「udi」の検索結果

Architects the encryption, codec/transport, and premium-feature layer of a group video-calling product, on top of an already-chosen SFU topology. Covers E2EE patterns (Zoom leader-fan-out, Signal pairwise-fan-out, MLS/TreeKEM as in Discord's DAVE, Telegram's ledger consensus), the SFrame (RFC 9605) + WebRTC Insertable Streams mechanism letting an SFU relay encrypted frames it can't read, codec/transport choice (AV1/VP9 SVC vs. simulcast, Opus, congestion control, Media-over-QUIC, WHIP/WHEP), and which "premium" features (virtual backgrounds, live captions, recording, breakout rooms, spatial audio) survive E2EE vs. structurally can't. Use when adding E2EE to an SFU-based call, choosing a 2026 codec/SVC strategy, or deciding which features to disable under E2EE mode. NOT for call topology/platform choice (see webrtc-adhoc-video-chat, which this composes with). NOT for one-way broadcast (see managed-video-streaming-pipeline). NOT for the DM/group-chat text backend (see realtime-messaging-backend-architecture).

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Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).

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Designs and audits data retention and file compaction for an embedded SQLite datastore in a long-lived service — a unified retention registry (one declared policy per table), TTL / absolute-age / row-count-cap policies, a fail-loud coverage guard, WAL/checkpoint tuning, and auto_vacuum/incremental-vacuum so pruning actually shrinks the file. Use when a SQLite DB grows unbounded, a table has an index but no DELETE, a pruned DB never shrinks on disk, metrics are stored as raw events forever, or a new table needs bounding. Keywords retention policy, TTL, reaper, prune, VACUUM, auto_vacuum, incremental_vacuum, WAL checkpoint, freelist, DB bloat, row cap, per-tenant quota. NOT for Postgres/MySQL/server-DB partitioning or TTL, log-line rate-limiting/sampling, backup/replication, schema migrations, or query-performance indexing.

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Guides the practical path from 'we have no design system' or 'we have a mess' to a real, functional design system. Component inventory, design token extraction, headless UI adoption (Radix, React Aria, Headless UI), migration order of operations, and the assessment-to-rollout pipeline. Activate on 'design system from scratch', 'bootstrap design system', 'no design system', 'design system migration', 'headless UI adoption', 'component audit', 'design token extraction', 'design system strategy'. NOT for building specific components once a system exists (use design-system-creator), not for generating token files/configs (use design-system-generator).

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Cloudflare Pages and Workers deployment checklist for wrangler deploys, secret audits, D1 migrations, Worker-compatible SDK checks, and browser-auth verification. Use when shipping Cloudflare changes to production. NOT for general CI/CD architecture, non-Cloudflare deployments, or post-launch UX QA.

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Analyze design trends and recommend appropriate styles, color palettes, and typography systems for your project based on current design movements. NOT for accessibility auditing or full design system creation.

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Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).

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Designs and audits native-unit capacity reservation, serial admission, ambiguity holds, stop reserves, effect closure, and control-disjoint settlement handoff for consequential agent attempts. Use when scarce capacity must be conserved across retry, rework, resurrection, or compensation. NOT for scheduling work, choosing models, pricing unlike resources into one scalar, authorizing effects, grading work, paying workers, or asserting runtime containment.

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Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit. Use for confidence calibration, agent routing, expertise audits, and escalation design. NOT for deterministic implementation tasks, pure syntax debugging, or domains with explicit verifiable answers.

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Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit. Use for confidence calibration, agent routing, expertise audits, and escalation design. NOT for deterministic implementation tasks, pure syntax debugging, or domains with explicit verifiable answers.

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Use when designing or fixing a Content Security Policy on a real site, choosing between nonce-based and hash-based CSP, adding strict-dynamic, debugging "Refused to execute inline script" errors, deploying CSP in report-only mode first, configuring report-to / report-uri, or auditing an existing policy for unsafe-inline / unsafe-eval / wildcards. Triggers: "CSP blocks legitimate inline script", strict-dynamic, nonce-{RANDOM}, sha256-{HASH}, object-src none, base-uri none, frame-ancestors, Trusted Types, X-Content-Security-Policy obsolete, report-only vs enforced. NOT for general HTTP security headers (HSTS, COOP/COEP), Trusted Types deep dive, CORS configuration, or building a WAF.

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Own whether a multi-document product-architecture binder is internally consistent, complete against its stated customer/contingency/architecture coverage, and honest about which older product ambitions it has absorbed, superseded, deferred, contradicted, orphaned, or rejected. Use when running a binder Architect of Record pass, before an implementation chain cites a binder chapter as ready, when a proof-gate owner is missing, or when reconciling the binder against the older ambition corpus (website, plans, examples, ADRs). NOT for auditing a single Claude Skill bundle's structure or frontmatter (use skill-hygiene), rendering a single-PM accept/reject verdict on one finished deliverable (use port-daddy-user-surrogate-pm-review), or sequencing/stewarding a roadmap's sidequests once a gap or operator decision has been surfaced (use legible-roadmap-with-sidequests).

