[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73FM4Z11C9NGFH6PP5YCNT5":3,"skill-same:01M73FM4Z11C9NGFH6PP5YCNT5":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},"01M73FM4Z11C9NGFH6PP5YCNT5","pubmed-database",null,"Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.","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\u002Fscientific-db-pubmed-database\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: pubmed-database\ndescription: Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.\norigin: community\n---\n\n# PubMed Database\n\nUse this skill when a task needs biomedical literature from PubMed rather than\ngeneral web search.\n\n## When to Use\n\n- Searching MEDLINE or life-sciences literature.\n- Building PubMed queries with MeSH terms, field tags, dates, or article types.\n- Looking up PMIDs, abstracts, publication metadata, or related citations.\n- Running systematic-review search passes that need repeatable search strings.\n- Using NCBI E-utilities directly from Python, shell, or another HTTP client.\n\n## Query Construction\n\nStart with the research question, split it into concepts, then combine concepts\nwith Boolean operators.\n\n```text\nconcept_1 AND concept_2 AND filter\nsynonym_a OR synonym_b\nNOT exclusion_term\n```\n\nUseful PubMed field tags:\n\n- `[ti]`: title\n- `[ab]`: abstract\n- `[tiab]`: title or abstract\n- `[au]`: author\n- `[ta]`: journal title abbreviation\n- `[mh]`: MeSH term\n- `[majr]`: major MeSH topic\n- `[pt]`: publication type\n- `[dp]`: date of publication\n- `[la]`: language\n\nExamples:\n\n```text\ndiabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2026[dp]\n(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]\nsmith ja[au] AND cancer[tiab] AND 2026[dp] AND english[la]\n```\n\n## MeSH and Subheadings\n\nPrefer MeSH when the concept has a stable controlled-vocabulary term. Combine\nMeSH with title\u002Fabstract terms when the topic is new or terminology varies.\n\nCorrect subheading syntax puts the subheading before the field tag:\n\n```text\ndiabetes mellitus, type 2\u002Fdrug therapy[mh]\ncardiovascular diseases\u002Fprevention & control[mh]\n```\n\nUse `[majr]` only when the topic must be central to the paper. It can improve\nprecision but may miss relevant work.\n\n## Filters\n\nPublication types:\n\n- `clinical trial[pt]`\n- `meta-analysis[pt]`\n- `randomized controlled trial[pt]`\n- `review[pt]`\n- `systematic review[pt]`\n- `guideline[pt]`\n\nDate filters:\n\n```text\n2026[dp]\n2020:2026[dp]\n2026\u002F03\u002F15[dp]\n```\n\nAvailability filters:\n\n```text\nfree full text[sb]\nhasabstract[text]\n```\n\n## E-utilities Workflow\n\nNCBI E-utilities supports repeatable API workflows:\n\n1. `esearch.fcgi`: search and return PMIDs.\n2. `esummary.fcgi`: return lightweight article metadata.\n3. `efetch.fcgi`: fetch abstracts or full records in XML, MEDLINE, or text.\n4. `elink.fcgi`: find related articles and linked resources.\n\nUse an email and API key for production scripts. Store API keys in environment\nvariables, never in committed files or command history.\n\n```python\nimport os\nimport time\nimport requests\n\nBASE = \"https:\u002F\u002Feutils.ncbi.nlm.nih.gov\u002Fentrez\u002Feutils\"\n\n\ndef esearch(query: str, retmax: int = 20) -> list[str]:\n    params = {\n        \"db\": \"pubmed\",\n        \"term\": query,\n        \"retmode\": \"json\",\n        \"retmax\": retmax,\n        \"tool\": \"ecc-pubmed-search\",\n        \"email\": os.environ.get(\"NCBI_EMAIL\", \"\"),\n    }\n    api_key = os.environ.get(\"NCBI_API_KEY\")\n    if api_key:\n        params[\"api_key\"] = api_key\n\n    response = requests.get(f\"{BASE}\u002Fesearch.fcgi\", params=params, timeout=30)\n    response.raise_for_status()\n    time.sleep(0.35)\n    return response.json()[\"esearchresult\"][\"idlist\"]\n\n\npmids = esearch(\"hypertension[mh] AND randomized controlled trial[pt] AND 2024:2026[dp]\")\nprint(pmids)\n```\n\nFor batches, prefer NCBI history server parameters (`usehistory=y`,\n`WebEnv`, `query_key`) instead of passing very long PMID lists through URLs.\n\n## Output Discipline\n\nFor each search pass, record:\n\n- exact search string\n- database searched\n- date searched\n- filters used\n- result count\n- export format\n- any manual exclusions\n\nExample:\n\n```markdown\n| Database | Date searched | Query | Filters | Results |\n| --- | --- | --- | --- | ---: |\n| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |\n```\n\n## Review Checklist\n\n- Are field tags valid PubMed tags?\n- Are MeSH terms paired with free-text synonyms for newer topics?\n- Is the date range explicit and appropriate?\n- Does the search log include enough detail to reproduce the query?\n- Are API keys loaded from the environment?\n- Does HTTP code call `raise_for_status()` or otherwise handle non-200\n  responses before parsing?\n- Are rate limits respected?\n\n## References\n\n- [PubMed help](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002Fhelp\u002F)\n- [NCBI E-utilities documentation](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fbooks\u002FNBK25501\u002F)\n- [NCBI API key guidance](https:\u002F\u002Fsupport.nlm.nih.gov\u002FkbArticle\u002F?pn=KA-05317)\n- NCBI support: \u003Ceutilities@ncbi.nlm.nih.gov>\n","# PubMed Database\n\nUse this skill when a task needs biomedical literature from PubMed rather than\ngeneral web search.