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「dsc」の検索結果

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概要と使いどころ

The telemetry each Active Directory technique generates and what alerts a defender: Kerberoasting produces Event 4769 with RC4 encryption (0x17) and an MDI alert, DCSync produces Event 4662 with the DS-Replication-Get-Changes GUID, AS-REP roasting produces Event 4768 with no pre-auth, LSASS dumping is blocked by EDR, plus a lateral-movement telemetry table by protocol (SMB/WMI/WinRM/RDP/DCOM). Use this whenever you run or plan an offensive AD technique and need to know what noise it makes, when writing the engagement's detection notes, or when a defender wants to know what to monitor. Teaches red teamers what is loud and blue teamers what to watch. Standard-tooling knowledge, no vendor engine.

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ADScanPro/Claude-AD2102026年8月25日 更新

acl-abuse

無料

Abusing Active Directory object ACLs (DACL/ownership) for privilege escalation and lateral movement (GenericAll, GenericWrite, WriteDACL, WriteOwner, AddMember, ForceChangePassword, and replication rights via DS-Replication-Get-Changes-All). Use when BloodHound CE shows an outbound control edge from a principal you own toward a higher-value object, and you want the exact bloodyAD/impacket command to weaponize that ACE, plus detection and remediation. DCSync is covered here strictly as a post-compromise technique, not a user entry path.

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ADScanPro/Claude-AD2102026年8月25日 更新

The order of operations for an Active Directory penetration test: setup, collection, exploitation, post-processing. Use this whenever you are planning or driving an AD assessment and need to know what to run before what and why (map before you exploit; harvest easy credentials before spraying to avoid lockouts; collect the graph before you reason about paths). Covers phase sequencing with standard tooling: netexec/nxc, impacket, certipy, bloodyAD, kerbrute, BloodHound CE. Invoke it at the start of an engagement, when deciding the next phase, or when a step feels out of 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。

ADScanPro/Claude-AD2102026年8月25日 更新

Active Directory Certificate Services (AD CS) escalation techniques ESC1 through ESC17, driven by hand with Certipy (ly4k). Use when the target runs a Certificate Authority and you want to find vulnerable certificate templates or CA misconfigurations, request a certificate that impersonates a privileged user, and know the exact certipy command, what each ESC actually checks, the Windows Event IDs that fire, and the remediation. ESC1 and ESC8 are the two you hit most in the field.

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。

ADScanPro/Claude-AD2102026年8月25日 更新

Authentication coercion (PetitPotam MS-EFSR, PrinterBug MS-RPRN, DFSCoerce MS-DFSNM) chained into NTLM relay (impacket ntlmrelayx) toward LDAP, AD CS web enrollment (ESC8), or SMB. Use when SMB signing is not enforced or LDAP channel binding is missing, and you want to force a privileged machine account to authenticate to your relay and turn that into RBCD, a DCSync-capable ACL grant, or a certificate. Includes the exact Coercer/ntlmrelayx commands, requirements, detection, and remediation.

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。

ADScanPro/Claude-AD2102026年8月25日 更新

Kerberos-based Active Directory attacks driven by hand with standard tooling (Kerberoasting, AS-REP roasting, and delegation abuse: unconstrained, constrained/S4U, RBCD). Use when the target has SPN-bearing service accounts, accounts without pre-authentication, or delegation configured on computer/user objects, and you want the exact impacket/Rubeus/bloodyAD command, the hash format and hashcat mode, the Windows Event IDs that fire, and the remediation to write up.

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ADScanPro/Claude-AD2102026年8月25日 更新

Sector overview, TAM estimation, competitive concentration HHI, regulatory landscape, industry analysis, market size analysis, sector trends, industry structure, market growth rate, sector profitability

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agentii-ai/agentii-investment-intelligence2072026年9月29日 更新

Competitive landscape analysis, competitor comparison, peer positioning, market share dynamics, competitive moat assessment, Porter five forces, industry competition, competitive advantage analysis, market positioning, strategic group mapping, compare competitors

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。

agentii-ai/agentii-investment-intelligence2072026年9月29日 更新

Think and work like an expert Geomorphologist. Use when a task calls for Geomorphologist judgment. Reasons from coupled form, process, and time and from rates, thresholds, and lag times through field mapping, lidar/SfM DEM morphometry (chi profiles, Ksn in LSDTopoTools/Landlab), cosmogenic nuclide dating (CRONUS-Earth, OSL, U-Th), and landscape-evolution models, while treating equifinality, inheritance, DEM artifacts, and steady-state assumed over transient response as first-class failure modes.

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。

K-Dense-AI/scientific-agents2052026年10月3日 更新

Think and work like an expert Geotechnical Engineer. Use when a task calls for Geotechnical Engineer judgment. Reasons from effective stress and LRFD/EC7 limit states through GDR/GBR/FDR deliverables, shallow and deep foundations (GEC 6/10/12), excavation support (DeepEX, LPILE), ground improvement, ASCE 7 liquefaction, observational-method triggers, and FHWA pile acceptance while treating DSC claims, setup vs. blow count, and GBR-vs-design conflation as first-class failure modes.

