1.6万 件 ・ 関連度順
概要と使いどころ
claude-org-runtime のリリース (release-* タスク / vX.Y.Z タグ発行) を、 同 PyPI 発行を受ける claude-org-ja 側の expectation 同期と ペアで設計・委譲・完了させるためのワークフロー。 DEFAULT_NOTIFY 値・classifier vocabulary・role_configs_schema (ja 側ミラー: org_extension_schema)・attention.example.json テンプレ等が 変わるリリースで発動する。 CI cascade を予測・予防し、ja-side の red を回避することを目的とする。
Run a Busabase-backed job-search outreach desk with a bundled Hono App-in-Skill deployable to AirApp. Use when the user invokes $kelly-jobhunt or /kelly-jobhunt, wants to turn a resume into a structured profile and a typeset PDF, wants to find target companies and their contact addresses instead of applying through a job board, wants one tailored application email drafted per company, or wants to review and approve each send. Four subcommands - setup, profile, research, send - plus the desk itself. One company receives exactly one email; mailbox credentials live in the Busabase Vault and never reach browser code.
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。
Detect LSASS credential dumping, SAM database extraction, and NTDS.dit theft using Sysmon Event ID 10, Windows Security logs, and SIEM correlation rules
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。
Perform DCSync attacks to replicate Active Directory credentials and establish domain persistence by extracting KRBTGT, Domain Admin, and service account hashes for Golden Ticket creation.
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。
Implement Microsoft's Enhanced Security Admin Environment (ESAE) tiered administration model for Active Directory. Covers Tier 0/1/2 separation, privileged access workstations (PAWs), administrative f
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。
Responds to phishing incidents by analyzing reported emails, extracting indicators, assessing credential compromise, quarantining malicious messages across the organization, and remediating affected accounts. Covers email header analysis, URL/attachment sandboxing, and mailbox-wide purge operations. Activates for requests involving phishing response, email incident, credential phishing, spear phishing investigation, or phishing 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。
Plan and execute a comprehensive red team engagement covering reconnaissance through post-exploitation using MITRE ATT&CK-aligned TTPs to evaluate an organization's detection and response capabilities.
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。
Build and configure a resilient command-and-control infrastructure using BishopFox's Sliver C2 framework with redirectors, HTTPS listeners, and multi-operator support for authorized red team engagements.
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。
Discover and use shared team skills stored in PostHog. Use when the user asks to list, browse, load, or manage "shared skills", "team skills", or references the "skills store" / "skill store".
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。
Add PostHog log capture to track application logs. Use after implementing features or reviewing PRs to ensure meaningful log events are captured with structured properties. Also handles initial OTLP exporter setup if not yet configured.
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。
Builds and validates predictive time-to-event models on clinical and omics data with penalized Cox, random survival forests, gradient-boosted and deep survival models, and prediction-grade evaluation (Uno's C, time-dependent AUC, integrated Brier, calibration, competing risks). Use when building an individualized risk predictor or prognostic omics signature, choosing a survival model, or evaluating one beyond the C-index. For Kaplan-Meier, log-rank, and classical Cox hazard-ratio inference in a trial see clinical-biostatistics/survival-analysis.
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。
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
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。
End-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic data.
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。
Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.
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。
Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.
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。
Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.
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。
Designs PROTACs, molecular glues, and bivalent degraders with explicit handling of E3 ligase choice (VHL, CRBN, IAP, MDM2, KEAP1), linker design (length, composition, rigidity), ternary complex prediction (PRosettaC, DeepTernary, AlphaFold3 with constraints), cooperativity (alpha), DC50 / Dmax characterization, hook effect, and prediction-experiment reconciliation. Use when designing targeted protein degraders, planning linker SAR, predicting ternary complex stability, or building generative degrader workflows.
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。
Build AI inventory prediction. TRIGGERS - Use when user needs help with ai-inventory-predictor related tasks.
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。
Build AI churn prediction systems. TRIGGERS - Use when user needs help with ai-churn-predictor related tasks.
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。
Prepare for educational accreditation. TRIGGERS - Use when user needs help with accreditation-preparation related tasks.
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。
Prepare for educational accreditation. TRIGGERS - Use when user needs help with accreditation-prep related tasks.
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。
Extract structured fields from credit card statements documents.
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。
Extract structured fields from credit invoice documents.
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。
Controls the traffic-light actors on a running CARLA server — set a light green/red/yellow/off, freeze all lights (optionally all-green), set phase timing (green/yellow/red durations), and reset to the normal cycle. Target all lights, one by id, all in a junction, or the nearest. Use when the user asks to "make the light red", "freeze all lights green", "change the light timing", "turn the traffic lights off", or "reset the lights". This is the light actors, not the Traffic Manager (that's control-traffic).
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。