Results for “randomization”

Provides GSAP utility functions for math, value mapping, randomization, snapping, and unit parsing in animations.

Randomly selects winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests with fair, transparent selection.

Identify cryptographic algorithms, key generation flaws, and potential decryption opportunities in ransomware samples using static and dynamic analysis.

Use before sensitive, clinical, identifying, or raw interview data is placed in front of an AI tool, when transcripts or student materials need de-identification, or when a data-minimization gate must run before any analysis or drafting.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Write effective fuzzing harnesses across languages to improve code coverage and find bugs in your system under test.

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

Reviews parsers, deserialization, uploads, archives, YAML/JSON/XML/pickle, paths, templates, SSRF, and unsafe loaders.

Design experiments and studies before data collection — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable.

Humanize text: strip AI-isms and add real voice.

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。

Prevents AI-introduced regressions with sandbox-mode API testing, automated bug-check workflows, and patterns that catch blind spots where the same model writes and reviews code.

Analyzes malware-generated network traffic from PCAP files to identify C2 protocols, data exfiltration, DNS tunneling, and beaconing patterns using Wireshark, Zeek, Suricata, and Python.

Humanize text: strip AI-isms and add real voice.

Design and implement Monte Carlo methods for uncertainty quantification, risk analysis, and probabilistic simulations across scientific and financial domains. Use when "monte carlo, random sampling, uncertainty quantification, risk analysis, stochastic simulation, MCMC, variance reduction, probabilistic, " mentioned.

Sets up internationalization by extracting hardcoded user-facing strings to locale files, auto-detecting the framework, configuring the appropriate i18n library, handling pluralization, formatting, and RTL support.

Use when you need to characterize score distributions of a profile HMM on random sequences, such as calibration checks, benchmarking, or filter-behavior experiments.