「sample」の検索結果

Locates candidate encryption keys in a binary by finding high-entropy fixed-size regions (16/24/32 bytes) near cryptographic constants or crypto API references, and surfacing printable key/passphrase strings. Activates for requests to extract encryption keys, find an AES key in a sample, or recover hardcoded crypto material.

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。

Enriches indicators with context from threat-intel sources: planning lookups against reputation, passive DNS, WHOIS, and sandbox/sample databases, scoring confidence, and avoiding operational-security mistakes that tip off adversaries. Activates for requests to enrich IOCs, add threat context, or plan indicator lookups safely.

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。

Uses capa to identify malware capabilities from a binary: running rule-based detection over disassembly, mapping matched capabilities to MITRE ATT&CK and MBC, and interpreting results to focus deeper analysis. Activates for requests to run capa, generate a capability report, or map a sample's behaviors to ATT&CK automatically.

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。

Identifies cryptographic algorithms embedded in a binary by scanning for well-known constants and tables (AES S-box, SHA-256/MD5 init constants, ChaCha sigma, CRC32 table, base64 alphabet). Activates for requests to identify crypto in a binary, find AES/SHA constants, or detect which encryption algorithm a sample uses.

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。

Extracts ASCII and Unicode strings from a binary and classifies them into investigative categories: URLs, IPs, file paths, registry keys, mutexes, commands, and API names, prioritizing the analyst's attention. Activates for requests to extract strings, classify strings output, or pull human-readable artifacts from a sample.

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。

Detects packing and encryption by measuring Shannon entropy across a binary's sections and regions: flagging high-entropy executable sections, entropy spikes, and size/raw-vs-virtual anomalies that indicate compression or encryption. Activates for requests to measure entropy, detect packing, or assess whether a sample is compressed/encrypted.

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。

Identifies a sample's true file type independent of its extension: matching magic bytes and structural signatures, detecting container/archive formats, and flagging extension-content mismatches used to disguise malware. Activates for requests to identify a file type, check magic bytes, or detect a disguised/spoofed extension.

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。

Manually unpacks a runtime-packed Windows binary by finding the original entry point (OEP) through tail-jump and entropy analysis, then guiding a memory dump at OEP. Activates for requests to manually unpack a sample, find the OEP, or unpack a custom/unknown packer that generic tools cannot handle.

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。

Reverse engineers binaries using radare2/rizin interactively, covering analysis initialization, navigation, disassembly and graph views, patching, and the visual mode workflow. Activates for requests to reverse a binary with radare2 or rizin, learn r2 commands, or navigate and disassemble a sample in r2.

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。

Analyzes loader and dropper samples by identifying staging behavior — embedded or downloaded next-stage payloads, decode/decrypt routines, and the execution mechanism used to run the next stage. Activates for requests to analyze a loader or dropper, find a staged payload, or map multi-stage delivery behavior.

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。

Automates radare2/rizin analysis through r2pipe to script function enumeration, string and import extraction, and cross-reference queries across samples programmatically. Activates for requests to automate radare2 with r2pipe, script rizin analysis, or batch-extract functions and xrefs from binaries.

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。

Analyzes bootkit and rootkit samples by identifying boot-process tampering (MBR/VBR/ UEFI), kernel-mode components, and stealth hooking techniques from static indicators. Activates for requests to analyze a bootkit or rootkit, examine MBR/UEFI tampering, or identify kernel-mode stealth components.

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 reusable malware configuration extractors by applying a declarative JSON spec (offsets, decode layers, field definitions) to a sample to produce structured, defanged config. Activates for requests to build a config extractor, write a reusable family config parser, or run a spec-driven configuration extraction.

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。

Reverses .NET/managed malware: decompiling MSIL back to C#, defeating common .NET protectors and string encryptors, and tracing reflection-based loaders to recover the real payload. Activates for requests to analyze a .NET sample, decompile MSIL, or unpack a managed loader.

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。

Analyzes infostealer samples by mapping the browser, credential store, wallet, and application paths they target, and the exfiltration channel used, from static strings and behavior. Activates for requests to analyze an infostealer, identify targeted credential stores, or map stealer collection and exfiltration behavior.

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。

Analyzes malicious and vulnerable Windows kernel drivers (.sys) by parsing the PE for the native subsystem, identifying DriverEntry/IRP dispatch and IOCTL handlers, and flagging BYOVD and kernel-callback abuse. Activates for requests to analyze a Windows driver, examine a .sys sample, or assess a BYOVD/kernel driver threat.

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。

Analyzes how a ransomware sample encrypts files: identifying the crypto scheme (symmetric/asymmetric/hybrid), key handling, file targeting and extension/marker changes, shadow-copy deletion, and ransom-note artifacts. Activates for requests to analyze ransomware encryption, assess decryptability, or study ransomware behavior.

