Results for “dga-detection”

Analyze Zeek conn.log and NetFlow data to detect ransomware network indicators including C2 beaconing, TOR exit node connections, data exfiltration, and suspicious DNS patterns.

Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).

Engineering privacy and data governance: PII detection, data flow mapping, consent patterns, GDPR/CCPA-compliant implementation, DPIA. Use when privacy-by-design is needed.

Analyze candidate task nodes, dependencies, collisions, and critical path before a consumer accepts or rejects a final DAG.

Executes DAG workflows with parallel wave processing, agent spawning, context isolation, permission enforcement, and full execution tracing. Use when running a planned DAG, managing concurrent agent execution, enforcing isolation boundaries, or tracing execution for debugging. Activate on "execute DAG", "run workflow", "spawn agents", "parallel execution", "execution trace", "agent isolation". NOT for planning DAGs (use dag-planner), validating outputs (use dag-quality), or matching skills (use dag-skills-matcher).

Predicts enhancer activity in DNA sequences using the hosted Genomic Intelligence G0 DeepSTARR model, returning per-window activity scores.

Scans codebases for GDPR/DSGVO compliance issues, generates DPIA documentation, and tracks data subject rights requests with deadline management.

DeepEval — LLM evaluation framework, RAG metrics, hallucination detection, red-teaming, CI/CD integration

Detect and extract hidden data embedded in images, audio, and other media files using steganalysis tools to uncover covert communication channels.

Produces a machine-validated, auditable JSON decision record with assumptions, risks, recommendation, and review gating for high-stakes decisions.

GDPR and German DSGVO compliance automation. Scans codebases for privacy risks, generates DPIA documentation, tracks data subject rights requests. Use for GDPR compliance assessments, privacy audits, data protection planning, DPIA generation, and data subject rights management.

Diverga Dashboard - Live configuration status and feature overview. 24 specialized agents across 9 categories for social science research. VS methodology prevents mode collapse. Human checkpoints enforce human-in-the-loop decisions. Triggers: /diverga, diverga dashboard, diverga status

Produces auditable, schema-valid JSON decision records with assumptions, risks, recommendations, and review gating for high-stakes choices.

Agent D2 - Data Collection Specialist - Interviews, Focus Groups & Observation. Covers protocol development, question design, probing strategies, transcription conventions, and systematic observation. Absorbed D3 (Observation Protocol Designer) capabilities.

Guides preparation for supervisory authority (DPA) inspections and investigations including document readiness checklists, interview preparation for key personnel, technical demonstration procedures, on-site logistics, response protocols, and post-inspection follow-up. Covers unannounced inspections, formal audits, and complaint-triggered investigations. Keywords: DPA inspection, supervisory authority, investigation, readiness, interview preparation, response protocol.

Train, evaluate, export, distill, quantize, or run inference for a TAO DINO 2D object detector using transformer-based detection with denoising training and multi-scale features.

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Reverse engineer malware binaries using NSA's Ghidra disassembler and decompiler to understand internal logic, cryptographic routines, C2 protocols, and evasion techniques at the assembly and pseudo-C level.

Guides data protection impact assessments under GDPR Article 35, covering mandatory triggers, risk evaluation, and mitigation steps.

Guides authorized digital forensics and incident response workflows including memory dump analysis, disk timeline creation, PCAP investigation, and artifact triage with evidence preservation.

GDPR and CCPA/CPRA privacy compliance audit for codebases. Inventories PII fields (email, phone, SSN, IP, device ID, geolocation, biometrics, behavioral data), maps data collection points (forms, APIs, cookies, analytics, error tracking), audits consent mechanisms (cookie banners, opt-in, pre-checked boxes, consent withdrawal), verifies data subject rights implementation (right to access, erasure, rectification, portability, opt-out, Do Not Sell), traces third-party data sharing (Google Analytics, Facebook Pixel, Stripe, SendGrid, Sentry), and checks data retention policies and automated purging. Use when auditing privacy compliance, building data export or deletion endpoints, reviewing cookie consent, or assessing DSAR readiness.

Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

Investigates insider threat incidents involving employees, contractors, or trusted partners who misuse authorized access to steal data, sabotage systems, or violate security policies. Combines digital forensics, user behavior analytics, and HR/legal coordination to build an evidence-based case.

Configure, simulate, debug, and analyze radarDataGenerator within radarScenario. Use for: interactively building radar detection scenarios from datasheets or performance requirements; diagnosing missed detections and configuration errors; interpreting sensor spherical, body, and scenario-frame outputs; deriving ReferenceRange from hardware specs via link budget; scan mode configuration (mechanical, electronic/AESA, hybrid); and validating simulation results against analytical predictions.

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts using pandas for statistical analysis and anomaly detection.

Run NVIDIA garak probe suites against an LLM endpoint to test for jailbreaks, prompt injection, data leakage, and toxic generation, then interpret the hit-rate report for triage and reporting.

Audits a project's developer experience by scoring devcontainer, git hooks, linting, build caching, environment setup, and release pipeline, then generates a DX health report with prioritized recommendations.

Diverga Memory System v7.0 - Context-persistent research support with checkpoint auto-trigger and cross-session continuity. Triggers: memory, remember, context, recall, checkpoint, decision, persist, 기억, 맥락, 세션, 체크포인트

Use the Python-based ddgs CLI (via `uvx ddgs`) for ad-hoc, unlimited web searches across multiple search engines. Trigger this skill when the user asks you to look something up online, compare sources, or gather search-based research.

Performs free, open-source reverse engineering with Ghidra using headless or GUI modes, including decompilation, cross-references, and optional MCP workflows when IDA is unavailable.

Detect data staging activity before exfiltration by monitoring for archive creation with 7-Zip/RAR, unusual temp folder access, large file consolidation, and staging directory patterns via EDR and process telemetry.

Pattern-Based Diagnosis

Detect splice donor and acceptor sites in DNA sequences using the Genomic Intelligence G0 BigBird transformer, via the hosted /v1/tasks/splice/predict API. Returns per-position site probabilities and called sites.

System diagnostics and health checks for Diverga plugin. OpenClaw-style Check-Report-Fix pattern with 5-layer diagnostics. Triggers: /diverga:doctor, diverga doctor, system check, diagnose, 진단

Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).