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。

K-Dense-AI/scientific-agents2052026年10月3日 更新

Think and work like an expert Deep Learning Scientist. Use when a task calls for Deep Learning Scientist judgment. Reasons from CNN/Transformer inductive bias, Li et al. loss landscapes, grokking/mode connectivity, and Kaplan/Chinchilla scaling (~20 tokens/param); designs ResNet/ViT/DiT/MoE/FlashAttention stacks with FLOPs-matched ablations; trains AdamW+cosine/WSD via Megatron-FSDP/DeepSpeed; evaluates FID/MMLU-Pro/MMLU-CF with lm-eval decontamination and Pineau/NeurIPS reproducibility checklists.

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。

K-Dense-AI/scientific-agents2052026年10月3日 更新

Think and work like an expert Biophysicist. Use when a task calls for Biophysicist judgment. Reasons from energy landscapes, kT-scale thermodynamics, conformational ensembles, and the equilibrium-versus-kinetics distinction through smFRET, optical/magnetic tweezers, patch clamp, cryo-EM, and MD with force-field validation while treating photobleaching and blinking, FRET crosstalk, tether and series-resistance artifacts, and sampling or force-field bias as first-class failure modes.

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。

K-Dense-AI/scientific-agents2052026年10月3日 更新

Think and work like an expert Biophysical Chemist. Use when a task calls for Biophysical Chemist judgment. Reasons from free energy landscapes, binding equilibria (K_d, ΔG = ΔH − TΔS), and probe–system coupling through ITC, SPR/BLI, smFRET, AUC, and global fitting (KinTek, SEDFIT) while treating probe perturbation, mass-transport-limited kon, aggregation-driven avidity, and two-state melting violations as first-class failure modes.

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。

K-Dense-AI/scientific-agents2052026年10月3日 更新

Think and work like an expert Agroecologist. Use when a task calls for Agroecologist judgment. Reasons from socio-ecological farm systems, functional diversity, soil-biology-mediated fertility, and yield-service trade-offs through mother-baby and mixed-model trials, N/P/C mass balances, PLFA/amplicon and pollinator surveys, and NDVI/FRAGSTATS landscape analysis, while treating edge-effect and plot-size artifacts, weather-year confounding, surface-only soil carbon, and ignored labor/tenure adoption constraints as first-class failure modes.

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。

K-Dense-AI/scientific-agents2052026年10月3日 更新

Conduct comprehensive market research combining web search, academic sources, news coverage, and industry data. Produces market sizing signals, competitive landscape overviews, trend analysis, and evidence-backed market opportunity assessments.

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。

sandbaseai/sandbase-skills2042026年9月26日 更新

Identify the technology stack powering any website — frameworks, CMS, analytics, hosting, CDN, payment processors, and marketing tools. Useful for competitive intelligence, sales prospecting, and technology landscape research.

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。

sandbaseai/sandbase-skills2042026年9月26日 更新

Analyze hiring patterns, job market demand, salary signals, and talent competition across LinkedIn and Apollo. Covers job posting analysis, company hiring velocity, role requirement trends, and competitive talent landscape mapping.

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。

sandbaseai/sandbase-skills2042026年9月26日 更新

Research companies with employee reviews, product reviews, industry classification, and competitive landscape through Akta. Covers company enrichment, employee satisfaction signals, product feedback, industry mapping, and news monitoring.

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。

sandbaseai/sandbase-skills2042026年9月26日 更新

End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.

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。

BioTender-max/awesome-bio-agent-skills2012026年7月2日 更新

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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。

BioTender-max/awesome-bio-agent-skills2012026年7月2日 更新

research

無料

Full-spectrum product research pipeline. Runs competitive analysis, technology trend scouting, user feedback analysis, and feature ideation. Trigger on: research, competitive research, market research, feature discovery, what should we build next, technology trends, user feedback, app store reviews, GitHub issues analysis, competitive landscape, product strategy.

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。

skillmds/skillmd1982026年10月9日 更新

Analyze competitors with strengths, weaknesses, and differentiation opportunities. Identifies direct competitors and maps the competitive landscape. Use when doing competitive research, preparing a competitive brief, or finding differentiation opportunities.

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。

skillmds/skillmd1982026年10月9日 更新

Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification over Firecrawl's paper index — largely biomedical and life-science literature from PubMed, bioRxiv, and medRxiv, plus arXiv preprints in CS, physics, and math. Use when the user wants a literature review, systematic review, survey of studies, paper summary, research landscape, or sourced synthesis from scholarly and industry publications, including clinical, drug, gene, disease, epidemiology, and public-health topics. Prefer this over a general web-research workflow whenever the evidence base is published papers rather than web pages.

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。

firecrawl/firecrawl-workflows1922026年8月21日 更新

Assess competitor defensibility and define our own moat strategy during PRD v0.3 Commercial Model. Triggers on requests to analyze competitor moats, define our defensibility, assess switching costs, identify vulnerabilities, find wedge opportunities, or when user asks "what's our moat?", "how defensible are they?", "where can we compete?", "switching costs?", "defensibility", "who to target". Consumes Competitive Landscape (v0.2) CFD- entries. Outputs CFD- entries for competitor moats and BR- entries for targeting rules and our defensibility strategy.

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。

mattgierhart/PRD-driven-context-engineering1812026年8月31日 更新