「iga」の検索結果

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로, flujo de trabajo académico, investigación a artículo, flujo completo de artículo, pipeline de investigación completa, publicación de investigación, flujo de trabajo completo del artículo.

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Build collaborative forensic incident timelines using Timesketch to ingest, normalize, and analyze multi-source event data (including Plaso output) for attack chain reconstruction and investigation documentation. Use when reconstructing the sequence of events during an incident investigation or when multiple analysts need to jointly tag, annotate, and search a shared DFIR timeline.

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Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

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Expert guidance for security alert triage in Google SecOps. Use when investigating and triaging security alerts, determining false positives vs. true positives, assessing entity risk, adjusting alert severity or priority, and closing or escalating alerts and cases. Don't use for deep multi-hop incident investigations across host timelines (use secops-investigate), proactive threat hunting or retroactive IoC sweeps (use secops-hunt), or authoring new detection rules (use secops-detection-engineering).

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Expert guidance for deep security incident and entity investigations in Google SecOps. Use when investigating cases, analyzing entities (hosts, IPs, domains, hashes, users), extracting and searching UDM events, performing asset and user timeline analysis, and detecting lateral movement across enterprise networks. Don't use for detection rule authoring or YARA-L tuning (use secops-detection-engineering), proactive hypothesis-driven hunting (use secops-hunt), initial alert triage (use secops-triage), or basic case status updates (use secops-cases).

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Deep research agent skill. Use when the user needs thorough, scientific, truth-seeking research on any topic -- investigating claims, finding primary sources, synthesizing evidence, producing cited reports. Triggers on: 'research this', 'investigate', 'deep dive', 'find sources', 'what does the evidence say', 'literature review', 'fact check', 'analyze the research on', any request requiring multi-source investigation with citations.

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Visualises and validates a map's pedestrian navigation mesh by sampling walkable locations (get_random_location_from_navigation) and drawing them as debug points. Use when the user asks to "show/visualise the pedestrian navmesh", "check walkable areas", "validate pedestrian navigation after a map import", or "why won't my walkers move". Confirms the navmesh loaded and how far it covers.

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Drives a vehicle to a destination autonomously using CARLA's navigation agents (BasicAgent/BehaviorAgent/ConstantVelocityAgent), or plans a route between two points with GlobalRoutePlanner. The agent handles routing, obstacle avoidance and traffic-light obedience. Use when the user asks to "drive the ego to (x,y,z)", "navigate to the junction", "go to that location", "plan a route from A to B", or "make the car drive itself somewhere specific". Manual control is control-vehicle; autopilot roaming is control-traffic.

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Draft an audience-specific summary from the privileged investigation memo — HR, leadership, or outside counsel versions. Use when an investigation memo needs to be communicated to an audience that should not see the full privileged work product.

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Add data to an open investigation — documents, interview notes, or observations. Processes batches against the documented pull criteria, surfaces significant items, and logs everything reviewed for coverage verification. Use when new evidence, interview notes, or document productions come in for an open investigation.

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Ask questions against an open investigation log — what witnesses said, where accounts conflict, what gaps exist, what the strongest evidence is on each issue. Use when the attorney needs to query the investigation record without re-reading every entry.

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调查研究：在下判断、做决策、给建议之前，先用第一手材料弄清现状，产出「事实 / 推断 / 未知」三栏调查结论。当上下文不完整、证据薄弱、领域陌生、用户说"先别动手"、或你发现自己在凭印象作答时触发；已充分调查后的执行阶段不要重复触发。 English: Investigate before judging. Gather firsthand facts and produce a fact / inference / unknown ledger before any recommendation. Trigger when context is incomplete, evidence is thin, the domain is unfamiliar, the user says "look first", or you notice yourself answering from impression; skip when investigation is already done and the task is pure execution.

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Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet".

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Run a deep root-cause investigation on the AWS DevOps Agent. Use when the user describes an incident, alarm, outage, or unexplained behavior — keywords like "5xx", "503", "OOM", "latency spike", "deployment failure", "rollback", "sev1", "investigate", "root cause", "debug", "alarm fired", "service down". Polls and streams progress, then surfaces recommendations.

