[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CV9G06Y24A4CFARVFF7NRS":3,"skill-same:01M6CV9G06Y24A4CFARVFF7NRS":93},{"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":31,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":55,"updatedAt":56,"install":57,"resources":70},"01M6CV9G06Y24A4CFARVFF7NRS","privacy-by-design",null,"Use when building apps that collect user data. Ensures privacy protections are built in from the start—data minimization, consent, encryption.","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fprivacy-by-design\u002FSKILL.md",[18,23,28],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师",[32,33,34,35,36,37,38,39,40,41,42],"design","privacy by design","privacy","safe","collect","start","data","encryption","user","use","community","2026-11-10 10:12:30",false,"---\nname: privacy-by-design\ndescription: \"Use when building apps that collect user data. Ensures privacy protections are built in from the start—data minimization, consent, encryption.\"\nrisk: safe\nsource: community\ndate_added: \"2026-02-23\"\n---\n\n# Privacy by Design\n\n## Overview\n\nIntegrate privacy protections into software architecture from the beginning, not as an afterthought. This skill applies Privacy by Design principles (GDPR Article 25, Cavoukian's framework) when designing databases, APIs, and user flows. Protects real users' data and builds trust.\n\n## When to Use This Skill\n\n- Use when building apps that collect personal data (names, emails, locations, preferences)\n- Use when designing database schemas, APIs, or authentication flows\n- Use when the user mentions forms, user accounts, analytics, or third-party integrations\n- Use when deploying to production—verify privacy controls before launch\n\n## Legal Frameworks\n\n**GDPR (EU)** — Primary reference. Article 25 mandates \"data protection by design and by default.\" Applies to EU users and often adopted globally.\n\n**CCPA (California)** — Right to know, delete, opt-out of sale. Similar principles: minimize, disclose, allow control.\n\n**LGPD (Brazil)** — Aligned with GDPR. Purpose limitation, necessity, transparency. Applies to Brazil users.\n\nDesign for the strictest framework you target; it often satisfies others.\n\n---\n\n## Core Principles\n\n### 1. Data Minimization\nCollect only what is strictly necessary. Every field needs a documented justification. Avoid \"we might need it later.\"\n\n### 2. Purpose Limitation\nStore the purpose of each data point. Do not reuse data for purposes the user did not consent to.\n\n### 3. Storage Limitation\nDefine retention periods. Implement automated deletion or anonymization when retention expires. Never keep data \"forever\" by default.\n\n### 4. Privacy as Default\nOpt-in for optional collection, not opt-out. Sensitive settings (analytics, marketing) off by default. No pre-checked consent boxes.\n\n### 5. End-to-End Security\nEncrypt at rest and in transit. Use RBAC. Log access to sensitive data for audit.\n\n### 6. Transparency\nDocument what is collected and why. Clear privacy policies. Easy access and deletion for users.\n\n---\n\n## User Rights (GDPR)\n\nEnsure these are implementable from day one:\n\n| Right | What to build |\n|-------|---------------|\n| **Access** | Endpoint or flow to return all user data |\n| **Rectification** | Ability to update\u002Fcorrect data |\n| **Erasure** | Account deletion + data purge (including backups) |\n| **Portability** | Export data in machine-readable format (JSON, CSV) |\n\n---\n\n## Deep Dive: Why It Matters\n\n**Data minimization** — Less data = less breach impact, lower storage cost, simpler compliance. Each field is a liability.\n\n**Purpose limitation** — Reusing data without consent is illegal under GDPR. Document purpose in schema or metadata.\n\n**Retention** — Indefinite storage increases risk and violates GDPR. Define `retention_days` per data type; automate cleanup.\n\n**Logging** — Logs often leak PII. Redact emails, IDs, tokens. Use structured logging with allowlists.\n\n**Third parties** — Every SDK (analytics, crash reporting, ads) may send data elsewhere. Audit dependencies; require consent before loading.\n\n---\n\n## Code Examples\n\n### JavaScript\u002FNode — Minimal User Model\n\n```javascript\n\u002F\u002F BAD: Collecting everything \"just in case\"\nconst user = { email, name, phone, address, birthdate, ipAddress, userAgent, ... };\n\n\u002F\u002F GOOD: Minimal, documented purpose\nconst user = {\n  email,        \u002F\u002F purpose: authentication\n  displayName,  \u002F\u002F purpose: UI display\n  createdAt,    \u002F\u002F purpose: account age\n};\n```\n\n### JavaScript — Consent Before Tracking\n\n```javascript\n\u002F\u002F BAD: Track first, ask later\nanalytics.track(userId, event);\n\n\u002F\u002F GOOD: Check consent first\nif (userConsent.analytics) {\n  analytics.track(userId, event);\n}\n```\n\n### Python — Safe Logging\n\n```python\n# BAD: Logging PII in plain text\nlogger.info(f\"User {user.email} logged in from {request.remote_addr}\")\n\n# GOOD: