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Design an acoustic levitation system that uses standing waves to trap and suspend small objects at pressure nodes. Covers ultrasonic transducer selection, standing wave formation between a transducer and reflector, node spacing and trapping position calculation, acoustic radiation pressure analysis, and phased array configurations for multi-axis manipulation. Use when designing contactless sample handling for chemistry, biology, materials science, or demonstration purposes.

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Evaluate and compare levitation mechanisms for a given application through a structured trade study. Covers magnetic (passive diamagnetic, active feedback, superconducting), acoustic (standing wave, phased array), aerodynamic (hovercraft, air bearings, Coanda effect), and electrostatic (Coulomb suspension, ion traps) mechanisms. Use when selecting the most appropriate levitation approach for transport, sample handling, display, bearings, or precision measurement applications.

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Select an OCR/HTR engine by benchmarking candidates on the same labelled samples — source ground truth from open datasets (e.g. Hugging Face PAGE-XML corpora) or hand-transcription, wrap every engine behind a transcribe(image_path) -> str adapter (vision-LLMs via any OpenAI-compatible endpoint, dedicated HTR REST APIs, cloud OCR), and score with zero-dependency raw CER, lenient CER, WER, and a critical name/date token diff. Use when choosing an engine for handwriting or historical-document transcription, when published accuracy claims need honest verification on your own material, or when a records workflow must surface confident-but-wrong names and dates that a low aggregate CER hides.

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Generate and optimise novel small molecules with REINVENT 4 — de novo sampling from a chemical language model, scaffold decoration with LibInvent, fragment linking with LinkInvent, and similarity-constrained analogue generation with Mol2Mol. Use this skill to set up reinforcement-learning or curriculum runs, compose a multi-parameter scoring function from docking scores, predictive models, and physicochemical desirability, and read the resulting sampled sets. Also trigger on REINVENT, LibInvent, LinkInvent, Mol2Mol, scaffold hopping, R-group replacement, linker design, chemical language model, or reinforcement-learning molecule optimisation.

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DiffDock and DiffDock-L diffusion-based molecular docking. Use for blind protein-small-molecule pose prediction from a PDB file or sequence plus SMILES/SDF/MOL2, batch docking over a CSV of complexes, virtual screening triage, sampling multiple poses per complex, and reading the confidence score correctly. Also trigger on DiffDock, DiffDock-L, inference.py, confidence_model, samples_per_complex, ESM embedding preparation for docking, or blind docking without a defined box. Not for binding affinity prediction — the confidence score ranks pose plausibility, not potency.

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Design A/B tests with hypotheses, variants, sample sizes, and analysis plans. TRIGGERS - Use when user wants to run A/B tests, split tests, or experiment with variations.

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Use when you need authoritative classification, threat-name, MITRE ATT&CK mapping, dynamic-analysis or sandbox results on a file hash, or when you need network threat intelligence for a URL/domain/IP from ReversingLabs Spectra Analyze (A1000). Returns verdict, risk score, AV detection ratio, threat name, behavioural tags, and pivot candidates (parent containers, extracted files, related samples by family). Commonly invoked by /hash-investigation and /malware-analysis. Other agents/skills can chain this for deeper malware enrichment beyond VirusTotal.

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Prepare video URLs or local video files for GitHub Copilot analysis by extracting captions, sampled frames, contact sheets, and a prompt packet. Use when the user asks Copilot to watch, inspect, summarize, or diagnose a video, screen recording, demo, webinar, YouTube/Loom/TikTok/X/Vimeo URL, or .mp4/.mov/.mkv/.webm file.

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"Apply Partial Least Squares SEM (PLS-SEM) with reflective and formative measurement models to maximize explained variance in endogenous constructs. Use this skill when the user has small samples, formative indicators, or exploratory models, needs to assess AVE/CR/HTMT, or when they ask 'should I use PLS or CB-SEM', 'how do I handle formative constructs', or 'what is the path coefficient significance'.".

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"Apply Bayesian averaging to rank items by combining observed ratings with prior expectations. Use this skill when the user needs to rank items with varying review counts, build a 'top rated' list that handles low-sample items fairly, or implement IMDB-style weighted rating — even if they say 'weighted average rating', 'IMDB formula', or 'ranking with prior'.".

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Design rigorous A/B test plans with hypothesis, sample size calculation, Minimum Detectable Effect (MDE), randomization strategy, and decision rules. Includes guardrail metrics and rollout playbook. Use when planning product experiments, conversion optimization, or data-driven feature decisions.

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Web shell samples for detection and analysis: PHP, ASP, ASPX, JSP, Python, Perl shells. Use for security research and detection system testing.

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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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Design, analyze, and document A/B tests for conversion, onboarding, pricing, lifecycle, and product experiments. Use when the user asks for `/ab-test-setup`, experiment design, sample size, statistical significance, A/B test analysis, ICE-scored test backlogs, or avoiding common testing mistakes.

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Detect and rewrite AI writing patterns to make content sound authentically human. Supports voice calibration from the author's own writing samples. Use when asked to humanize text, remove AI patterns, calibrate voice, audit content for AI detectability, or check if writing sounds like ChatGPT. Triggers: /humanize, /voice-calibrate, /audit-ai

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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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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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Performs fast first-pass triage of an unknown file to decide depth of analysis: file type identification, hashing, reputation lookup, packing/entropy check, and string review. Activates for requests to triage, classify, or do initial assessment of an unknown or suspicious file.

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Reverse engineers malware written in less-common compiled languages (Nim, Crystal, V, Zig, D) by detecting language-specific runtime signatures, name-mangling, and runtime strings to orient analysis. Activates for requests to reverse Nim malware, identify the source language of an exotic binary, or analyze Crystal/V/Zig/D compiled samples.

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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.

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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.

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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.

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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.

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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。