'edge' 검색 결과

Expert knowledge for Azure IoT Edge development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring EFLOW devices, DPS/X.509 provisioning, gateway/nested topologies, CI/CD deployments, or IoT Edge metrics/logs, and other Azure IoT Edge related development tasks. Not for Azure IoT Hub (use azure-iot-hub), Azure IoT (use azure-iot), Azure IoT Operations (use azure-iot-operations), Azure Stack Edge (use azure-stack-edge).

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Use on any core for first-run planning intake — Phase 0 runs the vendored BMAD-METHOD planning shelf, shared by every core, and Phase 1 (mining `04-prd.md` into intent records plus the Add-ons/sync-and-remote-entities decision) is full-stack-app's and pwa-app's shared procedure — identical mechanics, a different decision at step 5/8. Phase 0 also defines compressed intake, the path a user's explicit "just build it" choice takes on full-stack-app, pwa-app, authored, and copywriting cores: one batched round of questions in place of the shelf, writing a thinned archive at the same paths so Phase 1 (or, on copywriting, that core's own mining step), `ux-planner`, and the Re-entry pass all keep their documented source. Also use for the Re-entry pass, which mines new scope into additional intents without re-running the shelf, on any core with a module axis to add an intent to (full-stack-app, pwa-app, authored) — landing-page has none, so its own new-scope path runs through `hedgehog-landing-loop`'s Correction Protocol instead. Invoked by the `planner` agent, which decides the path; don't run standalone. landing-page runs this skill's Phase 0 on first run, then mines the same archive through `hedgehog-landing-loop`'s own planning-intake section, that core's counterpart to this skill's Phase 1. copywriting runs this skill's Phase 0 the same way (full or compressed), then mines the same archive through `hedgehog-copywriting-loop`'s own planning-intake section into a what/audience/register brief — no module axis, so its own new-scope path runs through that loop's Correction Protocol rather than the Re-entry pass below. An authored core runs this skill's Phase 0, then `hedgehog-core-design`, then this skill's Phase 1 mining against the designed layer sequence. A brownfield adoption (`hedgehog-adopt`) never runs this skill's shelf at all — the drivers BMAD elicits are already settled facts of a repo that already exists.

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"Apply the Knowledge-Based View (Grant, 1996) and Nonaka and Takeuchi's SECI model to analyze how organizations create, transfer, and integrate knowledge for competitive advantage. Use this skill when the user needs to design knowledge management systems, understand why knowledge transfer fails across teams, evaluate knowledge creation processes, or when they ask 'how do we capture tacit knowledge', 'why does knowledge stay siloed', or 'how can we turn individual expertise into organizational capability'.".

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Build focused knowledge bases from claude-mem observations, filtered by topic, project, date, or file. Load selected records into an AI conversation to ask contextual follow-up questions about past decisions, fixes, and work patterns.

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• Compiling knowledge about a specific technical topic
• Reviewing decisions made during a chosen period
• Asking follow-up questions about a service's past bug fixes

Turn a structured LLM wiki into an interactive knowledge graph. It maps articles, topics, and sources, then adds entities and inferred relationships to help readers explore connections beyond the wiki’s explicit links.

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• Explore connections across wiki articles
• Find relationships beyond explicit links
• View articles and sources by topic

Synthesize OpenViking knowledge bases or document collections into topic-based Markdown pages that explain trends, changes, comparisons, and implications. Keep conclusions traceable to source evidence, distinguish facts from inferences, and preserve disagreements and uncertainty.

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• Comparing changes across financial reports
• Finding shared trends across knowledge bases
• Organizing sourced conclusions by topic

Turn documents, notes, research, or code into a knowledge graph that maps identifiable things and their relationships. Create or update entity files and typed connections, with source evidence attached for inspection and later visualization.

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• Map people and organizations across documents
• Organize research with traceable evidence
• Map relationships between software modules

Analyze Chrome, Edge, and other Chromium browser profiles with Hindsight to reconstruct web activity. Combine browsing history, downloads, cookies, and other stored records into a chronological timeline, with XLSX, JSON, or SQLite output for investigation.

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• Reconstructing browsing activity during incident response
• Checking download times and source URLs
• Comparing cookies and history in insider threat investigations

Search shared organizational knowledge to answer questions about people, projects, decisions, and history. Save lasting conclusions as pages, update existing knowledge, and link related topics within the areas your account is allowed to access.

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• Find the reasons behind a past decision
• Look up knowledge about people and projects
• Save conclusions for use beyond the conversation

Find and synthesize official Google developer documentation for platforms including Google Cloud, Gemini, Android, and Firebase. Retrieve command syntax, API details, permissions, and product comparisons through Developer Knowledge, then turn the findings into practical technical answers.

