「map」の検索結果

当用户想判断一个问题是否能用监督学习解决,或试图把业务问题转化为AI问题时调用。 用户在问"AI能不能做X"、"这算不算机器学习问题"、"我们的业务场景适合用AI吗"时使用。 不适用于: 纯数据分析洞察、无明确输入输出的探索性问题、聚类等非监督学习场景。

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。

Incrementally update Mintlify documentation (.mdx) from Python source changes only. Use when: (1) Python files referenced in doc_code_map frontmatter have changed, (2) a PR touches Python modules documented in docs/mintlify/key_modules/ or docs/mintlify/mcp/, (3) the user asks to sync docs after Python code changes. Prefer minimal diffs and leave correct content untouched.

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。

Use when designing bytecode dispatch loops, choosing stack or register VM shapes, adding inline caches, or building a first JIT with mmap. Not for a language toolchain: use compiler-frontend.

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。

Use when creating FreeRTOS tasks, queues, semaphores, or mutexes, catching stack overflow, writing FreeRTOSConfig.h, or debugging tasks over OpenOCD and GDB. Not for probe setup: use openocd-jtag.

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。

Use when reducing header bloat and compilation cascades with Include What You Use, interpreting IWYU reports, mapping files, forward declarations, or CMake integration.

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。

Use when debugging with LLDB on macOS, FreeBSD, or Linux-clang, mapping GDB commands to LLDB, Xcode or VS Code integration, LLDB Python scripting, or debugging Swift and Objective-C.

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。

Use when writing eBPF programs with libbpf, bpftrace, or Rust with aya-ebpf and aya-log, attaching kprobes, tracepoints, or XDP hooks, declaring maps, sharing data with a Tokio loader, triaging verifier or Aya load errors, or porting with CO-RE. Not for full kernel bypass: use dpdk.

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。

Use when writing or debugging Linux drivers: platform, I2C/SPI, device-tree, char-device, IRQ, DMA, regmap, PM, sysfs, udev, NACK, -EREMOTEIO, or probe failures.

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。

Use when the user asks for onboarding, orientation, a repository tour, or where to start. Produces an evidence-backed seven-section map, deep-reads real files, and asks for the next move.

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。

Use when building static archives with ar, stripping or converting binaries, mapping crash addresses with addr2line, or demangling C++ symbols. Not for ELF analysis: use elf-inspection.

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。

Use when loading core files in GDB or LLDB, enabling core dump generation via ulimit or coredumpctl, mapping symbols with debuginfod, or extracting backtraces from production segfaults.

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。

Use when the user points to a study-material folder and asks to make it teachable. Not for remote, credential, publish, deploy, or irreversible changes.

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。

Use when the user asks about recent engineering work, what the team is working on, planning or roadmap material, or an explicitly requested Slack summary. Not for remote or irreversible changes.

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。

Use when asked to explore the codebase to map structure, symbols, and dependencies. Not for a single file under 50 LOC: read it directly.

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。

Use when the user asks to set up the agent environment. Covers credentials, placeholders, tool access checks, a uv venv, a pack install, or a role-model mapping file. Not for unattended runs.

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。

Use when mapped Compass requirement evidence needs synchronization into component living specs.

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。

Load the essential AI Factory project context before work begins. Reads configured DESCRIPTION, ARCHITECTURE, ROADMAP, RESEARCH, RULES, and applicable AGENTS.md files, then returns a compact session handoff suitable for continuing or forking. Use at the start of a new session or after context loss.

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。

Design or implement the smallest complete executable evidence for a governed Proto UI behavior. Use for conformance cases, regression tests, cross-layer verification, test-entity mappings, failure-path coverage, or validation-plan design. Do not use tests to decide unsettled semantics.

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。

Sync mapped Compass requirement evidence into component living specs.

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。

Analyze a job posting and map its requirements to the master resume as strong matches, partial matches, or honest gaps. Use when the user provides a job description or URL and wants a fit analysis before tailoring.

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。

Diagnose React + Three.js (or any browser canvas) frame stutter by capturing a CDP CPU profile via Playwright instead of guessing from `[perf]` console logs. Use when: (1) frame time is bad but `[perf]` instrumentation doesn't pinpoint the offender, (2) you've spent more than ~30 minutes "fixing" suspects without measuring, (3) hot paths involve animation systems, allocation bursts, or invisible scene-graph traversal, (4) you need self-time per function with line numbers, not aggregate "render slow". The `console.warn` pattern is good for monitoring; CDP is needed for diagnosis. Covers script template, sourcemap setup so minified function names are decoded, and interactive variant where the user drives the interaction and signals stop via `touch /tmp/<flag>`.

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。

Read project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.

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。

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.

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。

Stress-test a design decision with premortem/backcast analysis, then produce a risk-calibrated recommendation that maps to ADR fields. Use when: (1) Multiple competing architecture options, (2) Irreversible or high-risk design choices, (3) /discuss identifies a complex trade-off

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。