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Household assistant for a whole family. Runs a morning brief, plans meals and builds grocery lists (hunting the best prices), looks ahead at the week's logistics, stays on top of the kids' school, watches prices on things the family wants, and helps book appointments and reservations. Use it for anything that keeps the family's day or week running. Trigger even on implicit asks - "what's on today", "what do the kids have this week", "what should we make for dinner", "add milk to the list", "when's the dentist", "did anything come from school", "remind me about the permission slip", "watch this jacket and tell me if it drops", "book us a table Friday".

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Scientific research engine with adversarial review, tree search, and serendipity detection. Use when: exploring hypotheses, validating findings against literature, running computational experiments with quality gates, or hunting for unexpected discoveries. Do NOT use for simple Q&A, code editing, or non-research tasks.

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Provide comprehensive techniques for testing REST, SOAP, and GraphQL APIs during bug bounty hunting and penetration testing engagements. Covers vulnerability discovery, authentication bypass, IDOR exploitation, and API-specific attack vectors.

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Use when verifying a story or ticket implementation against its spec and acceptance criteria - per-AC verdicts with file:line evidence and proving tests, code reuse and design conformance, dead code, stale docs, comment discipline. Finds the spec from an explicit path, the ticket key in the branch name, or plan files - and asks rather than guessing. For defect-hunting code review use dotnet-techne-code-review instead. Keywords: story QA, acceptance criteria, spec conformance, AC coverage, definition of done, dead code check, reuse check, comment check, verify story, QA this branch.

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Use when a diff builds more than the task needs — code that should not exist, a dependency the platform already covers, or a clever form where a flat one reads better. Deletion-hunting, not quality.

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Use when the user shares a Sentry error, Jira bug ticket, or error description and wants root cause analysis. Also for proactive bug hunting and code audits for hidden bugs.

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Detect NTLM relay attacks through Windows Security Event correlation by analyzing Event 4624 LogonType 3 for IP-to-hostname mismatches, identifying Responder/LLMNR poisoning artifacts, auditing SMB and LDAP signing enforcement across the domain, and detecting NTLM downgrade attacks from NTLMv2 to NTLMv1 using event log analysis.

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Detect process hollowing (T1055.012) by analyzing memory-mapped sections, hollowed process indicators, and parent-child process anomalies in EDR telemetry.

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Detect Pass-the-Hash attacks by analyzing NTLM authentication patterns, identifying Type 3 logons with NTLM where Kerberos is expected, and correlating with credential dumping.

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Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft.

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Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns

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Detect Golden Ticket attacks in Active Directory by analyzing Kerberos TGT anomalies including mismatched encryption types, impossible ticket lifetimes, non-existent accounts, and forged PAC signatures in domain controller event logs.

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Hunt AADGraphActivityLogs and MicrosoftGraphActivityLogs in Microsoft Sentinel/Log Analytics for fingerprints of offensive Entra ID tools such as ROADtools, AADInternals, and AzureHound.

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Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

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Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.

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Detect abuse of service accounts through anomalous interactive logons, privilege escalation, lateral movement, and unauthorized access patterns.

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Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

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Detect Living Off the Land Binaries (LOLBins/LOLBAS) abuse including certutil, regsvr32, mshta, and rundll32 via process telemetry, Sigma rules, and parent-child process analysis

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Detect privilege escalation attempts including token manipulation, UAC bypass, unquoted service paths, kernel exploits, and sudo/doas abuse across Windows and Linux.

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Detect malicious email forwarding rules created by adversaries to maintain persistent access to email communications for intelligence collection and BEC attacks.

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Detect DCSync attacks where adversaries abuse Active Directory replication privileges to extract password hashes by monitoring for non-domain-controller accounts requesting directory replication via DsGetNCChanges.

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Detect DLL side-loading attacks where adversaries place malicious DLLs alongside legitimate applications to hijack execution flow for defense evasion.

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This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake for petabyte-scale threat hunting across AWS, Azure, and GCP security telemetry.

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Detect PowerShell Empire framework artifacts in Windows event logs by identifying Base64 encoded launcher patterns, default user agents, staging URL structures, stager IOCs, and known Empire module signatures in Script Block Logging events.

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