Redact or hash identifiers\nlogger.info(f\"User {hash_user_id(user.id)} logged in\")\n# Or: logger.info(\"User login\", extra={\"user_id_hash\": hash_id(user.id)})\n```\n\n### SQL — Schema with Purpose and Retention\n\n```sql\n-- GOOD: Document purpose and retention in schema\nCREATE TABLE users (\n  id UUID PRIMARY KEY,\n  email VARCHAR(255) NOT NULL,  -- purpose: auth, retention: account lifetime\n  display_name VARCHAR(100),   -- purpose: UI, retention: account lifetime\n  created_at TIMESTAMPTZ,      -- purpose: audit, retention: 7 years\n  last_login_at TIMESTAMPTZ    -- purpose: security, retention: 90 days\n);\n\n-- Add retention policy (PostgreSQL example)\n-- Schedule job to anonymize\u002Fdelete last_login_at after 90 days\n```\n\n### API — Return Only Needed Fields\n\n```python\n# BAD: Returning full user object\nreturn jsonify(user)  # May include internal fields, hashed passwords\n\n# GOOD: Explicit allowlist\nreturn jsonify({\n    \"id\": user.id,\n    \"email\": user.email,\n    \"displayName\": user.display_name,\n})\n```\n\n---\n\n## Common Pitfalls\n\n| Pitfall | Solution |\n|---------|----------|\n| Logs contain emails, IPs, tokens | Redact PII; use hashed IDs or structured logs |\n| Error messages expose data | Return generic errors to client; log details server-side |\n| Third-party SDKs load before consent | Load analytics\u002Fads only after consent; use consent management |\n| No deletion flow | Design account deletion + data purge from day one |\n| Backups keep data forever | Include backups in retention; encrypt backups |\n| Cookies without consent | Use consent banner; respect Do Not Track where applicable |\n\n---\n\n## Third-Party Audit\n\nBefore adding a dependency that touches user data:\n\n- [ ] What data does it collect or receive?\n- [ ] Where does it send data (servers, countries)?\n- [ ] Is it loaded before or after user consent?\n- [ ] Can we disable it if user opts out?\n- [ ] Does their privacy policy align with ours?\n\n---\n\n## Implementation Checklist\n\nWhen building a feature that touches user data:\n\n- [ ] Is this data necessary? Can we achieve the goal with less?\n- [ ] Do we have explicit consent for this use?\n- [ ] Is it encrypted (at rest and in transit)?\n- [ ] Do we have a retention\u002Fdeletion policy?\n- [ ] Can the user export or delete their data?\n- [ ] Are third-party services disclosed and consented?\n- [ ] Are logs free of PII?\n- [ ] Are backups included in retention policy?\n\n---\n\n## Best Practices\n\n- ✅ Ask \"do we need this?\" for every new data field\n- ✅ Design deletion and export flows from day one\n- ✅ Use hashing or tokenization for sensitive identifiers when possible\n- ✅ Document purpose and retention in schema or metadata\n- ❌ Don't log passwords, tokens, or PII in plain text\n- ❌ Don't share data with third parties without explicit consent\n- ❌ Don't assume \"we'll add privacy later\"—it rarely happens\n- ❌ Don't expose stack traces or internal errors to clients\n\n---\n\n## When to Use\nThis skill is applicable when building software that collects, stores, or processes personal data. Apply it proactively during design and implementation.\n","# Privacy by Design\n\n## Overview\n\nIntegrate privacy protections into software architecture from the beginning, not as an afterthought. This skill applies Privacy by Design principles (GDPR Article 25, Cavoukian's framework) when designing databases, APIs, and user flows. Protects real users' data and builds trust.\n\n## When to Use This Skill\n\n- Use when building apps that collect personal data (names, emails, locations, preferences)\n- Use when designing database schemas, APIs, or authentication flows\n- Use when the user mentions forms, user accounts, analytics, or third-party integrations\n- Use when deploying to production—verify privacy controls before launch\n\n## Legal Frameworks\n\n**GDPR (EU)** — Primary reference. Article 25 mandates \"data protection by design and by default.\" Applies to EU users and often adopted globally.\n\n**CCPA (California)** — Right to know, delete, opt-out of sale. Similar principles: minimize, disclose, allow control.\n\n**LGPD (Brazil)** — Aligned with GDPR. Purpose limitation, necessity, transparency. Applies to Brazil users.\n\nDesign for the strictest framework you target; it often satisfies others.\n\n---\n\n## Core Principles\n\n### 1. Data Minimization\nCollect only what is strictly necessary. Every field needs a documented justification. Avoid \"we might need it later.\"\n\n### 2. Purpose Limitation\nStore the purpose of each data point. Do not reuse data for purposes the user did not consent to.\n\n### 3. Storage Limitation\nDefine retention periods. Implement automated deletion or anonymization when retention expires. Never keep data \"forever\" by default.\n\n### 4. Privacy as Default\nOpt-in for optional collection, not opt-out. Sensitive settings (analytics, marketing) off by default. No pre-checked consent boxes.\n\n### 5. End-to-End Security\nEncrypt at rest and in transit. Use RBAC. Log access to sensitive data for audit.