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Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.

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Add AI navigation in Unity 6.3 LTS: bake a NavMesh with the AI Navigation package (NavMeshSurface), move agents with NavMeshAgent.SetDestination, and handle dynamic obstacles. Use when setting up pathfinding, making an enemy chase the player, baking navigation, or when the user mentions NavMesh, NavMeshAgent, NavMeshSurface, NavMeshObstacle, or Unity pathfinding.

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Plan a feature or initiative through a computed route: the conductor derives the canon delta and assembles the document package — from a zero-document null route for small fixes to an umbrella PRD with one spec per capability for large initiatives. Modes, named as the first word: sdd (full package), sources (MRD → BRD → URD) for market research, customer discovery, and business or user requirements, iso (BRS → StRS → SyRS → SRS) for ISO 29148 and regulated work, research for a technical investigation — never market research — that the research instrument closes either by scope coverage (a research document) or by a recommendation (an rnd). Explicit form: plan research <topic>. Use for 'plan the X redesign', 'create a roadmap', 'plan a new feature', 'I need market research before we plan', 'we're regulated — start the ISO requirements cascade', 'investigate X before we plan', 'compare the alternatives for Y' — a new investigation, not a finished report already in hand. Not for recording a decision or documenting existing code — use /archcore:document. Not for checking docs against code — use /archcore:review.

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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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Create Viking web projects, start and verify a local preview, or deploy a generated project to Volcengine IGA Pages when explicitly requested. Includes agent-guided feature, eligible application, dataset, scene, and authentication choices. Use only after confirming the installed CLI exposes `vs project`; otherwise stop without taking action.

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Deprecated compatibility entry for Navigation Menu construction. This component is now maintained in Bricks; install and use component-builder to build a new Navigation Menu.

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Expert India Digital Personal Data Protection Act, 2023 (DPDPA) compliance advisor. Use this skill whenever a user asks about the DPDPA, DPDP Act, DPDP Rules 2025, India data privacy law, Data Fiduciary obligations, Data Principal rights, Significant Data Fiduciary, Data Protection Board of India, consent under DPDPA, notice requirements, breach notification India, children's data India, cross-border data transfer India, India privacy compliance, DPDPA gap analysis, DPDPA vs GDPR, or any obligation under India's personal data protection framework. Also trigger for: "Section 6 consent", "Section 7 legitimate uses", "Section 9 children's data", "Section 10 SDF", "Section 16 cross-border", "Rule 6 breach notification", "Rule 13 SDF obligations", "Data Protection Board complaint", "verifiable parental consent India", "DPDPA compliance roadmap", or "India privacy law global company".

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Systematic root-cause debugging. Iron Law: no fixes without root cause investigation first. Four phases: Root Cause Investigation, Pattern Analysis (6 known patterns), Hypothesis Testing (3-strike rule), Implementation. Structured debug report output.

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When the user wants to plan, map, or restructure their website's page hierarchy, navigation, URL structure, or internal linking. Also use when the user mentions "sitemap," "site map," "visual sitemap," "site structure," "page hierarchy," "information architecture," "IA," "navigation design," "URL structure," "breadcrumbs," "internal linking strategy," "website planning," "what pages do I need," "how should I organize my site," or "site navigation." Use this whenever someone is planning what pages a website should have and how they connect. NOT for XML sitemaps (that's technical SEO — see seo-audit). For SEO audits, see seo-audit. For structured data, see schema.

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Calendar litigation and arbitration deadlines from a scheduling order. Parses a PDF scheduling order, identifies key dates, computes backward deadlines using the applicable rules (Colorado CRCP, Federal FRCP, or arbitration forum rules for AAA/JAMS), and generates an .ics calendar file for Outlook or Google Calendar import. Use this skill whenever the user mentions: litigation deadlines, scheduling order, case management order, deadline calendaring, discovery deadlines, trial preparation deadlines, arbitration scheduling, or anything related to computing or tracking court or arbitration deadlines. Also trigger when the user uploads a PDF that appears to be a court scheduling order or arbitration scheduling order.

日本語の概要は準備中です。原文の説明を表示しています。