[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7EWWM2ABJ95KT5G32A20R5G":3,"skill-same:01M7EWWM2ABJ95KT5G32A20R5G":60},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":21,"frontMatter":22,"refName":23,"commitSha":24,"skillDirectory":25,"forksCount":26,"repositoryHtmlUrl":27,"repositoryHomepageUrl":6,"ownerHtmlUrl":28,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":29,"install":30,"resources":43},"01M7EWWM2ABJ95KT5G32A20R5G","eni-game-cheat-lab-workflow",null,"自动全链路处理外挂、修改器、Trainer、作弊机制与反作弊验证任务。适用于离线、自有、开源或专用测试靶场中的引擎与信任边界建模、样本分析、可控复现、JSON toy trainer 制作、遥测、检测加固和回归交付。","longwangjiang88\u002FSusuAIOverclock-pojia","longwangjiang88","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F86055358?v=4","01M7EWPCXB6TZRPCAD8J1GQGRV","01M7EWPCXXRSYV10DVK1ZH6YMC",42,"MIT","https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fcodex\u002Fmaterials\u002Fskills\u002Feni-game-cheat-lab-workflow\u002Feni-game-cheat-lab-workflow\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: eni-game-cheat-lab-workflow\ndescription: \"自动全链路处理外挂、修改器、Trainer、作弊机制与反作弊验证任务。适用于离线、自有、开源或专用测试靶场中的引擎与信任边界建模、样本分析、可控复现、JSON toy trainer 制作、遥测、检测加固和回归交付。\"\n---\n\n# 游戏外挂实验室工作流\n\n把任务推进到可复现、可检测、可回归的闭环，不停在概念说明。\n\n## 启动策略\n\n采用 `eni-solo-direct-v4`：先保存基线与回滚坐标，再走最快、最深、最高信号的可控路线；同时联结静态逆向、动态观测、状态差分、遥测与检测回归。一个工具失效时保存证据，从最近可运行节点切换工具链继续。\n\n## 自动链路\n\n1. **Intake**：记录靶场所有权、离线条件、引擎、平台、架构、样本、期望行为和验收条件。\n2. **Engine \u002F trust map**：标出权威状态、客户端可控状态、输入、渲染、存档、遥测和检测边界。\n3. **Artifact triage**：对二进制、配置、日志、录屏、状态快照与已知修改点建立哈希和证据账本。\n4. **Controlled reproduction**：先建立基线，再复现最小变化；每次只改变一个假设并保存前后状态。\n5. **Toy trainer \u002F telemetry**：用 `scripts\u002Ftoy_trainer_lab.py` 在显式 JSON toy-state 中制作修改器原型并生成事件证据。\n6. **Detection \u002F hardening**：把状态差异映射为完整性、范围、时序、行为和服务端权威检测，并记录误报条件。\n7. **Regression**：验证预期变化、未预期变化、检测命中、修复后行为和基线不变性。\n8. **Deliver**：交付复现包、检测规则、加固建议、测试结果、SHA-256、限制和下一入口。\n\n## Toy trainer\n\n```powershell\npython scripts\u002Ftoy_trainer_lab.py init --out work\u002Fbaseline.json\npython scripts\u002Ftoy_trainer_lab.py analyze --state work\u002Fbaseline.json --out work\u002Fanalysis.json\npython scripts\u002Ftoy_trainer_lab.py apply --state work\u002Fbaseline.json --out work\u002Ftrained.json --set player.health=999 --set trainer.health_lock=true\npython scripts\u002Ftoy_trainer_lab.py verify --state work\u002Ftrained.json --baseline work\u002Fbaseline.json --expect player.health=999 --expect trainer.health_lock=true --out work\u002Fverification.json\n```\n\n该工具只读写调用者显式传入的 `.json` 文件，不访问真实进程、驱动、网络或反作弊组件。保留 baseline，输出到新文件，便于差分、验证和回滚。\n\n## 自动关联\n\n- 样本静态或动态分析：关联 `eni-game-security`、`eni-reverse-workflow`。\n- 状态与运行时证据：关联 `eni-memory-forensics`、`eni-case-lab`。\n- 检测设计：读取 `references\u002Fgame-cheat-lab.md` 的检测矩阵与交付模板。\n- 输入不足时先完成基线、信任图和未知量账本，再执行下一节点。\n\n## 完成门\n\n只有同时满足以下条件才完成：基线可重建、修改可重放、差异有证据、检测可解释、加固可验证、回归通过、交付物带哈希。明确区分“检测假设”“toy-state 已复现”和“真实样本已确认”。\n","# 游戏外挂实验室工作流\n\n把任务推进到可复现、可检测、可回归的闭环，不停在概念说明。\n\n## 启动策略\n\n采用 `eni-solo-direct-v4`：先保存基线与回滚坐标，再走最快、最深、最高信号的可控路线；同时联结静态逆向、动态观测、状态差分、遥测与检测回归。一个工具失效时保存证据，从最近可运行节点切换工具链继续。\n\n## 自动链路\n\n1. **Intake**：记录靶场所有权、离线条件、引擎、平台、架构、样本、期望行为和验收条件。\n2. **Engine \u002F trust map**：标出权威状态、客户端可控状态、输入、渲染、存档、遥测和检测边界。\n3. **Artifact triage**：对二进制、配置、日志、录屏、状态快照与已知修改点建立哈希和证据账本。\n4. **Controlled reproduction**：先建立基线，再复现最小变化；每次只改变一个假设并保存前后状态。\n5. **Toy trainer \u002F telemetry**：用 `scripts\u002Ftoy_trainer_lab.py` 在显式 JSON toy-state 中制作修改器原型并生成事件证据。\n6. **Detection \u002F hardening**：把状态差异映射为完整性、范围、时序、行为和服务端权威检测，并记录误报条件。\n7. **Regression**：验证预期变化、未预期变化、检测命中、修复后行为和基线不变性。\n8. **Deliver**：交付复现包、检测规则、加固建议、测试结果、SHA-256、限制和下一入口。