\n\n## When to Use\n\n- Searching MEDLINE or life-sciences literature.\n- Building PubMed queries with MeSH terms, field tags, dates, or article types.\n- Looking up PMIDs, abstracts, publication metadata, or related citations.\n- Running systematic-review search passes that need repeatable search strings.\n- Using NCBI E-utilities directly from Python, shell, or another HTTP client.\n\n## Query Construction\n\nStart with the research question, split it into concepts, then combine concepts\nwith Boolean operators.\n\n```text\nconcept_1 AND concept_2 AND filter\nsynonym_a OR synonym_b\nNOT exclusion_term\n```\n\nUseful PubMed field tags:\n\n- `[ti]`: title\n- `[ab]`: abstract\n- `[tiab]`: title or abstract\n- `[au]`: author\n- `[ta]`: journal title abbreviation\n- `[mh]`: MeSH term\n- `[majr]`: major MeSH topic\n- `[pt]`: publication type\n- `[dp]`: date of publication\n- `[la]`: language\n\nExamples:\n\n```text\ndiabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2026[dp]\n(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]\nsmith ja[au] AND cancer[tiab] AND 2026[dp] AND english[la]\n```\n\n## MeSH and Subheadings\n\nPrefer MeSH when the concept has a stable controlled-vocabulary term. Combine\nMeSH with title\u002Fabstract terms when the topic is new or terminology varies.\n\nCorrect subheading syntax puts the subheading before the field tag:\n\n```text\ndiabetes mellitus, type 2\u002Fdrug therapy[mh]\ncardiovascular diseases\u002Fprevention & control[mh]\n```\n\nUse `[majr]` only when the topic must be central to the paper. It can improve\nprecision but may miss relevant work.\n\n## Filters\n\nPublication types:\n\n- `clinical trial[pt]`\n- `meta-analysis[pt]`\n- `randomized controlled trial[pt]`\n- `review[pt]`\n- `systematic review[pt]`\n- `guideline[pt]`\n\nDate filters:\n\n```text\n2026[dp]\n2020:2026[dp]\n2026\u002F03\u002F15[dp]\n```\n\nAvailability filters:\n\n```text\nfree full text[sb]\nhasabstract[text]\n```\n\n## E-utilities Workflow\n\nNCBI E-utilities supports repeatable API workflows:\n\n1. `esearch.fcgi`: search and return PMIDs.\n2. `esummary.fcgi`: return lightweight article metadata.\n3. `efetch.fcgi`: fetch abstracts or full records in XML, MEDLINE, or text.\n4. `elink.fcgi`: find related articles and linked resources.\n\nUse an email and API key for production scripts. Store API keys in environment\nvariables, never in committed files or command history.\n\n```python\nimport os\nimport time\nimport requests\n\nBASE = \"https:\u002F\u002Feutils.ncbi.nlm.nih.gov\u002Fentrez\u002Feutils\"\n\n\ndef esearch(query: str, retmax: int = 20) -> list[str]:\n    params = {\n        \"db\": \"pubmed\",\n        \"term\": query,\n        \"retmode\": \"json\",\n        \"retmax\": retmax,\n        \"tool\": \"ecc-pubmed-search\",\n        \"email\": os.environ.get(\"NCBI_EMAIL\", \"\"),\n    }\n    api_key = os.environ.get(\"NCBI_API_KEY\")\n    if api_key:\n        params[\"api_key\"] = api_key\n\n    response = requests.get(f\"{BASE}\u002Fesearch.fcgi\", params=params, timeout=30)\n    response.raise_for_status()\n    time.sleep(0.35)\n    return response.json()[\"esearchresult\"][\"idlist\"]\n\n\npmids = esearch(\"hypertension[mh] AND randomized controlled trial[pt] AND 2024:2026[dp]\")\nprint(pmids)\n```\n\nFor batches, prefer NCBI history server parameters (`usehistory=y`,\n`WebEnv`, `query_key`) instead of passing very long PMID lists through URLs.\n\n## Output Discipline\n\nFor each search pass, record:\n\n- exact search string\n- database searched\n- date searched\n- filters used\n- result count\n- export format\n- any manual exclusions\n\nExample:\n\n```markdown\n| Database | Date searched | Query | Filters | Results |\n| --- | --- | --- | --- | ---: |\n| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |\n```\n\n## Review Checklist\n\n- Are field tags valid PubMed tags?\n- Are MeSH terms paired with free-text synonyms for newer topics?\n- Is the date range explicit and appropriate?\n- Does the search log include enough detail to reproduce the query?\n- Are API keys loaded from the environment?\n- Does HTTP code call `raise_for_status()` or otherwise handle non-200\n  responses before parsing?\n- Are rate limits respected?\n\n## References\n\n- [PubMed help](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002Fhelp\u002F)\n- [NCBI E-utilities documentation](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fbooks\u002FNBK25501\u002F)\n- [NCBI API key guidance](https:\u002F\u002Fsupport.nlm.nih.gov\u002FkbArticle\u002F?pn=KA-05317)\n- NCBI support: \u003Ceutilities@ncbi.nlm.nih.gov>\n",{"name":5,"origin":24,"description":7},"community","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fscientific-db-pubmed-database",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":36,"prompt":46,"zipAvailable":47},[37,40,43],{"tool":38,"command":39},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill pubmed-database",{"tool":41,"command":42},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill pubmed-database",{"tool":44,"command":45},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill pubmed-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fscientific-db-pubmed-database\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",[],[]]