「rag」の検索結果

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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Analyze recent changes and add test coverage for HEAD commit

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Use whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots, agents), computer vision, NLP, recommendation systems, data engineering, BI/analytics. Triggers on "find an AI/ML consulting firm to build our recommendation engine", "shortlist three RAG/LLM consultancies for an enterprise chatbot", "compare three AI/ML consulting firms with strong ratings", or "pull contact info for these 8 AI consultancy domains", even when described indirectly (we want to use AI for X, deploy ML to production). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for general app/backend work where AI is just a feature. Skip in-house ML/data hires, LLM/AI-tool comparisons (ChatGPT vs Claude), "how do I fine-tune X" DIY questions, AI courses for individuals, non-US firms, individual freelancers.

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Require corresponding tests for every Go code change, E2E coverage whenever developing tests, and immediate diagnostic remediation while agents edit code; select, run, and maintain AuthCrunch tests through pinned tested, browser tests, automation fixtures, coverage artifacts, and CI gates.

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Write tests for untested OSS code as a standalone contribution. Identifies coverage gaps, studies the repo's test conventions, and guides the user through writing tests that match existing patterns. Use when contributing tests to an open source repo, looking for a low-risk first contribution, or improving test coverage. Not for writing tests alongside a bug fix — use oss-contribute for that.

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Use when 高教社杯/CUMCM mathematical modeling work needs lightweight reusable paper assets, national-prize style paragraph templates, section controls, per-question code/plot/test scaffolds, evidence registries, paper fragments, reproduction scripts, or safe reuse of prior contest project structure without copying old problem-specific content.

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模型评估全流程。覆盖分类/回归/排序/生成/RAG/LLM 评估、A/B 测试、 在线监控、数据漂移检测、评估集构建、基准测试。 触发关键词：评估、指标、Precision、Recall、F1、AUC、BLEU、ROUGE、 A/B 测试、基准测试、数据漂移、模型监控、RAGAS、LLM-as-Judge。

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Use MemoryBench to run repeatable conversational memory and RAG benchmarks across providers, datasets, judge models, checkpoints, and structured reports.

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Deploys custom Apify Actors via the Apify API v2 for large-scale web crawling using CrawleeJS. Leverages Apify dataset storage, RequestQueue, and proxy configuration for distributed scraping at scale.

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Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

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AIChat is a comprehensive LLM command-line tool written in Rust that combines chat-REPL, shell command generation, RAG, AI tools, and multi-provider support into a single binary. It connects to 20+ LLM providers including OpenAI, Claude, Gemini, and Ollama.

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Expert PR and media relations guidance for earned media, press coverage, and reputation building. Use when writing press releases, crafting media pitches, developing journalist relationships, planning embargo strategies, managing crisis communication, placing thought leadership, building analyst relations, submitting award applications, or measuring PR coverage. Use for media outreach, press kits, speaking opportunities, and earned media strategy.

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Multi-backend database security auditor. Audits Supabase, Firebase (Firestore/RTDB/Storage/Functions/Remote Config), MongoDB (self-hosted + Atlas), self-hosted PostgreSQL, and self-hosted MySQL for RLS/rules misconfigurations, exposed credentials, auth bypasses, MongoBleed (CVE-2025-14847), pgBouncer CVE-2025-12819, mysql_native_password drift, ghost auth, and storage exposures. Use this skill whenever the user mentions database security, RLS or rule audits, security review, penetration testing a vibe-coded app, checking if their DB is exposed, hardening a backend, fixing security rules, or auditing apps built with Lovable/Bolt/Replit/Cursor/Claude Code on any of these backends. Trigger on phrases like 'is my app secure', 'check my database', 'audit my Firebase', 'audit my MongoDB', 'audit my Postgres', 'audit my Supabase', 'is my DB exposed', or any cross-backend variant ('audit my full stack').

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调用 Verilator 5.012 编译 RTL + TB 并跑 coverage-driven simulation，产出 sim log / coverage.dat / VCD。触发场景：(1) bba-guru-verification 完成 TB 后跑回归；(2) RTL 修复后回归验证；(3) 显式 /bb-invoke-verilator。

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Creates or completes a medium-coverage test suite: unit, component, and critical e2e flows.

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Analyze test coverage and identify gaps with actionable recommendations

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Assesses energy transition investments with battery storage, grid modernization, EV charging, and hydrogen infrastructure analysis. Use when modeling energy transition assets, evaluating storage economics, or analyzing grid infrastructure.

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Analyzes rights offering dynamics with theoretical ex-rights price, subscription premium, and nil-paid value calculation. Use when evaluating rights offerings, modeling TERP, or analyzing subscription arbitrage.

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Constructs risk parity allocation with equal risk contribution, leverage optimization, and asset class volatility targeting. Use when building risk parity, equalizing risk contribution, or designing leveraged balanced portfolios.

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Evaluates secondary-focused credit facilities with leverage terms, borrowing base mechanics, and portfolio pledging requirements. Use when analyzing secondary lending, structuring portfolio leverage, or evaluating fund finance options.

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Monitors leveraged loan market conditions with new issue activity, technical dynamics, and CLO demand analysis. Use when analyzing loan markets, tracking CLO activity, or assessing market technical conditions.

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Evaluates optimal leverage with WACC minimization, rating impact, and financial flexibility assessment across market conditions. Use when optimizing capital structure, analyzing target leverage, or evaluating rating implications.

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Verifies implementation plan quality across 8 dimensions (requirement-coverage, task-completeness, dependency-validity, scope-sanity, artifact-wiring, risk-assessment, testability, estimation-quality) and returns pass/fail with per-dimension scores.

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AI-first security scanning with Medusa. 3,000+ detection patterns covering AI/ML, agents, MCP, RAG, prompt injection, and traditional SAST vulnerabilities. Wraps Medusa CLI with SARIF/JSON parsing, structured finding output, OWASP mapping, and remediation guidance.

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。