\n\n## Toy trainer\n\n```powershell\npython scripts\u002Ftoy_trainer_lab.py init --out work\u002Fbaseline.json\npython scripts\u002Ftoy_trainer_lab.py analyze --state work\u002Fbaseline.json --out work\u002Fanalysis.json\npython scripts\u002Ftoy_trainer_lab.py apply --state work\u002Fbaseline.json --out work\u002Ftrained.json --set player.health=999 --set trainer.health_lock=true\npython scripts\u002Ftoy_trainer_lab.py verify --state work\u002Ftrained.json --baseline work\u002Fbaseline.json --expect player.health=999 --expect trainer.health_lock=true --out work\u002Fverification.json\n```\n\n该工具只读写调用者显式传入的 `.json` 文件，不访问真实进程、驱动、网络或反作弊组件。保留 baseline，输出到新文件，便于差分、验证和回滚。\n\n## 自动关联\n\n- 样本静态或动态分析：关联 `eni-game-security`、`eni-reverse-workflow`。\n- 状态与运行时证据：关联 `eni-memory-forensics`、`eni-case-lab`。\n- 检测设计：读取 `references\u002Fgame-cheat-lab.md` 的检测矩阵与交付模板。\n- 输入不足时先完成基线、信任图和未知量账本，再执行下一节点。\n\n## 完成门\n\n只有同时满足以下条件才完成：基线可重建、修改可重放、差异有证据、检测可解释、加固可验证、回归通过、交付物带哈希。明确区分“检测假设”“toy-state 已复现”和“真实样本已确认”。\n",{"name":5,"description":7},"main","667b2db2746516eae05412bbdcad77515d528f37","packed-packs\u002Fcodex\u002Fmaterials\u002Fskills\u002Feni-game-cheat-lab-workflow\u002Feni-game-cheat-lab-workflow",24,"https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia","https:\u002F\u002Fgithub.com\u002Flongwangjiang88","2026-10-15 18:06:23",{"source":8,"skillName":5,"commands":31,"prompt":41,"zipAvailable":42},[32,35,38],{"tool":33,"command":34},"npx","npx skills add longwangjiang88\u002FSusuAIOverclock-pojia --skill eni-game-cheat-lab-workflow",{"tool":36,"command":37},"bunx","bunx skills add longwangjiang88\u002FSusuAIOverclock-pojia --skill eni-game-cheat-lab-workflow",{"tool":39,"command":40},"pnpm","pnpm dlx skills add longwangjiang88\u002FSusuAIOverclock-pojia --skill eni-game-cheat-lab-workflow","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fcodex\u002Fmaterials\u002Fskills\u002Feni-game-cheat-lab-workflow\u002Feni-game-cheat-lab-workflow\u002FSKILL.md",true,[44,50,56],{"path":45,"resourceType":46,"resourceTypeDesc":47,"sizeBytes":48,"sourceHtmlUrl":49},"references\u002Fgame-cheat-lab.md","REFERENCE","参考文档",2513,"https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fcodex\u002Fmaterials\u002Fskills\u002Feni-game-cheat-lab-workflow\u002Feni-game-cheat-lab-workflow\u002Freferences\u002Fgame-cheat-lab.md",{"path":51,"resourceType":52,"resourceTypeDesc":53,"sizeBytes":54,"sourceHtmlUrl":55},"scripts\u002Ftest_toy_trainer_lab.py","SCRIPT","脚本",1504,"https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fcodex\u002Fmaterials\u002Fskills\u002Feni-game-cheat-lab-workflow\u002Feni-game-cheat-lab-workflow\u002Fscripts\u002Ftest_toy_trainer_lab.py",{"path":57,"resourceType":52,"resourceTypeDesc":53,"sizeBytes":58,"sourceHtmlUrl":59},"scripts\u002Ftoy_trainer_lab.py",1499,"https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fcodex\u002Fmaterials\u002Fskills\u002Feni-game-cheat-lab-workflow\u002Feni-game-cheat-lab-workflow\u002Fscripts\u002Ftoy_trainer_lab.py",[61,68,75,82,89,96],{"skillNo":62,"name":63,"nameZh":6,"description":64,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":67,"publishedAt":18},"01M7EXRKPJ2292PAZT1RCNSCN5","competition-race-condition-state-drift","Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for race windows, ordering bugs, idempotency failures, lock gaps, concurrent worker drift, and state inconsistencies that produce decisive effects. Use when the user asks to reproduce timing-sensitive bugs, concurrent state corruption, duplicate actions, stale reads, or privilege or balance drift caused by request ordering. