[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AEPWPJ59G6C7G7N1QSHWFD":3,"skill-same:01M6AEPWPJ59G6C7G7N1QSHWFD":73},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6AEPWPJ59G6C7G7N1QSHWFD","nosql-expert",null,"Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fnosql-expert\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"nosql","nosql expert","expert","single","design","for","high","scale","systems","modeling","partitions","dynamodb","query","guidance","models","databases","table","first","cassandra","distributed","2026-11-10 10:12:30",false,"---\nname: nosql-expert\ndescription: \"Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.\"\n---\n\n# NoSQL Expert Patterns (Cassandra & DynamoDB)\n\n## Overview\n\nThis skill provides professional mental models and design patterns for **distributed wide-column and key-value stores** (specifically Apache Cassandra and Amazon DynamoDB).\n\nUnlike SQL (where you model data entities), or document stores (like MongoDB), these distributed systems require you to **model your queries first**.\n\n## When to Use\n\n- **Designing for Scale**: Moving beyond simple single-node databases to distributed clusters.\n- **Technology Selection**: Evaluating or using **Cassandra**, **ScyllaDB**, or **DynamoDB**.\n- **Performance Tuning**: Troubleshooting \"hot partitions\" or high latency in existing NoSQL systems.\n- **Microservices**: Implementing \"database-per-service\" patterns where highly optimized reads are required.\n\n## The Mental Shift: SQL vs. Distributed NoSQL\n\n| Feature | SQL (Relational) | Distributed NoSQL (Cassandra\u002FDynamoDB) |\n| :--- | :--- | :--- |\n| **Data modeling** | Model Entities + Relationships | Model **Queries** (Access Patterns) |\n| **Joins** | CPU-intensive, at read time | **Pre-computed** (Denormalized) at write time |\n| **Storage cost** | Expensive (minimize duplication) | Cheap (duplicate data for read speed) |\n| **Consistency** | ACID (Strong) | **BASE (Eventual)** \u002F Tunable |\n| **Scalability** | Vertical (Bigger machine) | **Horizontal** (More nodes\u002Fshards) |\n\n> **The Golden Rule:** In SQL, you design the data model to answer *any* query. In NoSQL, you design the data model to answer *specific* queries efficiently.\n\n## Core Design Patterns\n\n### 1. Query-First Modeling (Access Patterns)\n\nYou typically cannot \"add a query later\" without migration or creating a new table\u002Findex.\n\n**Process:**\n1.  **List all Entities** (User, Order, Product).\n2.  **List all Access Patterns** (\"Get User by Email\", \"Get Orders by User sorted by Date\").\n3.  **Design Table(s)** specifically to serve those patterns with a single lookup.\n\n### 2. The Partition Key is King\n\nData is distributed across physical nodes based on the **Partition Key (PK)**.\n-   **Goal:** Even distribution of data and traffic.\n-   **Anti-Pattern:** Using a low-cardinality PK (e.g., `status=\"active\"` or `gender=\"m\"`) creates **Hot Partitions**, limiting throughput to a single node's capacity.\n-   **Best Practice:** Use high-cardinality keys (User IDs, Device IDs, Composite Keys).\n\n### 3. Clustering \u002F Sort Keys\n\nWithin a partition, data is sorted on disk by the **Clustering Key (Cassandra)** or **Sort Key (DynamoDB)**.\n-   This allows for efficient **Range Queries** (e.g., `WHERE user_id=X AND date > Y`).\n-   It effectively pre-sorts your data for specific retrieval requirements.\n\n### 4. Single-Table Design (Adjacency Lists)\n\n*Primary use: DynamoDB (but concepts apply elsewhere)*\n\nStoring multiple entity types in one table to enable pre-joined reads.\n\n| PK (Partition) | SK (Sort) | Data Fields... |\n| :--- | :--- | :--- |\n| `USER#123` | `PROFILE` | `{ name: \"Ian\", email: \"...\" }` |\n| `USER#123` | `ORDER#998` | `{ total: 50.00, status: \"shipped\" }` |\n| `USER#123` | `ORDER#999` | `{ total: 12.00, status: \"pending\" }` |\n\n-   **Query:** `PK=\"USER#123\"`\n-   **Result:** Fetches User Profile AND all Orders in **one network request**.\n\n### 5. Denormalization & Duplication\n\nDon't be afraid to store the same data in multiple tables to serve different query patterns.\n-   **Table A:** `users_by_id` (PK: uuid)\n-   **Table B:** `users_by_email` (PK: email)\n\n*Trade-off: You must manage data consistency across tables (often using eventual consistency or batch writes).*\n\n## Specific Guidance\n\n### Apache Cassandra \u002F ScyllaDB\n\n-   **Primary Key Structure:** `((Partition Key), Clustering Columns)`\n-   **No Joins, No Aggregates:** Do not try to `JOIN` or `GROUP BY`. Pre-calculate aggregates in a separate counter table.\n-   **Avoid `ALLOW FILTERING`:** If you see this in production, your data model is wrong. It implies a full cluster scan.\n-   **Writes are Cheap:** Inserts and Updates are just appends to the LSM tree. Don't worry about write volume as much as read efficiency.\n-   **Tombstones:** Deletes are expensive markers. Avoid high-velocity delete patterns (like queues) in standard tables.