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概要と使いどころ
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
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Generate or refresh a STRIDE-based threat model for the current repository using Bug Hunter-native artifacts. Use whenever the repository has no threat model yet, the architecture changed materially, a security review needs fresh trust-boundary context, or the user explicitly asks for a threat model.
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根据 model_route.json、数据计划和清洗数据，为数学建模论文生成结果证据契约和 q1/q2/q3 建模代码脚手架。Invoke when 需要把模型输出、评价指标、结构化结论、论文表格和当前赛题专用建模代码沉淀到 paper_output/results/、paper_output/tables/ 和 paper_output/code/modeling/，供 QA 与正文生成读取。
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Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.
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Pick an LM output format per (task x consumer x model) rather than by reflex: different formats carry different cognitive load (e.g. code-in-JSON makes the same model write worse code than plain-text+diff, while asking for prose when you need a typed object fails the other way). Use when designing or debugging an LM's output schema, choosing between plain text / diff / JSON / tool-call / grammar-constrained output, or when a model's quality drops after wrapping its output in a structured format.
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Orchestrate the BZD mathematical-modeling Skills across problem reading, idea generation, model selection, paper drafting, section checks, final format/AIGC audits and judge-style scoring. Use when a user wants one entry point, an end-to-end competition workflow, the next appropriate Skill, or coordinated processing of a problem and paper.
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Use when an EntitySet exposes list_skills, a user requests a UModel or RunbookSet Skill, or an entity task supplies SKILL.md. Triggers: list_skills, list_knowledge, RunbookSet Skill, UModel Skill, execute skill, run skill, 执行技能, 运行技能, Runbook 技能.
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Run a structured threat-modeling session for a new feature, system, or architecture — STRIDE, attack trees, data flow diagrams, abuse cases. Use when the user mentions 'threat model,' 'threat modeling,' 'STRIDE,' 'attack tree,' 'abuse case,' 'data flow diagram,' 'DFD,' 'security architecture review,' 'security review,' 'design review,' 'pre-implementation security,' 'shift left,' 'what could go wrong,' or needs strategic security thinking before code is written.
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Guide for maintaining the MassGen model and backend registry. This skill should be used when adding new models, updating model information (release dates, pricing, context windows), or ensuring the registry stays current with provider releases. Covers both the capabilities registry and the pricing/token manager.
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Choose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via claudish, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.
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Automated pipeline for retraining ML models with new construction data. Monitor model drift, trigger retraining, and validate model performance.
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Audits business spreadsheet models (.xlsx, .xlsm, or Google Sheets exported to .xlsx) for structural integrity errors and explains each fix -- budgets, forecasts, pricing models, commission calculators, FP&A packs, ops trackers, not only banking models. A bundled openpyxl script finds hardcoded numbers inside formulas, typed values pasted over formulas, inconsistent formulas across a row or column,
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Builds a custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the model.
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Main app ViewModel patterns (ScopedViewModel, Hilt, StateFlow, triggerEvent, navArgs, savedState flows, events). Use when writing, editing, exploring, debugging, or reviewing ViewModels in the store management app. Covers navArgs, combine+asLiveData, sealed state hierarchies, custom events, init blocks, and companion object placement. NOT for POS (WooPos*) code — use the `pos` skill instead.
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Guides privacy program maturity assessment using the AICPA/CIPT Privacy Maturity Model with five levels: Ad Hoc, Repeating, Defined, Managed, and Optimized. Covers assessment methodology across ten privacy domains, scoring criteria, gap analysis, maturity roadmap generation, and benchmarking against industry peers. Keywords: privacy maturity, AICPA, maturity model, assessment, roadmap, benchmarking.
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Conduct LINDDUN privacy threat modeling across all seven categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat trees, privacy-specific mitigation strategies, and integration with STRIDE security threat modeling.
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Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.
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Add any Hugging Face image or video model, checkpoint or LoRA to Guaardvark from a URL, list what is installed, and download registry models on request. Use when the user pastes a huggingface.co link or a .safetensors URL, asks "can it run <model>", or wants a new LoRA or checkpoint available in the Studio.
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Automatically repair code violations of temporal properties using model-checking counterexamples as guidance. Use when working with formal verification results, temporal logic property violations (LTL, CTL), model checking counterexamples, or when asked to repair property violations, fix counterexamples, repair temporal properties, fix model checking violations, or repair code based on counterexamples. Applicable to concurrent systems, state machines, synchronization issues, safety/liveness properties, and resource management violations.
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Extract abstract mathematical models from imperative code (C, C++, Python, Java, etc.) suitable for formal reasoning in Coq. Use when the user asks to model imperative code in Coq, create Coq specifications from imperative programs, extract mathematical models for verification, or translate imperative algorithms to Coq for formal reasoning and proof.
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Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.
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Use when choosing between engines or models for an agent pipeline and the answer must come from measurement on your own data, not from marketing pages: speech recognition for meeting recordings, the model that turns a transcript into notes, the critic model that checks it. Runs every variant in an isolated container, measures time, cost per hour of input (USD and RUB, with price source and date) and quality (WER/CER and course terms for ASR; code checks plus a judge of a different model for LLM steps), writes results.json/csv and one static analytics page. Triggers on "бенчмарк", "сравни движки", "сколько стоит час записи", "какую модель взять для пайплайна", "benchmark the pipeline", "compare ASR engines".
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3-statement financial model, integrated IS BS CF, income statement projection, balance sheet forecast, cash flow statement, cross-statement balancing, financial model build, operating model, three statement model, integrated financial statements
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Think and work like an expert Laser Physicist. Use when a task calls for Laser Physicist judgment. Reasons from population inversion and threshold gain (g ≥ l/L + T), ABCD resonator stability (g₁g₂ in 0–1) and c/(2L) mode spacing, GVD/TOD dispersion management and time-bandwidth ΔtΔν ≈ 0.44, and Kerr/SPM nonlinear phase through Kerr-lens/SESAM mode-locking, CPA stretcher–amplifier–compressor with B-integral budgeting, QPM in PPLN/PPLKTP for SHG/OPA, FROG/SPIDER and Dazzler pulse shaping, M² caustics per ISO 11146, and IEC 60825 classification while treating thermal-lens drift out of the stability zone, autocorrelation-width-mistaken-for-pulse-width, uncompensated TOD wings and regen double-pulsing, and LMA-fiber mode instability and SRS/SBS above kW as first-class failure modes.
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。