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概要と使いどころ
Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.
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Map existing business processes using BPMN 2.0 notation or simplified swimlane diagrams. Identify waste, handoffs, decision points, and improvement opportunities using Lean principles (8 Wastes). Use when documenting workflows for SOP creation, automation assessment, or process improvement. Do not use for software architecture diagrams — use architecture tools instead.
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Compare policies, procedures, onboarding files, or operating evidence against a named compliance framework such as AML/KYC, privacy, internal controls, or audit readiness. Use when a team needs a gap matrix, control checklist, remediation priorities, or evidence request list. Do not use when no target framework is specified or when formal legal or regulatory sign-off is required.
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。
Codex 环境专用的视觉物料生产：PPT / Keynote / HTML deck、公众号封面与正文配图、B站/YouTube 视频封面。存在的全部理由是走 Codex 内置 image_gen，不花 nano banana / Gemini API 的钱——不在 Codex 环境就别用。用户说做PPT、幻灯片、演示文稿、deck、公众号封面、视频封面时触发。
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Python engineering skill for implementing, debugging, testing, packaging, typing, reviewing, and refactoring Python applications, scripts, APIs, automation, data processing, CLIs, and libraries. Use for .py files, pyproject.toml, requirements files, pytest, async Python, type hints, packaging, dependency management, and Python performance or reliability work.
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 sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary testing.
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Execute advanced Man-in-the-Middle (MITM) NTLM Relay attacks specifically targeting environments where SMB Signing is purportedly enabled but improperly configured (e.g., exclusively required on Domain Controllers but NOT enforced universally on standard Workstations or File Servers). Utilize powerful tools like Responder and Impacket's NTLMRelayX to coercively capture NTLMv2 authentications (via LLMNR, WPAD, or Coercion methods like PetitPotam) and seamlessly relay them across the network identically yielding immediate Local Administrator command execution.
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。
Java "Ghost Bits" / Cast Attack playbook (Black Hat Asia 2026). Use when attacking Java services where 16-bit char is silently narrowed to 8-bit byte to bypass WAF/IDS for SQL injection, deserialization RCE, file upload (Webshell), path traversal, CRLF injection, request smuggling, and SMTP injection. Affects Tomcat, Spring, Jetty, Undertow, Vert.x, Jackson, Fastjson, Apache Commons BCEL, Apache HttpClient, Angus Mail, JDK HttpServer, Lettuce, Jodd, XMLWriter and re-enables many "patched" CVEs through WAF bypass.
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Cross-Site Request Forgery (CSRF) tricks authenticated users into submitting forged requests to a target application by exploiting browser automatic cookie attachment. Detect via missing or predictable CSRF tokens in state-changing requests (POST/PUT/DELETE), absent `SameSite` cookie attributes, and JSON endpoints accepting `text/plain` Content-Type. Test using HTML auto-submitting forms, XHR requests, and CORS-enabled fetch. Tools: Burp Suite (Generate CSRF PoC), OWASP ZAP.
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Identify and exploit default or weak credentials on web application login forms, admin panels, CMS backends (WordPress wp-admin, Joomla, Drupal), and embedded device management interfaces. Signals include framework fingerprinting (WhatWeb, Wappalyzer, Nikto), exposed admin paths from robots.txt/dirbusting, and weak password policy acceptance of "Password1" or "123456". Tools: Burp Suite Intruder, Hydra, Medusa, OWASP ZAP.
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。
Abuse Azure Managed Identities from compromised Azure Virtual Machines (VMs), Functions, or App Services to seamlessly request valid, highly-privileged Azure AD access tokens and laterally move throughout the cloud environment without requiring explicit credentials.
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。
Full ASVS 5.0 compliance assessment against a codebase and/or architecture diagrams. Reads all 346 controls from the companion CSV, performs targeted code analysis per control, and produces a complete matrix marked COMPLIANT / NON_COMPLIANT / NOT_RELEVANT — with per-control reasoning and evidence (code snippets, file:line references, diagram observations). Outputs a reviewed CSV matrix and a self-contained HTML evidence report.
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Exploit Active Directory Certificate Services (AD CS) misconfigurations, specifically ESC1. By requesting a certificate based on a overly permissive template that allows the enrollee to supply a Subject Alternative Name (SAN), an attacker can impersonate highly privileged users (like Domain Admins) and seamlessly escalate privileges across the entire AD environment.
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Identify and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops) to bias the AI.
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Triages an Aikido security findings CSV against a local codebase. For each finding, reads the flagged file, traces the code path, and verdicts it as KEEP OPEN or CLOSE with a specific reason. Outputs a reviewed CSV and a self-contained HTML evidence report. Run this at the end of a pentest when an Aikido CSV is available.
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。
Curate, review, or repair ai-gene-review ModuleReview YAML documents under modules/, including pathway/module boundary setting, parts and variant modeling, annoton-level molecular functions, representative UniProt/PTN grounding, module deep-research provenance, validation, rendering, and project batch updates.
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Review AI Gene Review pull requests and curation changes for GO term quality, evidence support, core function identification, and annotation action justification. Use for PR review comments, review follow-up automation, and QA of gene review YAML.
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Review computational protein or gene function predictions and fill PredictionReview YAML files using the COR/CNN/LSP/UNC/PLI/NPI/REP biological-validity taxonomy. Use for ProtNLM, DeepECTF, BioReason/GO-GPT, InterPro2GO, PANTHER/IBA, CLEAN, GloEC, MAPred, ProteinInfer, or other predicted EC/GO annotations, including audits of the evidence and reasoning in existing prediction reviews.
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Use this agent when you need to synthesize and distill the core functions of a gene from multiple information sources (textual summaries, reviewed annotations, bioinformatics analysis) and populate the `core_functions` section of a gene review YAML file. This agent should be called after completing the gene research, annotation review, and bioinformatics analysis phases of a gene review project.
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Work with BioReason-Pro predictions and reasoning traces for protein function. Use when: comparing BioReason outputs against curated reviews, converting web exports to YAML, reviewing reasoning trace quality, or running the SFT/RL comparison. Triggers: "bioreason", "GO-GPT predictions", "reasoning trace review", "bioreason comparison", "SFT vs RL".
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Use this agent when you need to systematically review existing GO annotations for a gene and make curation decisions based on literature evidence and functional analysis. This agent should be called after seeding the SPECIES/GENE/GENE-ai-review.yaml file, which seeds each annotation with `action: PENDING`; these should all be manually reviewed.
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Upgrade Flutter and package dependencies in this repository while keeping .fvmrc, pubspec.yaml, code generation, CI, and tests aligned. Use when bumping Flutter, Dart constraints, direct dependencies, or generator toolchains.
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。
Maestro mobile E2E testing - YAML flows, selectors, flow control, environment variables, JavaScript expressions, device interactions, Maestro Studio, Maestro Cloud CI, tags, test suites
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Testing and evaluation framework for LLM prompts and applications -- promptfooconfig.yaml, assertions, model-graded evals, red teaming, CI/CD integration, custom providers, and comparative evaluation
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。