\n\n### 6. Transparency\nDocument what is collected and why. Clear privacy policies. Easy access and deletion for users.\n\n---\n\n## User Rights (GDPR)\n\nEnsure these are implementable from day one:\n\n| Right | What to build |\n|-------|---------------|\n| **Access** | Endpoint or flow to return all user data |\n| **Rectification** | Ability to update\u002Fcorrect data |\n| **Erasure** | Account deletion + data purge (including backups) |\n| **Portability** | Export data in machine-readable format (JSON, CSV) |\n\n---\n\n## Deep Dive: Why It Matters\n\n**Data minimization** — Less data = less breach impact, lower storage cost, simpler compliance. Each field is a liability.\n\n**Purpose limitation** — Reusing data without consent is illegal under GDPR. Document purpose in schema or metadata.\n\n**Retention** — Indefinite storage increases risk and violates GDPR. Define `retention_days` per data type; automate cleanup.\n\n**Logging** — Logs often leak PII. Redact emails, IDs, tokens. Use structured logging with allowlists.\n\n**Third parties** — Every SDK (analytics, crash reporting, ads) may send data elsewhere. Audit dependencies; require consent before loading.\n\n---\n\n## Code Examples\n\n### JavaScript\u002FNode — Minimal User Model\n\n```javascript\n\u002F\u002F BAD: Collecting everything \"just in case\"\nconst user = { email, name, phone, address, birthdate, ipAddress, userAgent, ... };\n\n\u002F\u002F GOOD: Minimal, documented purpose\nconst user = {\n  email,        \u002F\u002F purpose: authentication\n  displayName,  \u002F\u002F purpose: UI display\n  createdAt,    \u002F\u002F purpose: account age\n};\n```\n\n### JavaScript — Consent Before Tracking\n\n```javascript\n\u002F\u002F BAD: Track first, ask later\nanalytics.track(userId, event);\n\n\u002F\u002F GOOD: Check consent first\nif (userConsent.analytics) {\n  analytics.track(userId, event);\n}\n```\n\n### Python — Safe Logging\n\n```python\n# BAD: Logging PII in plain text\nlogger.info(f\"User {user.email} logged in from {request.remote_addr}\")\n\n# GOOD: Redact or hash identifiers\nlogger.info(f\"User {hash_user_id(user.id)} logged in\")\n# Or: logger.info(\"User login\", extra={\"user_id_hash\": hash_id(user.id)})\n```\n\n### SQL — Schema with Purpose and Retention\n\n```sql\n-- GOOD: Document purpose and retention in schema\nCREATE TABLE users (\n  id UUID PRIMARY KEY,\n  email VARCHAR(255) NOT NULL,  -- purpose: auth, retention: account lifetime\n  display_name VARCHAR(100),   -- purpose: UI, retention: account lifetime\n  created_at TIMESTAMPTZ,      -- purpose: audit, retention: 7 years\n  last_login_at TIMESTAMPTZ    -- purpose: security, retention: 90 days\n);\n\n-- Add retention policy (PostgreSQL example)\n-- Schedule job to anonymize\u002Fdelete last_login_at after 90 days\n```\n\n### API — Return Only Needed Fields\n\n```python\n# BAD: Returning full user object\nreturn jsonify(user)  # May include internal fields, hashed passwords\n\n# GOOD: Explicit allowlist\nreturn jsonify({\n    \"id\": user.id,\n    \"email\": user.email,\n    \"displayName\": user.display_name,\n})\n```\n\n---\n\n## Common Pitfalls\n\n| Pitfall | Solution |\n|---------|----------|\n| Logs contain emails, IPs, tokens | Redact PII; use hashed IDs or structured logs |\n| Error messages expose data | Return generic errors to client; log details server-side |\n| Third-party SDKs load before consent | Load analytics\u002Fads only after consent; use consent management |\n| No deletion flow | Design account deletion + data purge from day one |\n| Backups keep data forever | Include backups in retention; encrypt backups |\n| Cookies without consent | Use consent banner; respect Do Not Track where applicable |\n\n---\n\n## Third-Party Audit\n\nBefore adding a dependency that touches user data:\n\n- [ ] What data does it collect or receive?\n- [ ] Where does it send data (servers, countries)?\n- [ ] Is it loaded before or after user consent?\n- [ ] Can we disable it if user opts out?\n- [ ] Does their privacy policy align with ours?\n\n---\n\n## Implementation Checklist\n\nWhen building a feature that touches user data:\n\n- [ ] Is this data necessary? Can we achieve the goal with less?\n- [ ] Do we have explicit consent for this use?\n- [ ] Is it encrypted (at rest and in transit)?\n- [ ] Do we have a retention\u002Fdeletion policy?\n- [ ] Can the user export or delete their data?\n- [ ] Are third-party services disclosed and consented?\n- [ ] Are logs free of PII?\n- [ ] Are backups included in retention policy?\n\n---\n\n## Best Practices\n\n- ✅ Ask \"do we need this?\" for every new data field\n- ✅ Design deletion and export flows from day one\n- ✅ Use hashing or tokenization for sensitive identifiers when possible\n- ✅ Document purpose and retention in schema or metadata\n- ❌ Don't log passwords, tokens, or PII in plain text\n- ❌ Don't share data with third parties without explicit consent\n- ❌ Don't assume \"we'll add privacy later\"—it rarely happens\n- ❌ Don't expose stack traces or internal errors to clients\n\n---\n\n## When to Use\nThis skill is applicable when building software that collects, stores, or processes personal data. Apply it proactively during design and implementation.