[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73F6EDRNET6EE16PSRSAX9B":3,"skill-same:01M73F6EDRNET6EE16PSRSAX9B":49},{"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":25,"commitSha":26,"skillDirectory":27,"forksCount":28,"repositoryHtmlUrl":29,"repositoryHomepageUrl":30,"ownerHtmlUrl":31,"ownerBlogUrl":32,"ownerDescription":33,"updatedAt":34,"install":35,"resources":48},"01M73F6EDRNET6EE16PSRSAX9B","prediction-market-risk-review",null,"Review prediction-market, basket, oracle, and trading-agent workflows for compliance, safety, data-quality, privacy, and execution risk. Use before any workflow handles venue auth, user portfolio data, API keys, or trade planning.","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fprediction-market-risk-review\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: prediction-market-risk-review\ndescription: Review prediction-market, basket, oracle, and trading-agent workflows for compliance, safety, data-quality, privacy, and execution risk. Use before any workflow handles venue auth, user portfolio data, API keys, or trade planning.\norigin: ECC\n---\n\n# Prediction Market Risk Review\n\nUse this skill before a prediction-market workflow touches user financial\ncontext, venue authentication, portfolio data, automation, or execution-capable\ntools.\n\n## Review Gates\n\n### Advice Boundary\n\n- Confirm the output is informational.\n- Remove buy\u002Fsell\u002Fhold\u002Fsize recommendations.\n- Keep manual user decision points explicit.\n\n### Venue And Regulatory Boundary\n\n- Identify venue terms, geography restrictions, account limits, and API rules.\n- Flag betting, derivatives, securities, or commodities ambiguity for legal\n  review when relevant.\n- Do not bypass venue restrictions or rate limits.\n\n### Data Quality\n\n- Check market liquidity, spread, resolution rules, stale prices, and source\n  timestamps.\n- Separate public venue data from Itô gated data.\n- Do not mix public and private sources without labels.\n\n### Security\n\n- Do not request or store private keys, seed phrases, or passwords.\n- Keep `ITO_API_KEY` and venue API keys out of logs and docs.\n- Use read-only scopes by default.\n- Require circuit breakers, spend limits, dry runs, and human approval before\n  any private implementation adds execution.\n\n### Privacy\n\n- Minimize user portfolio, financial, and knowledge-base data.\n- Redact private sources in public artifacts.\n- Preserve only the fields needed for the review.\n\n## Output Contract\n\nReturn:\n\n1. scope reviewed\n2. pass\u002Fwarn\u002Ffail findings\n3. blocked actions\n4. required mitigations\n5. safe next step\n\nIf any execution-capable step is requested, require a separate implementation\nplan and explicit user approval.\n","# Prediction Market Risk Review\n\nUse this skill before a prediction-market workflow touches user financial\ncontext, venue authentication, portfolio data, automation, or execution-capable\ntools.\n\n## Review Gates\n\n### Advice Boundary\n\n- Confirm the output is informational.\n- Remove buy\u002Fsell\u002Fhold\u002Fsize recommendations.\n- Keep manual user decision points explicit.\n\n### Venue And Regulatory Boundary\n\n- Identify venue terms, geography restrictions, account limits, and API rules.\n- Flag betting, derivatives, securities, or commodities ambiguity for legal\n  review when relevant.\n- Do not bypass venue restrictions or rate limits.\n\n### Data Quality\n\n- Check market liquidity, spread, resolution rules, stale prices, and source\n  timestamps.\n- Separate public venue data from Itô gated data.\n- Do not mix public and private sources without labels.\n\n### Security\n\n- Do not request or store private keys, seed phrases, or passwords.\n- Keep `ITO_API_KEY` and venue API keys out of logs and docs.\n- Use read-only scopes by default.\n- Require circuit breakers, spend limits, dry runs, and human approval before\n  any private implementation adds execution.\n\n### Privacy\n\n- Minimize user portfolio, financial, and knowledge-base data.\n- Redact private sources in public artifacts.\n- Preserve only the fields needed for the review.\n\n## Output Contract\n\nReturn:\n\n1. scope reviewed\n2. pass\u002Fwarn\u002Ffail findings\n3. blocked actions\n4. required mitigations\n5. safe next step\n\nIf any execution-capable step is requested, require a separate implementation\nplan and explicit user approval.\n",{"name":5,"origin":24,"description":7},"ECC","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fprediction-market-risk-review",1,"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.","2026-11-11 23:56:26",{"source":8,"skillName":5,"commands":36,"prompt":46,"zipAvailable":47},[37,40,43],{"tool":38,"command":39},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill prediction-market-risk-review",{"tool":41,"command":42},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill prediction-market-risk-review",{"tool":44,"command":45},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill prediction-market-risk-review","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fprediction-market-risk-review\u002FSKILL.md",true,[],[50,58,65,73,81,89],{"skillNo":51,"name":52,"nameZh":6,"description":53,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":54,"licenseFileUrl":6,"sourceHtmlUrl":55,"categories":56,"keywords":57,"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`.","MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"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.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":69,"sourceHtmlUrl":70,"categories":71,"keywords":72,"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":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":77,"sourceHtmlUrl":78,"categories":79,"keywords":80,"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":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":85,"sourceHtmlUrl":86,"categories":87,"keywords":88,"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":90,"name":91,"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":92,"categories":93,"keywords":94,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]