Results for “persistent”

Design persistent agent memory that retains knowledge across sessions and retrieves relevant facts when needed. Compare storage approaches, track people and changing information, and plan memory consolidation and evaluation based on the kinds of questions the agent must answer.

• Retain user preferences across conversations
• Track people and entities consistently across sessions
• Retrieve facts valid at a specific point in time

Build a lasting research knowledge base linking papers, ideas, experiments, and claims. Keep failed approaches and unresolved gaps available for future work, generate compact summaries, and check records for missing connections or conflicting evidence.

• Keeping papers and research ideas in a project knowledge base
• Recording failed approaches and their reasons
• Reviewing links between experiments and claims

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Create Kubernetes deployment files and Helm charts, reusable application packages. Configure permissions, network isolation, and persistent storage, then investigate pod failures and resource usage to adjust workloads and verify deployment health.

• Create deployment YAML and Helm charts
• Configure access permissions and network isolation
• Set up persistent storage and sensitive configuration

Keep context for work that spans multiple sessions without belonging to a specific project phase. The skill manages lightweight persistent threads, letting you create, list, resume, check, and close them as work progresses.

• Keeping context for work across sessions
• Recording work outside a specific phase
• Listing open or resolved threads

Start an internal investigation after a complaint or allegation. The skill collects intake details, creates a checklist of information sources, and sets up a persistent investigation log for attorneys preparing an investigation workspace.

• Opening an investigation after an internal complaint
• Collecting initial details about an allegation
• Preparing a sources checklist and investigation log

Sets up AFL++ to test C/C++ programs with varied generated inputs, a technique called fuzzing. Covers running tests across CPU cores, monitoring progress, reducing input collections, and reproducing crashes for investigation.

• Running C/C++ fuzzing across multiple CPU cores
• Exploring further after libFuzzer progress stalls
• Monitoring fuzzing progress and stability

Create an isolated development container configured for Claude Code and the project's languages. Generate container files, editor settings, dependency setup commands, and persistent storage for Python, Node/TypeScript, Rust, and Go projects.

• Add devcontainer support to a project
• Set up an isolated Claude Code workspace
• Configure development tools for a multilingual project

Work through the move from product manager to Director with questions tailored to your stage. Get practical recommendations for promotion preparation, interviews, early leadership challenges, or persistent difficulties in the role.

• Identifying development priorities before a Director promotion
• Preparing experience stories and answers for Director interviews
• Addressing delegation and direction-setting challenges after promotion

Fix Blazor issues during prerendering, when the server sends initial HTML before interactive features load. Preserve component state to avoid repeated data requests and flicker, handle unavailable services, and configure prerendering or page routing where needed.

• Fixing duplicate data requests on initial page loads
• Reducing flicker when a page becomes interactive
• Handling client services unavailable during prerendering

Connect Timeline Inspector’s Refine panel to an agent through a chat-based fallback loop. It returns animation adjustments, groups transitions by component and phase, and writes accepted timing changes to source files when apply jobs arrive.

• Handling Refine requests without a persistent agent CLI
• Showing animation timing suggestions in the panel
• Grouping transitions into opening and closing phases

Assess the storage and retrieval layers of RAG systems, which search documents to support AI answers. Test persistent document poisoning, cross-tenant access, source-text leaks, and manipulated search rankings using independently verifiable evidence.

• Reviewing shared knowledge bases and document chatbots
• Testing access isolation between accounts or organizations
• Checking source text and metadata in search results

Organize complex tasks using persistent Markdown files for plans, research notes, and final deliverables. Track completed phases, decisions, and errors, and revisit the plan before major actions to keep work aligned with its goal.

• Planning a task with multiple stages
• Organizing research findings and sources
• Recording progress, decisions, and errors

Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.

• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

Diagnose Codex behavior within BB, including question cards, MCP requests for user input or approval, Computer Use app access, model availability, service tiers, session options, and persistent goals, using thread and provider controls.

• Investigate a Codex turn waiting for an answer
• Review app access and MCP tool approvals
• Check models and service tiers on the execution host

Manage local development servers and APIs with macOS LaunchAgents, which run background processes. Keep servers running after the shell closes, restart them after crashes, inspect logs, and handle occupied ports when starting a service.