\n",{"name":5,"risk":35,"source":42,"date_added":48,"description":7},"2026-02-23","main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fprivacy-by-design",0,"https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant","https:\u002F\u002Fgithub.com\u002FSam-06060","Mobile Application Developer | Kotlin | Flutter, KMP, Swfit, Android,Swift UI | Building at Saar It Resources | Creator of Jarvis (AI Assistant)","2026-06-23 05:13:38",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill privacy-by-design",{"tool":63,"command":64},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill privacy-by-design",{"tool":66,"command":67},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill privacy-by-design","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fprivacy-by-design\u002FSKILL.md",true,[71,77,81,85,89],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"README.md","REFERENCE","参考文档",839,"https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fprivacy-by-design\u002FREADME.md",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fesm-c-api.md",15715,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002Freferences\u002Fesm-c-api.md",{"path":82,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fesm3-api.md",11852,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002Freferences\u002Fesm3-api.md",{"path":86,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fforge-api.md",17615,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002Freferences\u002Fforge-api.md",{"path":90,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fworkflows.md",20667,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002Freferences\u002Fworkflows.md",[94,105,121,148,167,193],{"skillNo":95,"name":96,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":102,"publishedAt":43},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[99],{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[103,104],"Test Skill","test",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":111,"publishedAt":43},"01M6GDRQ40HX22MA6XWC31RHHK","zustand-store-ts","Create Zustand stores following established patterns with proper TypeScript types and middleware.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzustand-store-ts\u002FSKILL.md",[],[112,113,114,115,116,117,118,42,119,120],"zustand store ts","zustand","store","with","types","middleware","patterns","create","typescript",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":130,"publishedAt":43},"01M6GDRKEA0WE670SPFWX5GKCN","zoom-automation","Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoom-automation\u002FSKILL.md",[127],{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[131,132,133,134,135,136,137,138,139,140,141,142,143,144,42,145,146,147],"automation","zoom automation","zoom","first","Tools","search","MCP","via","meeting","automate","critical","creation","webinars","composio","recordings","management","tracking",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":155,"publishedAt":43},"01M6GDRFS1452PC71JQ38S24HR","zoho-crm-automation","Automate Zoho CRM tasks via Rube MCP (Composio): create\u002Fupdate records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoho-crm-automation\u002FSKILL.md",[154],{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},[156,131,157,158,159,160,161,42,136,162,144,163,141,140,135,164,119,165,137,166],"crm automation","zoho crm","crm","zoho crm automation","zoho","leads","contacts","records","update","tasks","manage",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":176,"publishedAt":43},"01M6GDRCKQBAVTGHX5XBH26KPZ","zod-validation-expert","Expert in Zod — TypeScript-first schema validation. Covers parsing, custom errors, refinements, type inference, and integration with React Hook Form, Next.js, and tRPC.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzod-validation-expert\u002FSKILL.md",[173],{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[177,178,179,180,181,120,35,182,183,184,185,186,115,187,188,189,190,42,191,192],"expert","zod","zod validation expert","validation","react","parsing","errors","schema validation","react hook form","hook","trpc","form","type","next","schema","Next.js",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":200,"publishedAt":43},"01M6GDR658B08K4MY0RWD4A6EH","zeroize-audit","Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C\u002FC++\u002FRust code handling secrets, keys, passwords, or other sensitive data.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzeroize-audit\u002FSKILL.md",[199],{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},[201,202,203,204,205,206,207,208,42,209,210,211,212,213,38,214,215,216,217,137],"zeroize audit","audit","write","passwords","get","project","c++","activate","assembly","read","Bash","secrets","analysis","flow","verification","code","rust"]