\n\n### AWS DynamoDB\n\n-   **GSI (Global Secondary Index):** Use GSIs to create alternative views of your data (e.g., \"Search Orders by Date\" instead of by User).\n    -   *Note:* GSIs are eventually consistent.\n-   **LSI (Local Secondary Index):** Sorts data differently *within* the same partition. Must be created at table creation time.\n-   **WCU \u002F RCU:** Understand capacity modes. Single-table design helps optimize consumed capacity units.\n-   **TTL:** Use Time-To-Live attributes to automatically expire old data (free delete) without creating tombstones.\n\n## Expert Checklist\n\nBefore finalizing your NoSQL schema:\n\n-   [ ] **Access Pattern Coverage:** Does every query pattern map to a specific table or index?\n-   [ ] **Cardinality Check:** Does the Partition Key have enough unique values to spread traffic evenly?\n-   [ ] **Split Partition Risk:** For any single partition (e.g., a single user's orders), will it grow indefinitely? (If > 10GB, you need to \"shard\" the partition, e.g., `USER#123#2024-01`).\n-   [ ] **Consistency Requirement:** Can the application tolerate eventual consistency for this read pattern?\n\n## Common Anti-Patterns\n\n❌ **Scatter-Gather:** Querying *all* partitions to find one item (Scan).\n❌ **Hot Keys:** Putting all \"Monday\" data into one partition.\n❌ **Relational Modeling:** Creating `Author` and `Book` tables and trying to join them in code. (Instead, embed Book summaries in Author, or duplicate Author info in Books).\n","# NoSQL Expert Patterns (Cassandra & DynamoDB)\n\n## Overview\n\nThis skill provides professional mental models and design patterns for **distributed wide-column and key-value stores** (specifically Apache Cassandra and Amazon DynamoDB).\n\nUnlike SQL (where you model data entities), or document stores (like MongoDB), these distributed systems require you to **model your queries first**.\n\n## When to Use\n\n- **Designing for Scale**: Moving beyond simple single-node databases to distributed clusters.\n- **Technology Selection**: Evaluating or using **Cassandra**, **ScyllaDB**, or **DynamoDB**.\n- **Performance Tuning**: Troubleshooting \"hot partitions\" or high latency in existing NoSQL systems.\n- **Microservices**: Implementing \"database-per-service\" patterns where highly optimized reads are required.\n\n## The Mental Shift: SQL vs. Distributed NoSQL\n\n| Feature | SQL (Relational) | Distributed NoSQL (Cassandra\u002FDynamoDB) |\n| :--- | :--- | :--- |\n| **Data modeling** | Model Entities + Relationships | Model **Queries** (Access Patterns) |\n| **Joins** | CPU-intensive, at read time | **Pre-computed** (Denormalized) at write time |\n| **Storage cost** | Expensive (minimize duplication) | Cheap (duplicate data for read speed) |\n| **Consistency** | ACID (Strong) | **BASE (Eventual)** \u002F Tunable |\n| **Scalability** | Vertical (Bigger machine) | **Horizontal** (More nodes\u002Fshards) |\n\n> **The Golden Rule:** In SQL, you design the data model to answer *any* query. In NoSQL, you design the data model to answer *specific* queries efficiently.\n\n## Core Design Patterns\n\n### 1. Query-First Modeling (Access Patterns)\n\nYou typically cannot \"add a query later\" without migration or creating a new table\u002Findex.\n\n**Process:**\n1.  **List all Entities** (User, Order, Product).\n2.  **List all Access Patterns** (\"Get User by Email\", \"Get Orders by User sorted by Date\").\n3.  **Design Table(s)** specifically to serve those patterns with a single lookup.\n\n### 2. The Partition Key is King\n\nData is distributed across physical nodes based on the **Partition Key (PK)**.\n-   **Goal:** Even distribution of data and traffic.\n-   **Anti-Pattern:** Using a low-cardinality PK (e.g., `status=\"active\"` or `gender=\"m\"`) creates **Hot Partitions**, limiting throughput to a single node's capacity.\n-   **Best Practice:** Use high-cardinality keys (User IDs, Device IDs, Composite Keys).\n\n### 3. Clustering \u002F Sort Keys\n\nWithin a partition, data is sorted on disk by the **Clustering Key (Cassandra)** or **Sort Key (DynamoDB)**.\n-   This allows for efficient **Range Queries** (e.g., `WHERE user_id=X AND date > Y`).\n-   It effectively pre-sorts your data for specific retrieval requirements.\n\n### 4. Single-Table Design (Adjacency Lists)\n\n*Primary use: DynamoDB (but concepts apply elsewhere)*\n\nStoring multiple entity types in one table to enable pre-joined reads.\n\n| PK (Partition) | SK (Sort) | Data Fields... |\n| :--- | :--- | :--- |\n| `USER#123` | `PROFILE` | `{ name: \"Ian\", email: \"...\" }` |\n| `USER#123` | `ORDER#998` | `{ total: 50.00, status: \"shipped\" }` |\n| `USER#123` | `ORDER#999` | `{ total: 12.00, status: \"pending\" }` |\n\n-   **Query:** `PK=\"USER#123\"`\n-   **Result:** Fetches User Profile AND all Orders in **one network request**.\n\n### 5. Denormalization & Duplication\n\nDon't be afraid to store the same data in multiple tables to serve different query patterns.