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Generates OpenAPI 3.0/3.1 specs, TypeScript client types, and curl examples from existing route handler code. Supports Express, Next.js API routes, Fastify, and Hono. Produces complete documentation including auth requirements, rate limits, error codes, and request/response examples. Activate on: 'generate API docs', 'OpenAPI from code', 'document endpoints', 'API reference', 'swagger generation', 'endpoint documentation'. NOT for: API design from scratch (use api-architect), deployment (use devops-automator), testing (use test-automation-expert).

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Plan or audit the cross-layer Harbor cooperative editor: Loro replicas for humans and agents, governed edit claims, salvage authority, and transport across shared, LAN, and remote harbors. Use when a change spans editor collaboration, identity, recovery, or transport contracts. For a single GPUI pane use gpui-rust-console; for motion use rust-gpui-motion; for a shader use gpui-shaders. NOT for non-collaborative screens, web editors, or generic Rust development.

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Design and audit a pricing function for variable-cost agent labor. Choose among per-seat, metered, credits, hybrid, and outcome pricing; name buyer value and cost metrics separately; calculate a reproducible cost floor; require pre-commitment guardrails; and stress-test declared personas. Use for offline pricing-design evidence, not billing implementation or runtime control. NOT for production billing, payment collection, or runtime spend enforcement.

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Declare an agentic application's interaction disclosure, state, context, capabilities, and effect-control boundaries. Use for a reviewable design specification and static consistency audit before implementation. NOT for proving that controls are deployed or enforced, exposing private chain-of-thought, selecting a model/router, or designing a multi-agent protocol.

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How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).

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Expert in script-to-video production pipelines for Apple Silicon Macs. Specializes in hybrid local/cloud workflows, LoRA training for character consistency, motion graphics generation, and artist commissioning. Activate on 'AI video production', 'script to video', 'video generation pipeline', 'character consistency', 'LoRA training', 'cloud GPU', 'motion graphics', 'Wan I2V', 'InVideo alternative'. NOT for real-time video editing, video compositing (use DaVinci/Premiere), audio production, or 3D modeling (use Blender/Maya).

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Research AI coding assistant products, audiences, user stories, social proof, hacks, and unmet needs for apps like Cursor, Claude Code, Codex, Warp Code, Devin, Windsurf, GitHub Copilot, Cline, Aider, and homegrown agent stacks. Use when designing or positioning agentic developer tools, Port Daddy Agent Harbor, swarm consoles, background coding agents, or competitive/product research for AI software engineering. NOT for implementing the coding agent runtime, generic market research, or UX mockups without developer workflow evidence.

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Verify every visual-evidence artifact (screenshot/GIF/recording) attached to a PR carries a provenance manifest binding it to real daemon-backed truth — daemon port, run id, transcript head hash, agent node id, commit, and an honest real/fixture/mock source label — and that operator-control-panel PRs cover the required set of proof states. Use when gating a PR's proof artifacts before merge, auditing whether a "LIVE" visual claim is backed by a real daemon run versus a reused or fixture-backed mock, or defining the required state-coverage set for a control-panel change. NOT for the capture technique itself — headless Playwright, `screencapture -x -l`, non-interruptive capture (use port-daddy-agent-skill's visual-evidence doctrine), designing the receipt body schema an artifact attaches to (use agent-work-receipt-designer), or the broader dogfood/Potemkin product-quality bar (use multi-agent-authoring-product-bar).

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Design a normalized, machine-readable "work receipt" that answers what changed, why, by whom, and with what validation, for any AI-coding-agent task regardless of backend (Claude Code, Codex, Cursor, Aider, CI, or homegrown). Use when defining a receipt schema, normalizing heterogeneous agent logs into one evidence shape, gating PR merges on artifact-backed proof, or designing an OSS maintainer's fast-reject/fast-merge workflow. NOT for validating a DAG node's output against its own schema (use output-contract-enforcer), continuous runtime monitoring of a live coordination daemon (use runtime-verification-for-agents), or general Port Daddy claims/notes/salvage mechanics (use port-daddy-agent-skill).

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Designs and audits deterministic, authority-filtered partitions of trust-typed context for already admitted bodies or abstract continuation slots. Use when causal context, obligations, disclosure boundaries, vector-space identity, capacity, and omission proofs must survive partitioning. NOT for spawning or admitting agents, choosing worker count, retrieving arbitrary knowledge, writing successor prompts, or granting tools, leases, identity, or effect authority.

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Use this skill when building applications with Gemini models, Gemini API, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript, com.google.genai:google-genai for Java, google.golang.org/genai for Go), model selection, and API capabilities.

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Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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。