[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73FFEVXQ0YZMSSM3NENG3BX":3,"skill-same:01M73FFEVXQ0YZMSSM3NENG3BX":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},"01M73FFEVXQ0YZMSSM3NENG3BX","research-ops",null,"Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.","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\u002Fresearch-ops\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: research-ops\ndescription: Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.\norigin: ECC\n---\n\n# Research Ops\n\nUse this when the user asks to research something current, compare options, enrich people or companies, or turn repeated lookups into a monitored workflow.\n\nThis is the operator wrapper around the repo's research stack. It is not a replacement for `deep-research`, `exa-search`, or `market-research`; it tells you when and how to use them together.\n\n## Skill Stack\n\nPull these ECC-native skills into the workflow when relevant:\n\n- `exa-search` for fast current-web discovery\n- `deep-research` for multi-source synthesis with citations\n- `market-research` when the end result should be a recommendation or ranked decision\n- `lead-intelligence` when the task is people\u002Fcompany targeting instead of generic research\n- `knowledge-ops` when the result should be stored in durable context afterward\n\n## When to Use\n\n- user says \"research\", \"look up\", \"compare\", \"who should I talk to\", or \"what's the latest\"\n- the answer depends on current public information\n- the user already supplied evidence and wants it factored into a fresh recommendation\n- the task may be recurring enough that it should become a monitor instead of a one-off lookup\n\n## Guardrails\n\n- do not answer current questions from stale memory when fresh search is cheap\n- separate:\n  - sourced fact\n  - user-provided evidence\n  - inference\n  - recommendation\n- do not spin up a heavyweight research pass if the answer is already in local code or docs\n\n## Workflow\n\n### 1. Start from what the user already gave you\n\nNormalize any supplied material into:\n\n- already-evidenced facts\n- needs verification\n- open questions\n\nDo not restart the analysis from zero if the user already built part of the model.\n\n### 2. Classify the ask\n\nChoose the right lane before searching:\n\n- quick factual answer\n- comparison or decision memo\n- lead\u002Fenrichment pass\n- recurring monitoring candidate\n\n### 3. Take the lightest useful evidence path first\n\n- use `exa-search` for fast discovery\n- escalate to `deep-research` when synthesis or multiple sources matter\n- use `market-research` when the outcome should end in a recommendation\n- hand off to `lead-intelligence` when the real ask is target ranking or warm-path discovery\n\n### 4. Report with explicit evidence boundaries\n\nFor important claims, say whether they are:\n\n- sourced facts\n- user-supplied context\n- inference\n- recommendation\n\nFreshness-sensitive answers should include concrete dates.\n\n### 5. Decide whether the task should stay manual\n\nIf the user is likely to ask the same research question repeatedly, say so explicitly and recommend a monitoring or workflow layer instead of repeating the same manual search forever.\n\n## Output Format\n\n```text\nQUESTION TYPE\n- factual \u002F comparison \u002F enrichment \u002F monitoring\n\nEVIDENCE\n- sourced facts\n- user-provided context\n\nINFERENCE\n- what follows from the evidence\n\nRECOMMENDATION\n- answer or next move\n- whether this should become a monitor\n```\n\n## Pitfalls\n\n- do not mix inference into sourced facts without labeling it\n- do not ignore user-provided evidence\n- do not use a heavy research lane for a question local repo context can answer\n- do not give freshness-sensitive answers without dates\n\n## Verification\n\n- important claims are labeled by evidence type\n- freshness-sensitive outputs include dates\n- the final recommendation matches the actual research mode used\n","# Research Ops\n\nUse this when the user asks to research something current, compare options, enrich people or companies, or turn repeated lookups into a monitored workflow.\n\nThis is the operator wrapper around the repo's research stack. It is not a replacement for `deep-research`, `exa-search`, or `market-research`; it tells you when and how to use them together.\n\n## Skill Stack\n\nPull these ECC-native skills into the workflow when relevant:\n\n- `exa-search` for fast current-web discovery\n- `deep-research` for multi-source synthesis with citations\n- `market-research` when the end result should be a recommendation or ranked decision\n- `lead-intelligence` when the task is people\u002Fcompany targeting instead of generic research\n- `knowledge-ops` when the result should be stored in durable context afterward\n\n## When to Use\n\n- user says \"research\", \"look up\", \"compare\", \"who should I talk to\", or \"what's the latest\"\n- the answer depends on current public information\n- the user already supplied evidence and wants it factored into a fresh recommendation\n- the task may be recurring enough that it should become a monitor instead of a one-off lookup\n\n## Guardrails\n\n- do not answer current questions from stale memory when fresh search is cheap\n- separate:\n  - sourced fact\n  - user-provided evidence\n  - inference\n  - recommendation\n- do not spin up a heavyweight research pass if the answer is already in local code or docs\n\n## Workflow\n\n### 1. Start from what the user already gave you\n\nNormalize any supplied material into:\n\n- already-evidenced facts\n- needs verification\n- open questions\n\nDo not restart the analysis from zero if the user already built part of the model.\n\n### 2. Classify the ask\n\nChoose the right lane before searching:\n\n- quick factual answer\n- comparison or decision memo\n- lead\u002Fenrichment pass\n- recurring monitoring candidate\n\n### 3. Take the lightest useful evidence path first\n\n- use `exa-search` for fast discovery\n- escalate to `deep-research` when synthesis or multiple sources matter\n- use `market-research` when the outcome should end in a recommendation\n- hand off to `lead-intelligence` when the real ask is target ranking or warm-path discovery\n\n### 4. Report with explicit evidence boundaries\n\nFor important claims, say whether they are:\n\n- sourced facts\n- user-supplied context\n- inference\n- recommendation\n\nFreshness-sensitive answers should include concrete dates.\n\n### 5. Decide whether the task should stay manual\n\nIf the user is likely to ask the same research question repeatedly, say so explicitly and recommend a monitoring or workflow layer instead of repeating the same manual search forever.\n\n## Output Format\n\n```text\nQUESTION TYPE\n- factual \u002F comparison \u002F enrichment \u002F monitoring\n\nEVIDENCE\n- sourced facts\n- user-provided context\n\nINFERENCE\n- what follows from the evidence\n\nRECOMMENDATION\n- answer or next move\n- whether this should become a monitor\n```\n\n## Pitfalls\n\n- do not mix inference into sourced facts without labeling it\n- do not ignore user-provided evidence\n- do not use a heavy research lane for a question local repo context can answer\n- do not give freshness-sensitive answers without dates\n\n## Verification\n\n- important claims are labeled by evidence type\n- freshness-sensitive outputs include dates\n- the final recommendation matches the actual research mode used\n",{"name":5,"origin":24,"description":7},"ECC","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fresearch-ops",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.","2026-11-13 13:15:29",{"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 research-ops",{"tool":41,"command":42},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill research-ops",{"tool":44,"command":45},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill research-ops","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fresearch-ops\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",[],[]]