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• Checking gcloud commands and flags
• Looking up API syntax and required permissions
• Comparing Google products and architectures

Turn a large codebase spanning multiple repositories or services into a structured knowledge base. It documents architecture, business rules, and dependencies, links conclusions to source lines, labels confidence in relationships, and supports incremental updates.

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• Documenting architecture across multiple repositories
• Tracing service dependencies and business rules
• Updating a knowledge base after code changes

Authoritative agent-runtime contract for working inside an OpenKnowledge project — a markdown-CRDT knowledge base exposed over MCP. Use whenever reading, listing, searching, editing, or linting any `.md` or `.mdx` file in the project, and before any `mcp__open-knowledge*` tool call (`exec`, `search`, `write`, `edit`, `lint`, and the rest). Installed by `ok init`, so its presence means this is an OpenKnowledge project and it governs every markdown file here. Covers the read/write tool surface, grounding and linking rules, folder/template conventions, the live browser preview, and the rule that OK's MCP tools — never native file tools — handle in-scope markdown.

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How to work in a Knowledge Base project (the `knowledge-base` starter pack). Read when the project has the three-layer source-grounded layout — `external-sources/` → `research/` → `articles/` — or when asked how this project is organized. Carries the layer model, per-folder rules, status flows, and log discipline so this guidance does NOT live inside template bodies or log.md. The three procedures live elsewhere: ingest in the platform `open-knowledge` skill, research and consolidate as their own sibling skills in this pack. Complements the platform `open-knowledge` skill; does not replace it.

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Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity, complexity, and risk management. No API key or registration required. Use when working with hedge fund data, systemic risk monitoring, financial stability research, hedge fund leverage or leverage ratios, counterparty concentration, Form PF statistics, repo market data, or OFR financial research data.

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Migrate a Jetpack Compose app to edge-to-edge display and fix system bar inset issues. Use when UI components are obscured by navigation/status bars, fixing IME insets, or enabling edge-to-edge for SDK 35+.

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Use when the user wants to contribute a fix or roadmap item back to the Hedgehog project itself (skyf0xx/hedgehog) rather than their own project. Triggers on "let's fix that in Hedgehog", "I want to contribute", "let's pick up a roadmap item", or when `tweaker` offers this at the end of a build and the user says yes. Covers forking/branching, making the change under Hedgehog's own repo rules, committing with Conventional Commits, and opening the PR.

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Use when the user asks to write, draft, or edit a standalone piece of prose — an article, essay, blog post, tweet, thread, newsletter, announcement, email, pitch, or other copy — and the request does not already name "Hedgehog". Length is not the test: a tweet qualifies the same as an essay. Does not apply to a question about existing code, a bug, a one-off script, or a request that already names Hedgehog (that goes to the `hedgehog` skill instead) or a project that already has `.hedgehog/` (its own installed skills own that session).

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Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.

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RFDB server becomes unresponsive (60s+ timeouts) during enricher addEdges write storms. Use when: (1) RFDB CPU spikes to 60-90% after addEdges calls; (2) subsequent reads time out at 60s; (3) server stays catatonic for 5-15 min after a write storm; (4) Tokio main thread parked (write-lock contention). Root cause: add_edges() calls maybe_auto_flush() which has a memory-pressure path that fires on NODE count — not edge count — and holds the exclusive write lock during disk I/O. Fix: separate maybe_auto_flush_edges() uses byte-limit only.

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Edge Computing Specialist IA — Expert en edge computing (Cloudflare Workers, Deno Deploy, Vercel Edge, Bun)

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Use when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For content-level AI optimization, see geo-content-optimizer.

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Turn documents into a growing Markdown wiki and a knowledge graph, a map of entities and their relationships. Preserve source text, update linked pages as new material arrives, and answer questions with supporting evidence.

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• Build a reusable knowledge base from documents
• Trace relationships between terms and topics
• Answer questions with source evidence

Plan and implement connections between Arduino devices and Azure IoT Hub, directly or through an IoT Edge gateway. Define sensor messages, device credentials, offline buffering, remote commands, and implementation tasks for firmware, gateways, and cloud services.

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• Connect Arduino sensors to Azure
• Design a local gateway for field devices
• Buffer sensor data during network outages

Train smaller language models using knowledge distillation, where a larger model supplies examples or prediction targets. This skill covers training losses, generated training data, parameter choices, and output comparisons to support model compression and specialized student models.

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• Training a smaller model from a larger teacher
• Fine-tuning with teacher-generated responses
• Distilling domain knowledge into a specialized model