「engine」の検索結果

Detects weak reverse engineering and tampering protections in mobile apps (Android/iOS). Trigger on: root detection bypass, jailbreak detection bypass, Frida detection, debugger detection, anti-debugging, ptrace, sysctl, emulator detection, code obfuscation absent, debug symbols present, get-task-allow, ProGuard disabled, R8 disabled, string encryption, integrity check, file tampering, repackaging, dynamic instrumentation, runtime hook, Magisk hide, Magisk, frida-server, objection bypass, signing verification, apk resign. Covers MASVS-RESILIENCE-1/2/3/4.

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Execute advanced LLM Jailbreaking techniques using roleplay, nested environments (virtual machines), and complex personas to completely bypass safety constraints and ethical alignments embedded in AI models.

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。

Analyze and detect synthetic media, including deepfake videos, AI-generated images, and cloned voice audio. Use this skill when investigating potential disinformation campaigns, verifying the authenticity of digital evidence, or assessing social engineering attacks leveraging synthetic media (e.g., vishing with voice clones, spear-phishing with deepfake video calls). Covers artifact analysis, frequency detection, metadata tracing, and automated detection tools.

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Execute and analyze malware samples within a highly controlled, instrumented sandbox environment to observe their true behavior, network communications, file system modifications, and registry changes in real-time.

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Browser and V8 exploitation playbook. Use when exploiting JavaScript engine vulnerabilities including JIT type confusion, incorrect bounds elimination, and V8 sandbox bypass to achieve renderer RCE and sandbox escape in Chrome/Chromium.

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。

Advanced techniques for bypassing LLM safety filters, instruction tuning, and system prompt restrictions using specialized linguistic constructs, hypothetical scenarios, and persona adoption.

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。

High-throughput inference on FriendliAI — optimized serving engine for low-latency, cost-efficient LLMs.

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。

Run the data science workflow end to end — framing, exploration, feature engineering, modeling, validation, and communicating results. Use when turning raw data into decisions or predictions.

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。

Strengthen defensive security operations — detection engineering, hardening, monitoring, and continuous defense improvement.

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Ship AI features to production — model selection, evals, latency/cost optimization, guardrails, monitoring, and iteration loops. Use when turning an LLM prototype into a reliable product feature.

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Build data-centric LLM applications with indexing and retrieval frameworks — document ingestion, indices, query engines, and RAG pipelines. Use when the core problem is getting the right data in front of the model.

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Build agents on OpenAI's platform patterns — assistants-style threads, function calling, structured outputs, and multi-step tool workflows. Use when working with OpenAI models as the agent engine.

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Optimize content for AI answer engines (AEO/GEO): citation-friendly structure, factual precision, and measuring AI visibility. Use when AI search drives discovery.

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Analyze a reference video and reverse-engineer its style into a reusable Seedance 2.0 prompting template for the Novoads API. The output is a new formula file — like seedance-2-ugc.md — that captures the video's structure, pacing, camera work, edit style and tone so it can be recreated with any product, any person, in any setting. Use this whenever someone provides a video they want to use as a style reference, says "I want to make videos like this", "deconstruct this video", "turn this into a template", "analyze this style", or drops a video file and wants to recreate that format repeatedly.

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Clones a STATIC IMAGE ad into finished ads for your own product, into a reusable prompt TEMPLATE, or both — the template is what makes the ads and the ads are what prove the template. Round-trips the frame through gpt-image-2, nano-banana-pro or reve-2.1 until it reproduces, replaces every brand-specific with a {placeholder}, then fills it with your product pinned to a real photo. Use for "clone this ad for my product", "rebuild this competitor's ad for us", "make me ads like this", "reverse engineer this ad", "make this ad reusable", "add this to my prompt library", or "clone my competitors' ads" with NO file attached — it sources one via spy-competitor-ads rather than asking which. **Among cloners the source's medium decides**: a still, or nothing attached, lands here; "clone this ad" plus a VIDEO is clone-video-ad. NOT for generating an ad from a brief (chatgpt-image-ad, nano-banana-image-ad), nor animating a still as-is (image-to-motion). Refuses to render your product without a real photo of it.

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Strip workflow notes, engineering meta text, status remarks, workspace references, and other process-facing language from any final deliverable such as a PPT, DOCX, PDF, report, manuscript, or submission package. Use when preparing a final export, submission-ready artifact, or client-facing handoff.

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在真实代码库中实现和演进软件。适用于用户希望构建功能、修复重大缺陷、重构或调整代码结构、完成迁移，或将规格说明或 Issue 落实为可用实现的场景。

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Uses symbolic execution (angr) to solve constraint-based malware problems — finding inputs that reach a target branch, recovering keys/passwords from a check routine, and brute-forcing opaque predicates — by generating a ready-to-edit angr harness. Activates for requests to use symbolic execution, solve a malware unlock check with angr, or recover an input that reaches a code path.

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Automates IDA Pro analysis with IDAPython by generating scripts that enumerate functions, decode strings, rename by signature, and export analysis results, and by documenting the headless idat batch invocation. Activates for requests to script IDA Pro, write IDAPython automation, or run IDA in batch/headless mode.

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Tunes noisy detections by analyzing historical alert data to quantify false-positive sources, recommend allow-list filters and threshold adjustments, and measure precision impact. Activates for requests to tune a detection, reduce false positives, or analyze alert noise and recommend filters.

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Identifies and unpacks packed binaries: detecting packers from section names and entropy, automatically unpacking UPX, and manually unpacking custom packers by finding the OEP and dumping. Activates for requests to unpack a packed sample, detect a packer, or recover the original binary from UPX or a custom packer.

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Validates detection coverage by mapping Atomic Red Team tests to ATT&CK techniques, checking which of your detections cover each tested technique, and reporting coverage gaps. Activates for requests to validate detections with atomic tests, map Atomic Red Team to coverage, or find detection gaps against tested techniques.

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Structures a clear, actionable malware analysis report covering summary, sample identity, capabilities, IOCs, ATT&CK mapping, and detection guidance for both technical and decision-making audiences. Activates for requests to write, structure, or review a malware analysis or reverse-engineering report.

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Converts hunt findings into portable Sigma detection rules: choosing the right logsource, expressing robust selection/filter logic, setting level and ATT&CK tags, and validating before converting to a SIEM query. Activates for requests to write a Sigma rule, convert a hunt to a detection, or create portable SIEM detection logic.

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。