[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73FDX28VXP58P7WC0JNVG19":3,"skill-same:01M73FDX28VXP58P7WC0JNVG19":50},{"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":26,"commitSha":27,"skillDirectory":28,"forksCount":29,"repositoryHtmlUrl":30,"repositoryHomepageUrl":31,"ownerHtmlUrl":32,"ownerBlogUrl":33,"ownerDescription":34,"updatedAt":35,"install":36,"resources":49},"01M73FDX28VXP58P7WC0JNVG19","recursive-decision-ledger",null,"Use when the user asks for repeated rollouts, marked decision processes, high-dimensional search, stochastic optimization, local-optima exploration, ensemble comparison, or recursive reasoning with a visible evidence trail.","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\u002Frecursive-decision-ledger\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: recursive-decision-ledger\ndescription: Use when the user asks for repeated rollouts, marked decision processes, high-dimensional search, stochastic optimization, local-optima exploration, ensemble comparison, or recursive reasoning with a visible evidence trail.\norigin: ECC\ntools: Read, Write, Edit, Bash, Grep, Glob\n---\n\n# Recursive Decision Ledger\n\nUse this skill when the user is trying to force deeper computation through\nrepeated rollouts or \"Prime Gauss\" style recursive prompting. Preserve the useful\npart: repeated trials, prior memory, fresh information, and explicit marks.\nRemove the unsafe part: pretending the loop proves certainty.\n\n## Ledger Contract\n\nEvery rollout should record:\n\n- rollout id and timestamp;\n- prior accepted winner and prior watchlist;\n- fresh information ingested;\n- search space size;\n- model families or heuristics used;\n- trial count and effective trial count;\n- top candidates;\n- decision marks;\n- coherence marks against the prior ledger;\n- promotion gate result.\n\nPrefer JSONL for append-only ledgers and Markdown for human summaries.\n\n## Rollout Loop\n\n1. Load the prior ledger.\n2. Capture new information at time-step zero.\n3. Run the bounded search.\n4. Mark each candidate: accept, watch, reject, decay watch, or needs replay.\n5. Compare winners against prior winners and latest marked rollout.\n6. Downgrade candidates when drift, tail risk, stale data, or failed replay\n   invalidates the previous mark.\n7. Append artifacts before summarizing.\n\n## Coherence Mark\n\nInclude a compact coherence mark:\n\n```text\nEnsemble matches prior winner: true\nRecursive matches prior winner: false\nLatest rollout match: true\nLive promotion allowed: false\nReason: replay and freshness gates not satisfied\n```\n\n## Promotion Rules\n\nFor trading, capital allocation, production deploys, migrations, or destructive\nops, recursive confidence is not approval.\n\nDefault to paper, dry-run, read-only, preview, or staged mode unless the user\nexplicitly approves the live action and the repo\u002Fservice gate supports it.\n\nPromote only when:\n\n- the candidate beats the prior accepted winner on the chosen metric;\n- correctness and replay checks pass;\n- risk limits are explicit;\n- the evidence is durable;\n- the user has approved the live step when needed.\n\n## Summary Shape\n\nLead with the decision, not the drama:\n\n```text\nRollout 15 complete. The prior winner still holds, but edge deteriorated 17%.\nStatus: watch, not live. Next gate: 20 replay fills with fresh orderbook age\nbelow threshold.\n```\n","# Recursive Decision Ledger\n\nUse this skill when the user is trying to force deeper computation through\nrepeated rollouts or \"Prime Gauss\" style recursive prompting. Preserve the useful\npart: repeated trials, prior memory, fresh information, and explicit marks.\nRemove the unsafe part: pretending the loop proves certainty.\n\n## Ledger Contract\n\nEvery rollout should record:\n\n- rollout id and timestamp;\n- prior accepted winner and prior watchlist;\n- fresh information ingested;\n- search space size;\n- model families or heuristics used;\n- trial count and effective trial count;\n- top candidates;\n- decision marks;\n- coherence marks against the prior ledger;\n- promotion gate result.\n\nPrefer JSONL for append-only ledgers and Markdown for human summaries.\n\n## Rollout Loop\n\n1. Load the prior ledger.\n2. Capture new information at time-step zero.\n3. Run the bounded search.\n4. Mark each candidate: accept, watch, reject, decay watch, or needs replay.\n5. Compare winners against prior winners and latest marked rollout.\n6. Downgrade candidates when drift, tail risk, stale data, or failed replay\n   invalidates the previous mark.\n7. Append artifacts before summarizing.\n\n## Coherence Mark\n\nInclude a compact coherence mark:\n\n```text\nEnsemble matches prior winner: true\nRecursive matches prior winner: false\nLatest rollout match: true\nLive promotion allowed: false\nReason: replay and freshness gates not satisfied\n```\n\n## Promotion Rules\n\nFor trading, capital allocation, production deploys, migrations, or destructive\nops, recursive confidence is not approval.\n\nDefault to paper, dry-run, read-only, preview, or staged mode unless the user\nexplicitly approves the live action and the repo\u002Fservice gate supports it.\n\nPromote only when:\n\n- the candidate beats the prior accepted winner on the chosen metric;\n- correctness and replay checks pass;\n- risk limits are explicit;\n- the evidence is durable;\n- the user has approved the live step when needed.\n\n## Summary Shape\n\nLead with the decision, not the drama:\n\n```text\nRollout 15 complete. The prior winner still holds, but edge deteriorated 17%.\nStatus: watch, not live. Next gate: 20 replay fills with fresh orderbook age\nbelow threshold.\n```\n",{"name":5,"tools":24,"origin":25,"description":7},"Read, Write, Edit, Bash, Grep, Glob","ECC","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Frecursive-decision-ledger",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.","2026-11-12 23:12:27",{"source":8,"skillName":5,"commands":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill recursive-decision-ledger",{"tool":42,"command":43},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill recursive-decision-ledger",{"tool":45,"command":46},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill recursive-decision-ledger","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Frecursive-decision-ledger\u002FSKILL.md",true,[],[51,59,66,74,82,90],{"skillNo":52,"name":53,"nameZh":6,"description":54,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":55,"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`.","MIT","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":15,"licenseFileUrl":6,"sourceHtmlUrl":63,"categories":64,"keywords":65,"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":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":70,"sourceHtmlUrl":71,"categories":72,"keywords":73,"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":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":78,"sourceHtmlUrl":79,"categories":80,"keywords":81,"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":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":86,"sourceHtmlUrl":87,"categories":88,"keywords":89,"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":91,"name":92,"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":93,"categories":94,"keywords":95,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]