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Judge whether a change made things better with demonstrable proxy measures tied to the mechanism it targets, not wall-clock time. Load before choosing acceptance measures for any before/after or A/B claim — CI and fleet speed-ups, build speed, DSP/audio quality, render fidelity. Each proxy names its data source, detection floor and sample size, and every zero is paired with a control on the same instrument.

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。

The measurement surface for ALL Pulp DSP and audio-pipeline work — read it BEFORE writing or gating DSP, not only when something already sounds wrong. Covers the C++ harness (signal generators, metrics, assertions, RenderScenario, contracts), the offline Audio Doctor (magnitude/frequency response, THD/THD+N, phase/group delay), and their Python sibling the Audio Quality Lab (tools/audio/quality-lab — null residual + alignment, LTAS log-spectral distance, spectral flux/centroid, HNR, Theil-Sen drift slope, Kaiser-sinc resampling, license-guarded corpus, regression-net ratchet). TRIGGER on AUTHORING work — "build/design an oscillator/filter/synth/effect", "add a DSP module", "what should the acceptance gate be", "how do I measure aliasing / anti-aliasing / alias floor", "null against a reference", "is this DSP correct", "choose a tolerance", "golden/regression corpus for audio", "measure drift or jitter", "A/B two renders" — AND on DEBUGGING work — "is there sound / no audio / I hear nothing", "does this filter/compressor/synth/delay produce the right signal", "prove the DSP / prove the contract", "measure the frequency response", "what's the THD / is it distorting", "what's the group delay / phase response / measured latency", "magnitude response curve", "render a test tone and assert", "audio regression", "64-frame works but 128 is silent", "sample-rate change pitch-shifted it", "describe what's in this buffer", "audio doctor", "compare before/after a DSP refactor". Reach for this BEFORE hand-rolling any FFT, null test, alias measurement, pitch tracker, or golden-render script — most of it already exists in one of the two lanes. Test/tool layer over HeadlessHost — deterministic, no audio device, no speakers. Off the realtime thread entirely.

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 comprehensive epigenomic profiles for tissues or cell types using ENCODE data. Use when the user wants to characterize chromatin states, assemble histone modification panels, create epigenomic landscapes, run ChromHMM segmentation, identify super-enhancers or bivalent domains, profile regulatory elements across a biosample, or understand epigenetic regulation in a specific biological context. Covers histone marks, chromatin accessibility, TF binding, transcription, DNA methylation, and 3D genome structure.

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Guide for multi-experiment batch operations: QC screening, batch download, comparison, and report generation across many ENCODE experiments simultaneously. Use when users need to process 5+ experiments together, create experiment comparison tables, perform batch quality checks, or generate summary reports. Trigger on: batch analysis, multiple experiments, bulk processing, experiment comparison, batch QC, multi-sample, batch download, experiment table, summary report, collection 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。

Generate accurate Volcengine SDK examples by locating an API through API Explorer search, fetching its swagger, and calling api/common/explorer/make-code with user-provided Params. Use when the user asks how to write Volcengine SDK code, generate SDK samples, or call a Volcengine API in Python, Go, Java, PHP, cURL, Node.js. Supports Chinese and English API names such as "角色扮演", "AssumeRole", or "STS AssumeRole". Also use for explicit SDK configuration questions about retry, timeout, AK/SK, STS, AssumeRole, temporary credentials, proxy, connection pooling, SSL, debug mode, request signing, response parsing, and error handling. If the user only needs API parameters, enum values, required fields, error codes, response schemas, pagination, or API comparisons, hand off to volcengine-api. If they need CLI-based operations, hand off to volcengine-cli.

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。

Generate images via the Stable Diffusion WebUI / Forge HTTP API (AUTOMATIC1111-compatible `/sdapi/v1/*`). Use when the user wants to (1) discover or pick a model / extra module (TE/VAE) / sampler / scheduler / style preset from a running sd-webui server, (2) generate an image with a given prompt (txt2img), (3) check generation progress, (4) cancel/interrupt an in-flight generation, (5) inspect or change a global sd-webui option (e.g. active checkpoint), or (6) test connectivity. This skill talks to a *generic* sd-webui-compatible server (AUTOMATIC1111, Forge, reForge, sd-webui-forge-classic). Do NOT trigger for requests that are purely writing the prompt itself.

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。

Craft high-quality natural-language image prompts for any modern text-to-image or image-edit model that accepts flowing English. Trigger when the user wants help writing, rewriting, improving, or translating an English natural-language image prompt — including "write me an image prompt", "improve this image prompt", "describe this scene for an image model", or "convert these tags into a natural language prompt". Do NOT trigger for requests that are purely about dispatching to an image API, choosing samplers/schedulers, picking LoRAs, or setting up ControlNet — those belong to a runtime skill.

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。