「watch」の検索結果

Watch a brand's own product footage (screen recording, product film), pick the moment that proves each line with the user, and render those picks as a product layer — free, local. survey.py turns footage into timestamped contact sheets + scene cuts + motion so the agent can actually look at it; the agent writes a cut list (beat → source window + framing); preview.py draws it as a review sheet the user corrects round by round; cut.py renders the approved list into a silent 1080x1920 layer with the creator's area left as a plate. Also frames footage as a filmed screen (keystone, bezel, handheld drift) with dissolves between beats, and takes still screenshots as sources. Use for any format that shows real product footage beside, between or instead of a creator (split-screen, screen inserts, walkthroughs).

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Find out why the tests pass but the app is broken. Catches false greens: a green suite over a feature that does not work, a mocked API standing in for a real one, an assertion that holds no matter what the app does, a click handler wired to nothing. Use when the suite is green and the user says it is broken, when a test never fails, when coverage looks fine but bugs still ship, or before trusting a passing run you did not watch.

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Use when working with HealthKit, WorkoutKit, health data, workouts, or fitness features on iOS or watchOS. Covers permissions, queries, background delivery, custom workouts, multidevice coordination.

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Access Core Motion accelerometer, gyroscope, magnetometer, device-motion, pedometer, activity-recognition, altitude, headphone motion, batched high-frequency workout motion, and water-submersion/depth data. Use when reading device sensors, counting steps, detecting walking/running/driving/cycling, tracking altitude, building motion interactions, handling AirPods head tracking, or implementing watchOS dive/depth features.

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Implement AlarmKit alarms and countdown timers for iOS and iPadOS with Lock Screen, Dynamic Island, StandBy, and paired Apple Watch system UI. Covers AlarmManager scheduling, AlarmAttributes and AlarmPresentation, system Stop and AlarmButton secondary actions, authorization, state observation, countdown widget-extension handoff, and Live Activity integration. Use when building wake-up alarms, countdown timers, or alarm-style alerts that need Apple's system alarm experience.

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Implements keyboard, directional, and scene-level focus behavior across SwiftUI and UIKit. Use when managing @FocusState, defaultFocus, focused values, focusable interactions, focus sections, tvOS geometric focus and Siri Remote navigation, watchOS Digital Crown input, visionOS connected-device focus versus gaze hover/input targets, macOS key view loop and Full Keyboard Access, focus restoration after presentation changes, custom focus routing with UIFocusGuide, or debugging focus with UIFocusDebugger.

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Access security tokens and smart cards using CryptoTokenKit. Use when building TKTokenDriver or TKSmartCardTokenDriver extensions, communicating with smart cards via TKSmartCard/TKSmartCardSlotManager, using iOS 26+ NFC smart-card sessions, registering smart cards, querying token-backed keychain items with kSecAttrTokenID, monitoring TKTokenWatcher, or configuring certificate-based smart-card authentication.

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AWS EC2 virtual machine management — instances, security groups, key pairs, AMIs, EBS volumes, Auto Scaling Groups, Spot Instances, Session Manager, placement groups, and instance lifecycle automation. Trigger on ANY of these, even when EC2 isn't named explicitly: - Launching or provisioning: "spin up a server", "create a VM", "new instance", "run-instances", mention of instance types (t3, m5, c5, r6, g5, p4d, t4g, c7g, etc.) - SSH / connectivity problems: "connection refused", "connection timed out", "permission denied publickey", "can't connect to my instance", "SSH not working" - Instance management: resize, stop, start, terminate, reboot, change instance type - Cost optimization: stop dev instances overnight, save money on EC2, spot vs on-demand, reserved instances - Auto Scaling: ASG, launch template, mixed instances policy, scale to zero, scheduled scaling - Spot Instances: spot fleet, spot interruption, capacity-optimized, price-capacity-optimized - AMIs and backups: create image, custom AMI, EBS snapshot, DLM lifecycle policy, copy AMI - Monitoring: EC2 CPU utilization, CloudWatch metrics for instance, instance status checks, console output - Access methods: Session Manager, EC2 Instance Connect, bastion host, port forwarding - Security: IMDSv2, instance metadata, IAM role on instance, security group rules - User data and bootstrap scripts, cloud-init

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Use when a task is already judged hard — pick which paid frontier seat takes it, then keep Jev watching the delegated run so it interrupts you only when the run needs a decision.

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Detect quality and efficiency regressions over time using Agent Monitor data — rising error rate (APIError events), falling cache hit rate, growing compaction frequency, and climbing cost-per-session. Splits history into an earlier baseline window and a recent window and reports which metrics are getting worse, by how much, and where. Use when checking whether things are degrading or trending in the wrong direction.

