Results for “binary-classification”

Classifies situations into 256 binary states and maps each to a prescribed action, reporting the pattern, decimal, name, range, and action to execute.

Cardinality and model family jointly determine the encoding.

Pwn/Binary kategorisi SKILL.md — BOF, ROP, kernel exploit araçları kurma rehberi

Extract false-positive and false-negative gaps from VLM binary-classification-question predictions by comparing model responses against ground truth, producing a structured JSONL file and summary report for downstream root-cause analysis.

Train or fine-tune sentence-transformers models for retrieval, similarity, clustering, classification, and reranking, with support for bi-encoders, cross-encoders, and sparse encoders.

Diagnose MSBuild build performance bottlenecks using binary log analysis, covering timeline analysis, performance summary interpretation, and seven common bottleneck categories.

Proactively hunt for adversary abuse of legitimate system binaries (LOLBins) to execute malicious payloads while evading detection.

Tier vocabulary from a text or topic into everyday, academic, and technical categories with teaching priorities. Use when pre-teaching vocabulary or identifying language barriers in a text.

Migrates symbols and reverse-engineering results from an older binary version to a newer one using LLM-based structured diffing of disassembly and pseudocode, enabling rapid offset and function-name mapping when PDBs are missing.

Add the /bl switch to MSBuild-based commands to generate binary logs for build diagnostics and performance analysis.

Train, evaluate, distill, quantize, export, and run inference for PyTorch-based TAO image classification models with support for multiple backbones.

Fine-tune and deploy CLIP vision-language models for zero-shot classification, image-text retrieval, and embedding extraction with ONNX and TensorRT support.

Train, evaluate, export, and run inference for pose classification models using ST-GCN on skeleton keypoint sequences.

Specialized CTF workflow for reverse engineering, malware analysis, DFIR, firmware, pwnable, and native exploit challenges under sandbox assumptions.

Authorized reverse engineering of custom binary protocols, Protobuf/gRPC, WebSocket frames, and PCAP-driven protocol recovery with structured workflow and tooling.

Organize, query, validate, and convert neuroscience and biomedical data using the Brain Imaging Data Structure (BIDS) standard.

Classify curriculum content into Know, Understand, and Do categories to align teaching and assessment approaches. Use when planning units, writing objectives, or selecting assessment methods.

Multi-dimensional lead qualification scoring. Evaluates leads against BANT criteria, firmographic fit, behavioral signals, and intent indicators. Outputs qualified/disqualified verdict with detailed reasoning.

Provides a command-line workflow for binary analysis with radare2, covering reconnaissance, disassembly, function analysis, string and import inspection, patching, binary diffing, and r2pipe scripting across PE, ELF, Mach-O, DEX, and WASM formats.

Evaluate scientific claims and evidence quality by assessing experimental design, identifying biases and confounders, and applying evidence grading frameworks like GRADE and Cochrane Risk of Bias.

融合分类系统与潜能蒸馏能力，从复杂现象中提取本质并进行结构化分类，促进跨领域知识整合与创新。

Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

基于 anchor 论文、topic 关键词或当前 wiki 状态，产出一份排好序的候选论文 shortlist，供用户或上游 skill 决定是否进一步 `/ingest`。当用户问 "接下来该读什么"、"找和这篇相似的论文"、"推荐相关工作"、"这个方向周围有什么" 时触发；`/ingest --discover` 也会内部调用本 skill。本身不 ingest，只提出候选。

Reads and preprocesses 5-minute stock candlestick CSV data, then clusters the time series using tslearn's TimeSeriesKMeans, including data cleaning, percentage change calculation, model training, saving, and representative sample extraction.

Analyze ELF binaries for exploitation vectors using checksec, ROPgadget, and pwntools for buffer overflow and ROP chain development during authorized security testing and CTF challenges.

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

BISON v2.0 — Conviction Holder (Hardened). Top 10 assets by volume. All signals are score contributors — no hard gates. Scanner enters via create_position internally (Wolverine pattern). RatchetStop exits. Thesis exit REMOVED. v2.0: every hard gate converted to score contributor, ensureExecutionAsTaker=false, conviction-scaled margin 25-37%.

Reverse engineer malware binaries using NSA's Ghidra disassembler and decompiler to understand internal logic, cryptographic routines, C2 protocols, and evasion techniques at the assembly and pseudo-C level.

融合分类系统与逻辑抽象能力，构建基于逻辑的分类体系，支持逻辑驱动的分类需求和逻辑分析。

融合分类系统与规则抽象能力，构建基于规则的分类体系，支持规则驱动的分类需求和规则执行。

選択肢(A/B/C)の深掘り比較→淘汰→推奨で判断負担を下げ判断の質を上げるスキル。5段階（L1固定3点/L1.5案拡張Diverge・自動/L2評価軸マトリクス/L3複数LLM弁証論/L4過去判断照合）。 「比較して」「深掘りして」「メリデメ教えて」「お勧めは？」「徹底的に」「過去の判断と照合」「前にどう決めたっけ」「/sentaku」等で発火。teian(浅)の深掘り要求を受け取り、brainstorming(深:設計全体)と棲み分け。

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

融合分类系统与虚空凝视能力，通过批判性思维突破传统分类边界，创造新的分类视角。