[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6HZXRBPEWX704GWB7X20AXF":3,"skill-same:01M6HZXRBPEWX704GWB7X20AXF":99},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":64,"ownerHtmlUrl":65,"ownerBlogUrl":6,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6HZXRBPEWX704GWB7X20AXF","analyzing-indicators-of-compromise",null,"Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block\u002Fmonitor\u002Fwhitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.","onfire7777\u002Funiversal-ai-skills-library","onfire7777","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F240866566?v=4","01M6HZJCF2QEW8DY7HE1JDKGK2","Jake B","01M6HZJCKK8SEXKN76V9SGKZ12",17,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-indicators-of-compromise\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"analyzing indicators of compromise","analyzing","indicators","feeds","source","addresses","domains","ip","alerts","intelligence","campaign","threat","security","NIST","NIST-CSF","threat-intelligence","email","campaign attribution","security alerts","cybersecurity","2026-11-10 10:12:30",false,"---\nname: analyzing-indicators-of-compromise\nlicense: Apache-2.0\ndescription: Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block\u002Fmonitor\u002Fwhitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.\nmetadata:\n  domain: cybersecurity\n  subdomain: threat-intelligence\n  tags:\n  - IOC\n  - VirusTotal\n  - AbuseIPDB\n  - MalwareBazaar\n  - MISP\n  - threat-intelligence\n  - STIX\n  - NIST-CSF\n  version: 1.0.0\n  author: mahipal\n---\n# Analyzing Indicators of Compromise\n\n## When to Use\n\nUse this skill when:\n- A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage\n- Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls\n- An incident investigation requires contextual enrichment of observed network artifacts\n\n**Do not use** this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).\n\n## Prerequisites\n\n- VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup\n- AbuseIPDB API key for IP reputation checks\n- MISP instance or TIP for cross-referencing against known campaigns\n- Python with `requests` and `vt-py` libraries, or SOAR platform with pre-built connectors\n\n## Workflow\n\n### Step 1: Normalize and Classify IOC Types\n\nBefore enriching, classify each IOC:\n- **IPv4\u002FIPv6 address**: Check if RFC 1918 private (skip external enrichment), validate format\n- **Domain\u002FFQDN**: Defang for safe handling (`evil[.]com`), extract registered domain via tldextract\n- **URL**: Extract domain + path separately; check for redirectors\n- **File hash**: Identify hash type (MD5\u002FSHA-1\u002FSHA-256); prefer SHA-256 for uniqueness\n- **Email address**: Split into domain (check MX\u002FDMARC) and local part for pattern analysis\n\nDefang IOCs in documentation (replace `.` with `[.]` and `:\u002F\u002F` with `[:\u002F\u002F]`) to prevent accidental clicks.\n\n### Step 2: Multi-Source Enrichment\n\n**VirusTotal (file hash, URL, IP, domain)**:\n```python\nimport vt\n\nclient = vt.Client(\"YOUR_VT_API_KEY\")\n\n# File hash lookup\nfile_obj = client.get_object(f\"\u002Ffiles\u002F{sha256_hash}\")\ndetections = file_obj.last_analysis_stats\nprint(f\"Malicious: {detections['malicious']}\u002F{sum(detections.values())}\")\n\n# Domain analysis\ndomain_obj = client.get_object(f\"\u002Fdomains\u002F{domain}\")\nprint(domain_obj.last_analysis_stats)\nprint(domain_obj.reputation)\nclient.close()\n```\n\n**AbuseIPDB (IP addresses)**:\n```python\nimport requests\n\nresponse = requests.get(\n    \"https:\u002F\u002Fapi.abuseipdb.com\u002Fapi\u002Fv2\u002Fcheck\",\n    headers={\"Key\": \"YOUR_KEY\", \"Accept\": \"application\u002Fjson\"},\n    params={\"ipAddress\": \"1.2.3.4\", \"maxAgeInDays\": 90}\n)\ndata = response.json()[\"data\"]\nprint(f\"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}\")\n```\n\n**MalwareBazaar (file hashes)**:\n```python\nresponse = requests.post(\n    \"https:\u002F\u002Fmb-api.abuse.ch\u002Fapi\u002Fv1\u002F\",\n    data={\"query\": \"get_info\", \"hash\": sha256_hash}\n)\nresult = response.json()\nif result[\"query_status\"] == \"ok\":\n    print(result[\"data\"][0][\"tags\"], result[\"data\"][0][\"signature\"])\n```\n\n### Step 3: Contextualize with Campaign Attribution\n\nQuery MISP for existing events matching the IOC:\n```python\nfrom pymisp import PyMISP\n\nmisp = PyMISP(\"https:\u002F\u002Fmisp.example.com\", \"API_KEY\")\nresults = misp.search(value=\"evil-domain.com\", type_attribute=\"domain\")\nfor event in results:\n    print(event[\"Event\"][\"info\"], event[\"Event\"][\"threat_level_id\"])\n```\n\nCheck Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).