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Use when the user has finished building a mobile app, started Metro with `npm run dev`, and wants the running app monitored for runtime errors AND silent failures (empty lists, blank screens, swallowed network errors) and fixed autonomously. Accepts a free-text symptom (e.g., `/debug-app "todos not appearing on home screen"`) to drive persisted-log diagnostics — injects temporary console.log statements at data-path boundaries, reads the sanitized `.powernative/metro-logs/` log, and cleans up logs after the root cause is fixed. Supports port/platform filtering, configurable clean-cycle and timeout limits, and watch-only `--no-fix` mode. Otherwise polls with a durable byte cursor, fixes inline or routes appropriately, verifies each fix from new output, and exits after the configured clean checks (default 3). Foreground loop — blocks the conversation while running. Run only after the app is loaded.

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Use when sculpting a stylized or cartoon character, bust, collectible or toy in ZBrush (Overwatch, Fortnite, Disney look), judging shape language (S and C curves, parallel lines, 70/30, silhouette read at a distance), exaggerating proportions on purpose, keeping stylized planes crisp through subdivision, sculpting stylized hair (chunky clumps, curve tube strands, ClayPolish), or designing a figure for toy production (articulation, wall thickness, scale).

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Drive one AI agent from another through kolu's terminals: spawn a Claude Code / Codex / opencode session in a PTY, prompt it, watch the screen for its reply, read it, and prompt again — a create→send→snapshot loop run with the `kaval-tui` CLI directly (no MCP). `kaval-tui` writes input and reads scrollback; `pulam-tui` adds a precise agent-state done-signal when you drive hooked terminals. Triggers on "drive another agent", "send a prompt to a terminal agent", "have one agent prompt another", "agent drives agent", "orchestrate agents in terminals", "make Claude drive Codex", "prompt the agent running in that terminal", or wiring a loop where one coding agent supervises another.

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Post-deploy monitoring. Watches live app via headless browser for console errors, performance regressions, page failures, visual anomalies. Alert on deltas not absolutes, transient tolerance (2+ consecutive checks). Default 10-minute monitoring window. Baseline capture mode.

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Builds iOS/macOS/watchOS/tvOS applications with Swift 5.9+, SwiftUI, async/await concurrency, actors, protocol-oriented programming, and Vapor.

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。

Thorough code review for pull requests on watched repositories

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Triage and categorize new GitHub issues on watched repos

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Monitor watched GitHub repos for new activity and changes

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Summarize activity for watched blockchain wallets

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Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key constraints, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails/SQLAlchemy), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compatibility validation, and bulk data loading. Load before answering DSQL limit or error-code questions; it has limits docs omit. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader, DSQL slow, DSQL performance, DSQL wait events, DSQL AAS.

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Turn new AI names or unstructured AI market signals into a keyword priority table. For each name it finds the generic search terms downstream of it, screens them for relevance, and checks novelty, demand size and momentum against Google Ads volume and Google Trends, ending in prioritize, watch or skip. Use when the user has new model, product or repository names (or an AI listening report, a batch of posts, a changelog) and asks which keywords to build pages or tools for. Runs the new-term-radar command line; needs an AISA_API_KEY, and a GOANYAPI_API_KEY improves it.

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One run from signal to decision. Finds the names of new AI models, products and repositories seen in the last 24 hours (X, Hugging Face, GitHub, sitemaps), then builds a keyword priority table for them, each new name and the generic search terms behind it, sized against Google Ads volume and Google Trends, with prioritize, watch or skip. Use when the user wants today's new AI keywords, an AI trend radar, or asks what new AI terms are worth building a page or tool for. Also takes names the user already has. Runs the new-term-radar command line; needs an AISA_API_KEY.

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When the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.

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Adjudicates whether a hit generated by sanctions, PEP, or adverse-media screening is a true positive, false positive, or requires human escalation. Use whenever a user presents a screening alert, a name match against a watchlist (OFAC SDN, EU consolidated list, UK OFSI, UN list, PEP list, adverse media hit, etc.), or asks to clear a screening hit / reduce false positives / determine whether a flagged name is actually the listed party. Use even when the user describes the task casually — "is this person actually on the sanctions list", "did we get a real match", "clear this alert", "I have a hit on X" — these are all screening-adjudication tasks. Produces a deterministic determination with full audit trail (structured JSON + human-readable narrative). Designed for use by compliance analysts and screening systems.

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。