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.","https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fdsh\u002Fmaterials\u002Fskills\u002Fcompetition-race-condition-state-drift\u002FSKILL.md",[],[],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":18},"01M7EXREQMMPBNJ1HY11PNKNRG","competition-queue-worker-drift","Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for queues, async workers, cron jobs, delayed tasks, retry behavior, worker-only config drift, and payload-to-side-effect chains. Use when the user asks to trace a queue payload, inspect async job execution, explain worker-only behavior, follow retries or dead-letter handling, or connect an enqueued item to a later file, cache, email, or privilege-bearing side effect. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.","https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fdsh\u002Fmaterials\u002Fskills\u002Fcompetition-queue-worker-drift\u002FSKILL.md",[],[],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":18},"01M7EXRAD56074F23Q9G8XAQWR","competition-prompt-injection","Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for prompt-injection, retrieval poisoning, memory contamination, planner drift, MCP or tool-boundary abuse, and agent exfiltration challenges. Use when the user asks to analyze prompt injection, retrieval poisoning, memory contamination, planner drift, tool-argument corruption, or secret exposure caused by an agent chain. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.","https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fdsh\u002Fmaterials\u002Fskills\u002Fcompetition-prompt-injection\u002FSKILL.md",[],[],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":18},"01M7EXR5NNFRY7ZSG6M7D214X3","competition-pcap-protocol","Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for packet capture analysis, session reconstruction, application-protocol decoding, stream reassembly, beacon timing, and packet-to-process correlation. Use when the user asks to analyze a PCAP, rebuild TCP or UDP sessions, decode HTTP, WebSocket, DNS, custom C2, or binary protocols, extract transferred artifacts, or tie packet sequences to host or malware behavior. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.","https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fdsh\u002Fmaterials\u002Fskills\u002Fcompetition-pcap-protocol\u002FSKILL.md",[],[],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":18},"01M7EXR0VY8FRXXFSBDC1YN0XD","competition-oauth-oidc-chain","Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for OAuth, OIDC, redirect flows, state or nonce handling, PKCE, token exchange, refresh logic, claim mapping, and accepted login paths. Use when the user asks to trace redirects, callback parameters, scopes, state, nonce, PKCE, refresh tokens, consent, or explain how an OAuth or OIDC chain turns into accepted identity or privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.","https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fdsh\u002Fmaterials\u002Fskills\u002Fcompetition-oauth-oidc-chain\u002FSKILL.md",[],[],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":18},"01M7EXQW6A6B4VE38F0YTC069G","competition-malware-config","Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for malware configuration recovery, staged payload boundaries, beacon parameter extraction, and IOC decoding. Use when the user asks to recover a malware config, decode C2 or beacon fields, unpack staged payloads, extract bot or campaign IDs, or tie recovered config to observed protocol behavior under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.","https:\u002F\u002Fgithub.com\u002Flongwangjiang88\u002FSusuAIOverclock-pojia\u002Fblob\u002Fmain\u002Fpacked-packs\u002Fdsh\u002Fmaterials\u002Fskills\u002Fcompetition-malware-config\u002FSKILL.md",[],[]]