\n\n### Step 4: Assign Confidence Score and Disposition\n\nApply a tiered decision framework:\n- **Block (High Confidence ≥ 70%)**: ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign\n- **Monitor\u002FAlert (Medium 40–69%)**: 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution\n- **Whitelist\u002FInvestigate (Low \u003C40%)**: ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)\n- **False Positive**: Legitimate business service incorrectly flagged; document and exclude from future alerts\n\n### Step 5: Document and Distribute\n\nRecord findings in TIP\u002FMISP with:\n- All enrichment data collected (timestamps, source, score)\n- Disposition decision and rationale\n- Blocking actions taken (firewall, proxy, DNS sinkhole)\n- Related incident ticket number\n\nExport to STIX indicator object with confidence field set appropriately.\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **IOC** | Indicator of Compromise — observable network or host artifact indicating potential compromise |\n| **Enrichment** | Process of adding contextual data to a raw IOC from multiple intelligence sources |\n| **Defanging** | Modifying IOCs (replacing `.` with `[.]`) to prevent accidental activation in documentation |\n| **False Positive Rate** | Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds |\n| **Sinkhole** | DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely |\n| **TTL** | Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days |\n\n## Tools & Systems\n\n- **VirusTotal**: Multi-engine malware scanner and threat intelligence platform with 70+ AV engines, sandbox reports, and community comments\n- **AbuseIPDB**: Community-maintained IP reputation database with 90-day abuse report history\n- **MalwareBazaar (abuse.ch)**: Free malware hash repository with YARA rule associations and malware family tagging\n- **URLScan.io**: Free URL analysis service that captures screenshots, DOM, and network requests for phishing URL triage\n- **Shodan**: Internet-wide scan data providing hosting provider, open ports, and banner information for IP enrichment\n\n## Common Pitfalls\n\n- **Blocking shared infrastructure**: CDN IPs (Cloudflare 104.21.x.x, AWS CloudFront) may legitimately host malicious content but blocking the IP disrupts thousands of legitimate sites.\n- **VT score obsession**: Low VT detection count does not mean benign — zero-day malware and custom APT tools often score 0 initially. Check sandbox behavior, MISP, and passive DNS.\n- **Missing defanging**: Pasting live IOCs in emails or Confluence docs can trigger automated URL scanners or phishing tools.\n- **No expiration policy**: IOCs without TTLs accumulate in blocklists indefinitely, generating false positives as infrastructure is repurposed by legitimate users.\n- **Over-relying on single source**: VirusTotal aggregates AV opinions — all may be wrong or lag behind emerging malware. Use 3+ independent sources for high-stakes decisions.\n","# Analyzing Indicators of Compromise\n\n## When to Use\n\nUse this skill when:\n- A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage\n- Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls\n- An incident investigation requires contextual enrichment of observed network artifacts\n\n**Do not use** this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).\n\n## Prerequisites\n\n- VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup\n- AbuseIPDB API key for IP reputation checks\n- MISP instance or TIP for cross-referencing against known campaigns\n- Python with `requests` and `vt-py` libraries, or SOAR platform with pre-built connectors\n\n## Workflow\n\n### Step 1: Normalize and Classify IOC Types\n\nBefore enriching, classify each IOC:\n- **IPv4\u002FIPv6 address**: Check if RFC 1918 private (skip external enrichment), validate format\n- **Domain\u002FFQDN**: Defang for safe handling (`evil[.]com`), extract registered domain via tldextract\n- **URL**: Extract domain + path separately; check for redirectors\n- **File hash**: Identify hash type (MD5\u002FSHA-1\u002FSHA-256); prefer SHA-256 for uniqueness\n- **Email address**: Split into domain (check MX\u002FDMARC) and local part for pattern analysis\n\nDefang IOCs in documentation (replace `.` with `[.]` and `:\u002F\u002F` with `[:\u002F\u002F]`) to prevent accidental clicks.