\n-   **Table A:** `users_by_id` (PK: uuid)\n-   **Table B:** `users_by_email` (PK: email)\n\n*Trade-off: You must manage data consistency across tables (often using eventual consistency or batch writes).*\n\n## Specific Guidance\n\n### Apache Cassandra \u002F ScyllaDB\n\n-   **Primary Key Structure:** `((Partition Key), Clustering Columns)`\n-   **No Joins, No Aggregates:** Do not try to `JOIN` or `GROUP BY`. Pre-calculate aggregates in a separate counter table.\n-   **Avoid `ALLOW FILTERING`:** If you see this in production, your data model is wrong. It implies a full cluster scan.\n-   **Writes are Cheap:** Inserts and Updates are just appends to the LSM tree. Don't worry about write volume as much as read efficiency.\n-   **Tombstones:** Deletes are expensive markers. Avoid high-velocity delete patterns (like queues) in standard tables.\n\n### AWS DynamoDB\n\n-   **GSI (Global Secondary Index):** Use GSIs to create alternative views of your data (e.g., \"Search Orders by Date\" instead of by User).\n    -   *Note:* GSIs are eventually consistent.\n-   **LSI (Local Secondary Index):** Sorts data differently *within* the same partition. Must be created at table creation time.\n-   **WCU \u002F RCU:** Understand capacity modes. Single-table design helps optimize consumed capacity units.\n-   **TTL:** Use Time-To-Live attributes to automatically expire old data (free delete) without creating tombstones.\n\n## Expert Checklist\n\nBefore finalizing your NoSQL schema:\n\n-   [ ] **Access Pattern Coverage:** Does every query pattern map to a specific table or index?\n-   [ ] **Cardinality Check:** Does the Partition Key have enough unique values to spread traffic evenly?\n-   [ ] **Split Partition Risk:** For any single partition (e.g., a single user's orders), will it grow indefinitely? (If > 10GB, you need to \"shard\" the partition, e.g., `USER#123#2024-01`).\n-   [ ] **Consistency Requirement:** Can the application tolerate eventual consistency for this read pattern?\n\n## Common Anti-Patterns\n\n❌ **Scatter-Gather:** Querying *all* partitions to find one item (Scan).\n❌ **Hot Keys:** Putting all \"Monday\" data into one partition.\n❌ **Relational Modeling:** Creating `Author` and `Book` tables and trying to join them in code. (Instead, embed Book summaries in Author, or duplicate Author info in Books).\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fnosql-expert",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add davila7\u002Fclaude-code-templates --skill nosql-expert",{"tool":65,"command":66},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill nosql-expert",{"tool":68,"command":69},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill nosql-expert","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fnosql-expert\u002FSKILL.md",true,[],[74,103,130,162,197,221],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":83,"publishedAt":44},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[80],{"categoryNo":81,"categoryName":82,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[84,85,86,87,88,29,89,90,91,92,93,94,95,96,97,98,99,100,101,102],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":110,"publishedAt":44},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[109],{"categoryNo":81,"categoryName":82,"taxonomyType":21,"taxonomyTypeDesc":22},[111,112,113,114,115,116,117,118,119,120,121,92,122,123,124,125,126,127,128,129],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":145,"publishedAt":44},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[136,139,140],{"categoryNo":137,"categoryName":138,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":81,"categoryName":82,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":141,"categoryName":142,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[146,147,148,149,150,151,152,29,153,154,155,156,157,158,159,91,160,161,92,36],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":167,"sourceHtmlUrl":168,"categories":169,"keywords":173,"publishedAt":44},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[170],{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,29,196],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":201,"sourceHtmlUrl":202,"categories":203,"keywords":208,"publishedAt":44},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[204,207],{"categoryNo":205,"categoryName":206,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},[209,210,211,212,213,175,214,184,185,215,216,217,218,29,219,94,220],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":225,"sourceHtmlUrl":226,"categories":227,"keywords":235,"publishedAt":44},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[228,231,232],{"categoryNo":229,"categoryName":230,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":81,"categoryName":82,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":233,"categoryName":234,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000036","内容创作者",[236,237,238,239,240,241,242,243,177,244,187,122,245,246,247,248,249,158,250,92,251,252,253,254,255,100,256],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]