery and Cloud Storage. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill 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。
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
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 skill should be used for personal operating-system workflows: content creation, voice consistency, relationship lookup, meeting preparation, weekly review, goal tracking, personal brand management, and network 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。
Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / knowledge (rules + NL→SQL pairs), then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
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。
Persistent research knowledge base that accumulates papers, ideas, experiments, claims, and their relationships across the entire research lifecycle. Inspired by Karpathy's LLM Wiki pattern. Use when user says "知识库", "research wiki", "add paper", "wiki query", "查知识库", or wants to build/query a persistent field map.
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。
Embed Mermaid diagrams in generated wiki pages. Use whenever documenting a runtime or request flow, a call sequence, a state machine or lifecycle, a data model or entity relationships, or non-trivial control flow, since these are clearer as a diagram than as prose. Also use when an update run touches a page that already contains a mermaid fence, or a page that contains a text fence a previous run degraded.
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 Attio CRM operations -- search records, query contacts and companies with advanced filters, manage notes, list attributes, and navigate your relationship data -- using natural language through the Composio MCP 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。
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
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 configure Laravel 10+ applications, including creating Eloquent models and relationships, implementing Sanctum authentication, configuring Horizon queues, designing RESTful APIs with API resources, and building reactive interfaces with Livewire. Use when creating Laravel models, setting up queue workers, implementing Sanctum auth flows, building Livewire components, optimising Eloquent queries, or writing Pest/PHPUnit tests for Laravel features.
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。
当用户纠结交朋友/选伴侣/换圈子、感觉被周围人拖累、问「该不该疏远某人」时调用。 核心理念: 五只黑猩猩理论(你的行为由最常接触的5人预测); 同伴是主动选择而非巧合; 只与价值观一致者深交, 远离愤世嫉俗者/愤怒者。 不适用于: 职场必须共事的同事关系(可先设边界)。 Triggers: 朋友/圈子/伴侣/该不该疏远/被拖累/五只黑猩猩/peer/friends/circle/values
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。
当用户在选择合作者/生意模式/人生策略、问「要不要长期投入这段关系/这个项目」「如何积累声誉」时调用。 核心理念: 财富、知识、声誉、关系都遵循复利; 只玩长期正和游戏, 与能想象共事一辈子的人合作, 拒绝短期思维交易。 不适用于: 紧急止损、短期现金周转等必须立即决策的场景。 Triggers: 长期/复利/声誉/合作/信任/compounding/long-term/trust
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。
Quickly determine whether a processing activity needs a PIA, a mandatory GDPR DPIA, or can proceed — surfaces privacy policy conflicts and routes to the right next step. Use when the user asks "does this need a PIA", "triage this feature", "privacy check on X", "is this okay from a privacy perspective", or describes a new data processing activity, product feature, or vendor relationship.
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。