\n\n### Step 2: Multi-Source Enrichment\n\n**VirusTotal (file hash, URL, IP, domain)**:\n```python\nimport vt\n\nclient = vt.Client(\"YOUR_VT_API_KEY\")\n\n# File hash lookup\nfile_obj = client.get_object(f\"\u002Ffiles\u002F{sha256_hash}\")\ndetections = file_obj.last_analysis_stats\nprint(f\"Malicious: {detections['malicious']}\u002F{sum(detections.values())}\")\n\n# Domain analysis\ndomain_obj = client.get_object(f\"\u002Fdomains\u002F{domain}\")\nprint(domain_obj.last_analysis_stats)\nprint(domain_obj.reputation)\nclient.close()\n```\n\n**AbuseIPDB (IP addresses)**:\n```python\nimport requests\n\nresponse = requests.get(\n    \"https:\u002F\u002Fapi.abuseipdb.com\u002Fapi\u002Fv2\u002Fcheck\",\n    headers={\"Key\": \"YOUR_KEY\", \"Accept\": \"application\u002Fjson\"},\n    params={\"ipAddress\": \"1.2.3.4\", \"maxAgeInDays\": 90}\n)\ndata = response.json()[\"data\"]\nprint(f\"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}\")\n```\n\n**MalwareBazaar (file hashes)**:\n```python\nresponse = requests.post(\n    \"https:\u002F\u002Fmb-api.abuse.ch\u002Fapi\u002Fv1\u002F\",\n    data={\"query\": \"get_info\", \"hash\": sha256_hash}\n)\nresult = response.json()\nif result[\"query_status\"] == \"ok\":\n    print(result[\"data\"][0][\"tags\"], result[\"data\"][0][\"signature\"])\n```\n\n### Step 3: Contextualize with Campaign Attribution\n\nQuery MISP for existing events matching the IOC:\n```python\nfrom pymisp import PyMISP\n\nmisp = PyMISP(\"https:\u002F\u002Fmisp.example.com\", \"API_KEY\")\nresults = misp.search(value=\"evil-domain.com\", type_attribute=\"domain\")\nfor event in results:\n    print(event[\"Event\"][\"info\"], event[\"Event\"][\"threat_level_id\"])\n```\n\nCheck Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).\n\n### Step 4: Assign Confidence Score and Disposition\n\nApply a tiered decision framework:\n- **Block (High Confidence ≥ 70%)**: ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign\n- **Monitor\u002FAlert (Medium 40–69%)**: 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution\n- **Whitelist\u002FInvestigate (Low \u003C40%)**: ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)\n- **False Positive**: Legitimate business service incorrectly flagged; document and exclude from future alerts\n\n### Step 5: Document and Distribute\n\nRecord findings in TIP\u002FMISP with:\n- All enrichment data collected (timestamps, source, score)\n- Disposition decision and rationale\n- Blocking actions taken (firewall, proxy, DNS sinkhole)\n- Related incident ticket number\n\nExport to STIX indicator object with confidence field set appropriately.\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **IOC** | Indicator of Compromise — observable network or host artifact indicating potential compromise |\n| **Enrichment** | Process of adding contextual data to a raw IOC from multiple intelligence sources |\n| **Defanging** | Modifying IOCs (replacing `.` with `[.]`) to prevent accidental activation in documentation |\n| **False Positive Rate** | Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds |\n| **Sinkhole** | DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely |\n| **TTL** | Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days |\n\n## Tools & Systems\n\n- **VirusTotal**: Multi-engine malware scanner and threat intelligence platform with 70+ AV engines, sandbox reports, and community comments\n- **AbuseIPDB**: Community-maintained IP reputation database with 90-day abuse report history\n- **MalwareBazaar (abuse.ch)**: Free malware hash repository with YARA rule associations and malware family tagging\n- **URLScan.io**: Free URL analysis service that captures screenshots, DOM, and network requests for phishing URL triage\n- **Shodan**: Internet-wide scan data providing hosting provider, open ports, and banner information for IP enrichment\n\n## Common Pitfalls\n\n- **Blocking shared infrastructure**: CDN IPs (Cloudflare 104.21.x.x, AWS CloudFront) may legitimately host malicious content but blocking the IP disrupts thousands of legitimate sites.\n- **VT score obsession**: Low VT detection count does not mean benign — zero-day malware and custom APT tools often score 0 initially. Check sandbox behavior, MISP, and passive DNS.\n- **Missing defanging**: Pasting live IOCs in emails or Confluence docs can trigger automated URL scanners or phishing tools.\n- **No expiration policy**: IOCs without TTLs accumulate in blocklists indefinitely, generating false positives as infrastructure is repurposed by legitimate users.\n- **Over-relying on single source**: VirusTotal aggregates AV opinions — all may be wrong or lag behind emerging malware. Use 3+ independent sources for high-stakes decisions.