'memory' 검색 결과

Route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.

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Use when the user asks to "codify how our founder sounds", "build a founder voice dossier", or "set our content pillars"; runs an 80%-extraction interview over the user's OWN posts, emails, and decks (never competitor scraping, never an invented persona) and produces the versioned voice record — a per-platform register map (incl. 小红书/微信公众号), banned phrases, per-context disclosure lines (the ECHO C2 upstream), a few-shot bank built exclusively from own posts, and 3-5 content pillars with Estimated %-allocations — submitted via memory/events/channels.ndjson for channel-registry to store as voice-dossier.md, the record every Craft-phase skill reads first. Not for the brand-level voice canon — use brand-language-codifier; not for audience/persona research — use audience-mapper. 声音档案/品牌语气/创始人语气/内容支柱/披露声明

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Use when the user asks to "plan launch pricing", "design pricing tiers / packaging", or "set up a launch discount / early-bird offer"; produces a launch pricing and packaging plan — tier structure and naming, a value-to-price map aligned to the message house, a launch-offer ladder with a true deadline reason, beta / early-adopter pricing with a post-launch graduation path, and guarantee / refund terms — with every price claim and offer term submitted to memory/events/claims.ndjson. Not for the canonical offer / claim record — use offer-claims-registry; not for paid-newsletter subscription economics — use newsletter-monetization-planner; not for ad bidding — use bid-strategy-planner. 发布定价/梯度打包/早鸟优惠/保证设计

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Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy patterns.

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.

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Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.

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Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.

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Detects long-lived objects (caches, listeners, singletons) that capture an entire enclosing scope via closures/environment capture, keeping large arrays or buffers alive, and proposes copying only the needed fields.

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Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.

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Integrates Google Mobile Ads (AdMob) into Flutter using google_mobile_ads v7.x with Banner, Interstitial, Rewarded, and Native ad formats. Use this skill whenever implementing app monetization, adding AdMob ads to Flutter, fixing ad loading or display errors, managing ad lifecycle (preload/dispose), preventing memory leaks from undisposed ads, handling GDPR/ATT consent flows, troubleshooting AdMob dashboard integration, or any time the words 'AdMob', 'google_mobile_ads', 'banner ad', 'interstitial', 'rewarded ad', 'native ad', or 'ad monetization' appear in the request. Supports Android (GMA SDK 24.9+) and iOS (GMA SDK 12.14+).

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Background processing and heavy computations using Dart Isolates with Isolate.run(), compute() function, and platform channels. Use this skill when performing CPU-intensive operations without blocking UI (image processing, data parsing, encryption), implementing background tasks that prevent jank or frame drops, handling large file operations (JSON parsing, database migrations), running parallel computations, fixing UI freezes caused by main thread blocking, using compute() for one-off background work, creating long-lived Isolates with ReceivePort/SendPort, implementing multi-threaded algorithms, debugging isolate communication issues, or preventing memory leaks from unclosed ReceivePorts. Covers isolate spawn, message passing, ReceivePort cleanup (CRITICAL for leak prevention), SendPort communication patterns, error handling, serialization rules, and performance optimization for compute-heavy operations.

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Expert-level guidance for Flutter production applications: architecture decisions (Clean/Layered/Hexagonal/Feature-First), advanced state management (BLoC/Riverpod/GetX), performance optimization (eliminating jank, memory profiling, reducing rebuild overhead), code review with design pattern enforcement, scalability assessments, native platform integration (MethodChannel/EventChannel/FFI), complex UI challenges (custom render objects, slivers), testing strategies (unit/widget/integration/golden), CI/CD pipeline architecture, build optimization, and accessibility compliance. Triggers: 'architecture decision', 'design pattern', 'performance issue', 'code review', 'production problem', 'scaling concerns', 'best practices', 'technical debt', 'refactoring strategy'. Essential for complex enterprise apps, critical production debugging, architectural refactors, or when choosing between competing technical approaches.

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Diagnoses and prevents mobile performance regressions on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers startup time, rendering and jank, memory, battery and background work, app size, the profiler and benchmark to use on each platform, and regression detection in CI. Use when an app is slow, janky, heavy or draining the battery, when setting performance budgets, or when a release must not regress.

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Search rad-mem's persistent cross-session memory database. Use when user asks "did we already solve this?", "how did we do X last time?", or needs work from previous sessions.

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Use tsift for token-efficient repository navigation, code search and reading, call graphs, diffs, logs, tests, session context, and workspace memory. TRIGGER: exploring or changing a codebase with tsift installed, or working on the tsift repo. SKIP: plain file-level glob, non-code web search. VERSION CHECK: compare `tsift --version` against tsift-version below before trusting any command text copied from this file.

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Use when you need code research over an indexed codebase with minimal context: structural graph queries (who calls X, call chains, architecture overview, dead code, cross-service HTTP routes) instead of file-by-file grep/read cycles. Also trigger to minimise token spend on repetitive code exploration.

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Use Upsy for secure access to third-party accounts and tasks involving memory, files, connected email, calendar, documents, messaging, project apps, and the cloud browser. Guide account connections, scoped permissions, and activity auditing, even when the user has not mentioned Upsy.

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Create a new Hindsight-powered subagent with long-term memory. Use when the user wants a specialized agent that learns and remembers across sessions.

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Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

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Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

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Write efficient C code with proper memory management, pointer

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool ...

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Proactive self-monitoring of infrastructure, services, and health. Tracks disk/memory/load, service health, cron job status, recent errors. Auto-fixes safe issues. Triggers on: health check, heartbeat, monitor status, service status, infrastructure check.

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Complete WhatsApp management for OpenClaw agents: per-conversation memory (groups + DMs), unanswered message tracking, loop prevention, and multi-PA coordination. Use when: tracking conversation context, recalling past decisions, finding unanswered messages, or preventing echo/duplicate message loops.

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