「engineering」の検索結果

Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features

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Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

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Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

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Operational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and safety. Integrates recent advances: PEFT/LoRA fine-tuning, hybrid RAG handoff (see dedicated skill), vLLM 24x throughput, multi-layered security (90%+ bypass for single-layer), automated drift detection (18-second response), and CI/CD-aligned evaluation.

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Deliberate-choice procedure for a medium-to-high-stakes engineering fork — when the first plausible solution (the instinct, the default next-token pull) would be costly to get wrong. Fires AUTONOMOUSLY: invoke proactively whenever a fork fits, never wait to be asked — mechanical triggers include a fix failing for the 2nd-3rd time, adding a dependency, schema/migration design, deleting or deprecating things others depend on, changing a public API, choosing an architecture or stack. Refuse the premature collapse: hold real options open (urge · contrarian · synthesis · out-of-box · intuitive dots · precedent · first-principles), ground each branch in at least one fact from outside the model (codebase, docs, benchmark, spike), future-model consequences (blast radius, reversibility, maintenance, pre-mortem), collapse by deliberate choice, then try to refute the winner before acting. Log the decision and rejected branches in docs/decision_logs/. Not for routine calls — a decision worth more than one forward pass.

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Record a hard-won engineering lesson after a bug or mistake is resolved. Use when a bug resisted ATLAS until Boss's guidance cracked it, or when a long debugging hunt ended in an aha moment worth never relearning.

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QA engineering workflows on top of Playwright. Use when a QA engineer asks to design test cases from a ticket, PRD or acceptance criteria, decide what to automate, write a bug report from a failing test or trace, convert manual test cases (TestRail, Xray, CSV) into Playwright specs with case-ID traceability, or review Playwright test code and pull requests.

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Use when reverse-engineering or detecting malware — static triage + capa/YARA-X, emulation/DBI/.NET unpacking, dynamic/fileless/Volatility 3 memory analysis, C2 config extraction (Cobalt Strike/CAPE), C2 traffic detection (JA4+, beaconing)

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Use when hunting threats or engineering detections — ATT&CK Detection-Strategies, Sigma + correlation with Detection-as-Code CI, Windows endpoint hunting (Sysmon/ETW/LSASS/LOLBins), network C2 hunting (JA4+, beaconing, DNS tunneling), cloud-identity hunting, Atomic Red Team purple-team validation

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Reviews animation and motion code against a high craft bar derived from Emil Kowalski's design engineering philosophy. Default to flagging; approval is earned.

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This skill encodes Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great. Use as a craft companion after Core's frontend, emil-design-engineering, and anim guidance.

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Python framework for single- and multi-objective optimization with evolutionary algorithms. Define vectorized objectives and constraints; solve with NSGA-II, NSGA-III, MOEA/D, GAs, or differential evolution. Analyze Pareto fronts, visualize trade-offs, customize operators and callbacks. For engineering design, hyperparameter search, and conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient), platypus, jMetalPy (Java).

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UMAP dimensionality reduction for visualization, clustering prep, and feature engineering. Fast nonlinear manifold learning preserving local and global structure. Standard UMAP (fit/transform, sklearn-compatible), supervised/semi-supervised, Parametric UMAP (NN encoder/decoder, TensorFlow), DensMAP (density), AlignedUMAP (temporal/batch). 15+ distance metrics, custom Numba metrics, precomputed distances. For linear reduction use PCA; for neighborhood graphs use sklearn NearestNeighbors.

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Review IEEE conference, journal, Transactions, Letters, or magazine manuscripts from a technical reviewer perspective using routed IEEE review gates. Use when evaluating scope fit, novelty, validity, data, clarity, compliance, advancement, engineering significance, method soundness, experiment sufficiency, baseline fairness, figure/table quality, formatting risks, rejection risk, major revision strategy, pre-submission readiness, or the quality and specificity of reviewer comments that may be generic, careless, contradictory, or possibly AI-generated.

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Polish, translate, diagnose, and restructure technical manuscript text into IEEE-style academic English using routed failure-mode guidance. Use for IEEE abstracts, introductions, related work, methods, experiments, conclusions, figure captions, rebuttals, Chinese-to-English technical writing, AI-like or template-like academic prose, and engineering prose that needs clearer problem logic, contribution framing, method rationale, evidence discipline, natural author voice, or reviewer-ready wording.

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Audit, redesign, generate, and improve IEEE manuscript figures, tables, captions, result presentation, plotting scripts, visual polish, hybrid Python/R plus vector-editor workflows, publication-ready matplotlib house style, and layout using routed visual-evidence checks. Use when checking or creating plot readability, axes, legends, line overlap, table structure, metric reporting, captions, grayscale accessibility, IEEE one-column/two-column layout, figure/table placement, claim-evidence mapping, reviewer first-impression risks, matplotlib/seaborn or ggplot2 figure code, SVG/PDF/TIFF/PNG export, Illustrator/Inkscape/PowerPoint/Figma finishing plans, or IEEE-style engineering evidence graphics.

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Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples, chain-of-thought, system prompts, prompt templates, or asks how to get better results from an LLM.

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When a founder needs demo scripts, discovery call frameworks, objection handling, RFP/RFI responses, competitive feature matrices, POC planning, or closing playbooks. Activate for sales calls, demo prep, talk tracks, bid responses, competitor comparisons, or pre-sales engineering.

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Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation.

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Use when an AI coding agent is writing or refactoring production code and its known failure modes need to be held in check — over-engineering, silent scope creep, invented APIs, claiming work is done without checking, deleting tests to make them pass. Trigger when the user asks for careful, production-grade, or reviewed code, or when a previous agent run produced confident output that turned out wrong.

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Computes DORA delivery-performance metrics from git and GitHub API. Use when assessing deployment frequency, lead time, or change failure rate.

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Pre-implementation gate covering think-first, simplicity, surgical edits, and verifiable goals. Use when starting implementation to verify the approach.

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Applies KISS, YAGNI, and SOLID principles for clean code with reduced complexity. Use when refactoring or reviewing code for over-engineering.

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