\n",{"name":5,"license":15,"metadata":49,"description":7},{"tags":50,"author":57,"domain":43,"version":58,"subdomain":39},[51,52,53,54,55,39,56,38],"IOC","VirusTotal","AbuseIPDB","MalwareBazaar","MISP","STIX","mahipal","1.0.0","main","2a76bf42ce5c860adf8d0f3fcf3f6804109ac65b","skills\u002Fanalyzing-indicators-of-compromise",0,"https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fwiki","https:\u002F\u002Fgithub.com\u002Fonfire7777","trying to create something actually useful","2026-09-11 13:25:26",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add onfire7777\u002Funiversal-ai-skills-library --skill analyzing-indicators-of-compromise",{"tool":74,"command":75},"bunx","bunx skills add onfire7777\u002Funiversal-ai-skills-library --skill analyzing-indicators-of-compromise",{"tool":77,"command":78},"pnpm","pnpm dlx skills add onfire7777\u002Funiversal-ai-skills-library --skill analyzing-indicators-of-compromise","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-indicators-of-compromise\u002FSKILL.md",true,[82,87,93],{"path":83,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-indicators-of-compromise\u002FLICENSE",{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fapi-reference.md","REFERENCE","参考文档",2926,"https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-indicators-of-compromise\u002Freferences\u002Fapi-reference.md",{"path":94,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":97,"sourceHtmlUrl":98},"scripts\u002Fagent.py","SCRIPT","脚本",8985,"https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-indicators-of-compromise\u002Fscripts\u002Fagent.py",[100,129,138,148,169,184],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":116,"publishedAt":44},"01M6J7MPK8ZE9VQJR4KK6ZMZJQ","zyte-api-automation","Automate Zyte API tasks via Rube MCP (Composio). Always search tools first for current schemas.","MIT","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzyte-api-automation\u002FSKILL.md",[107,110,111],{"categoryNo":108,"categoryName":109,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":112,"categoryName":113,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[117,118,119,120,121,122,123,124,125,126,127,128],"automation","api","zyte api automation","for","MCP","via","composio","automate","first","Tools","search","tasks",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":136,"publishedAt":44},"01M6J7MK45K2ZZ0EWTP1N6SZ7F","zylvie-automation","Automate Zylvie tasks via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzylvie-automation\u002FSKILL.md",[135],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[117,137,121,128,120,122,123,124,125,126,127],"zylvie automation",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":145,"publishedAt":44},"01M6J7MGDCVZ3YCXGEAPR5V3MS","zoominfo-automation","Automate Zoominfo tasks via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzoominfo-automation\u002FSKILL.md",[144],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[146,147,117,124,123,126,122,128,120,127,121,125],"zoominfo","zoominfo automation",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":155,"publishedAt":44},"01M6J7MDK48N09B7C6H9TCPY9A","zoom-out","Tell the agent to zoom out and give broader context or a higher-level perspective. Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzoom-out\u002FSKILL.md",[154],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[156,157,158,159,160,161,162,163,164,165,166,167,168],"zoom out","zoom","perspective","code","re","you","with","understand","it","use","context","how","true",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":176,"publishedAt":44},"01M6J7MAD47Q9A55AK3EXS914H","zoho-mail-automation","Automate Zoho Mail tasks via Rube MCP (Composio): email sending, folders, labels, and mailbox management. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzoho-mail-automation\u002FSKILL.md",[175],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[177,178,179,180,117,40,127,128,121,126,181,120,122,182,123,183,124,125],"mail","zoho mail automation","zoho","zoho mail","labels","mailbox","management",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":194,"publishedAt":44},"01M6J7M76HTWPP8TRWWCDS42NB","zoho-invoice-automation","Automate Zoho Invoice tasks via Rube MCP (Composio): invoices, estimates, expenses, clients, and payment tracking. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzoho-invoice-automation\u002FSKILL.md",[190,191],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":192,"categoryName":193,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000040","财务",[195,117,179,196,121,122,120,197,198,199,123,200,201,124,125,126,127,128],"invoice","zoho invoice automation","invoices","expenses","clients","payment","tracking"]