[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M730V3GP7AXTP5F60JB047K9":3,"skill-same:01M730V3GP7AXTP5F60JB047K9":51},{"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":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":32,"ownerHtmlUrl":33,"ownerBlogUrl":34,"ownerDescription":35,"updatedAt":36,"install":37,"resources":50},"01M730V3GP7AXTP5F60JB047K9","book--domain-driven-design-distilled--nano",null,"DDD Distilled (Vaughn Vernon) — Minimal rules — essential one-liners only. Use when asked to apply DDD Distilled principles or review code against DDD Distilled standards.","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fbook--domain-driven-design-distilled--nano\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: book--domain-driven-design-distilled--nano\ndescription: >-\n  DDD Distilled (Vaughn Vernon) — Minimal rules — essential one-liners only. Use when asked to apply DDD Distilled principles or review code against DDD Distilled standards.\norigin: \"github.com\u002Fciembor\u002Fagent-rules-books (MIT)\"\nlicense: MIT\nversion: \"1.0.0\"\ncompatibility: \"yana-ai >= 0.14.0\"\n---\n\n# OBEY Domain-Driven Design Distilled by Vaughn Vernon\n\n## When to use\n\nUse as compact DDD guidance when business language, context ownership, invariants, or integration meaning can affect the code.\n\n## Primary bias to correct\n\nDDD is selective modeling for real business complexity, not decorative layers, framework shape, or blanket tactical ceremony.\n\n## Decision rules\n\n- Start from business capability, subdomain type, Bounded Context, and local Ubiquitous Language before tactical patterns.\n- Invest most design effort in the Core Domain; keep Supporting, Generic, CRUD, and mainly technical areas simpler.\n- Put every meaningful model inside one explicit Bounded Context that owns its language, rules, semantics, code, tests, and integration contracts.\n- Translate between contexts when meanings differ; never share domain classes or terms whose meanings diverge.\n- Choose context relationships and integration mechanisms deliberately, including Conformist or Anticorruption Layer when foreign meaning is involved.\n- Keep integration contracts separate from internal models, and do not expose Aggregate internals through REST or transport payloads.\n- Use Entities for identity, Value Objects for validated meaning, Aggregates for small invariant boundaries, and Domain Events for meaningful past-tense facts.\n- Modify one Aggregate per transaction by default; reference other Aggregates by identity and use eventual consistency when the business allows it.\n- Keep business decisions in the domain model; Application Services coordinate use cases.\n- Keep frameworks, persistence, transport, and external schemas out of the domain model.\n- Use scenarios, acceptance tests, Event Storming, spikes, and domain-expert walkthroughs to learn quickly without hiding modeling debt.\n\n## Trigger rules\n\n- When language is fuzzy or overloaded, sharpen terms before coding.\n- When one model absorbs unrelated concerns, redraw the subdomain or context boundary.\n- When foreign models, schemas, APIs, or frameworks drive domain shape, add boundary translation.\n- When a transaction wants many roots or a large graph, revisit Aggregate boundaries and consistency timing.\n- When services, controllers, setters, flags, or primitives carry business decisions, expose the missing domain concept.\n- When events are vague, command-like, or noisy field changes, redesign them as specific business facts.\n\n## Final checklist\n\n- Clear subdomain?\n- Clear context?\n- Clear language?\n- Clear translation?\n- Clear invariant boundary?\n- Clear modeling debt?\n","# OBEY Domain-Driven Design Distilled by Vaughn Vernon\n\n## When to use\n\nUse as compact DDD guidance when business language, context ownership, invariants, or integration meaning can affect the code.\n\n## Primary bias to correct\n\nDDD is selective modeling for real business complexity, not decorative layers, framework shape, or blanket tactical ceremony.\n\n## Decision rules\n\n- Start from business capability, subdomain type, Bounded Context, and local Ubiquitous Language before tactical patterns.\n- Invest most design effort in the Core Domain; keep Supporting, Generic, CRUD, and mainly technical areas simpler.\n- Put every meaningful model inside one explicit Bounded Context that owns its language, rules, semantics, code, tests, and integration contracts.\n- Translate between contexts when meanings differ; never share domain classes or terms whose meanings diverge.\n- Choose context relationships and integration mechanisms deliberately, including Conformist or Anticorruption Layer when foreign meaning is involved.\n- Keep integration contracts separate from internal models, and do not expose Aggregate internals through REST or transport payloads.\n- Use Entities for identity, Value Objects for validated meaning, Aggregates for small invariant boundaries, and Domain Events for meaningful past-tense facts.\n- Modify one Aggregate per transaction by default; reference other Aggregates by identity and use eventual consistency when the business allows it.\n- Keep business decisions in the domain model; Application Services coordinate use cases.\n- Keep frameworks, persistence, transport, and external schemas out of the domain model.\n- Use scenarios, acceptance tests, Event Storming, spikes, and domain-expert walkthroughs to learn quickly without hiding modeling debt.\n\n## Trigger rules\n\n- When language is fuzzy or overloaded, sharpen terms before coding.\n- When one model absorbs unrelated concerns, redraw the subdomain or context boundary.\n- When foreign models, schemas, APIs, or frameworks drive domain shape, add boundary translation.\n- When a transaction wants many roots or a large graph, revisit Aggregate boundaries and consistency timing.\n- When services, controllers, setters, flags, or primitives carry business decisions, expose the missing domain concept.\n- When events are vague, command-like, or noisy field changes, redesign them as specific business facts.\n\n## Final checklist\n\n- Clear subdomain?\n- Clear context?\n- Clear language?\n- Clear translation?\n- Clear invariant boundary?\n- Clear modeling debt?\n",{"name":5,"origin":24,"license":15,"version":25,"description":7,"compatibility":26},"github.com\u002Fciembor\u002Fagent-rules-books (MIT)","1.0.0","yana-ai >= 0.14.0","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa",".claude\u002Fskills\u002Fbook--domain-driven-design-distilled--nano",2,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-14 13:24:09",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill book--domain-driven-design-distilled--nano",{"tool":43,"command":44},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill book--domain-driven-design-distilled--nano",{"tool":46,"command":47},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill book--domain-driven-design-distilled--nano","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fbook--domain-driven-design-distilled--nano\u002FSKILL.md",true,[],[52,59,67,75,83,91],{"skillNo":53,"name":54,"nameZh":6,"description":55,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":56,"categories":57,"keywords":58,"publishedAt":19},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":6,"sourceHtmlUrl":64,"categories":65,"keywords":66,"publishedAt":19},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":71,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":19},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":79,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":19},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":87,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]