• Keeping a local server running after closing the shell
• Restarting a server automatically after a crash
• Checking server status and logs

Use this skill when the user is doing hands-on DOCA GPUNetIO programming — wiring a CUDA kernel on an NVIDIA GPU to a doca-eth queue via doca_gpu_eth_rxq / doca_gpu_eth_txq, standing up the per-CUDA-device doca_gpu context, designing the persistent CUDA kernel that drains the GPU-visible queue, running the dual capability check (DOCA cap-query plus cudaGetDeviceProperties), registering cudaMalloc pools via doca_buf_arr_create_*, or debugging DOCA_ERROR_* returns from the GPUNetIO API. Trigger even when the user does not explicitly mention "DOCA GPUNetIO" or "persistent kernel" — typical implicit phrasings include "CUDA kernel reading packets directly from the NIC", "GPU-initiated networking on BlueField", "DOCA_ERROR_DRIVER on doca_gpu_create", "nvidia_peermem not loaded", "kernel-per-packet is too slow", or "which GPU supports GPU-side packet I/O". Refuse and route elsewhere for general CUDA programming, DOCA Ethernet queue bring-up, DOCA DPA, or DOCA install — those belong to other skills.

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Golang CLI command tree library using spf13/cobra — cobra.Command, RunE vs Run, PersistentPreRunE hook chain, Args validators (NoArgs, ExactArgs, MatchAll, custom), persistent vs local flags, command groups, ValidArgsFunction, RegisterFlagCompletionFunc, ShellCompDirective, usage/help template customization, man-page and markdown doc generation, and testing with SetArgs/SetOut/SetErr. Apply when using or adopting spf13/cobra, or when the codebase imports `github.com/spf13/cobra`. For configuration layering alongside cobra, see the `samber/cc-skills-golang@golang-spf13-viper` skill. For general CLI architecture (project layout, exit codes, signal handling, I/O patterns), see `samber/cc-skills-golang@golang-cli`.

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Build and query Kusto graphs from natural language. Covers transient graphs (make-graph), persistent graph models/snapshots, pattern matching (graph-match), shortest paths, connected components, and graph-to-table export. Generates the edges-first thinking: define edges, define node lookups, union, make-graph. WHEN: make-graph, graph-match, graph-shortest-paths, graph-to-table, graph-mark-components, persistent graph, graph model, graph snapshot, build a graph from data, find paths between nodes, pattern matching in graph, connected components, transient graph, Kusto graph, KQL graph.

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Design and build the visual layer of a long-form talking-head video as one persistent world the camera moves through, instead of a sequence of separate graphics. Use when asked to edit a video, add motion graphics, design the visuals for a script, plan an edit, or fix an edit judged confusing, text-heavy, hard to follow, or "clearly made by an AI". Covers the persistent world, camera grammar, the spotlight rule, spatial open loops, ornament and token discipline, transcript timing, and the verification gates.

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Build and query Kusto graphs from natural language. Covers transient graphs (make-graph), persistent graph models/snapshots, pattern matching (graph-match), shortest paths, connected components, and graph-to-table export. Generates the edges-first thinking: define edges, define node lookups, union, make-graph. WHEN: make-graph, graph-match, graph-shortest-paths, graph-to-table, graph-mark-components, persistent graph, graph model, graph snapshot, build a graph from data, find paths between nodes, pattern matching in graph, connected components, transient graph, Kusto graph, KQL graph.

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Design and build the visual layer of a long-form talking-head video as one persistent world the camera moves through, instead of a sequence of separate graphics. Use when asked to edit a video, add motion graphics, design the visuals for a script, plan an edit, or fix an edit judged confusing, text-heavy, hard to follow, or "clearly made by an AI". Covers the persistent world, camera grammar, the spotlight rule, spatial open loops, ornament and token discipline, transcript timing, and the verification gates.

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Build, review, or improve Core Data persistence in apps that have not adopted SwiftData. Use when working with NSManagedObject subclasses, NSFetchedResultsController for list-driven UI, NSBatchInsertRequest / NSBatchDeleteRequest / NSBatchUpdateRequest for bulk operations, NSPersistentHistoryChangeRequest for persistent history tracking and multi-target sync, NSStagedMigrationManager for staged schema migrations (iOS 17+), NSCompositeAttributeDescription for composite attributes (iOS 17+), or when integrating Core Data threading with Swift Concurrency. For Core Data + SwiftData coexistence or migration, see the swiftdata skill instead.

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Expert blueprint for open world games including chunk-based streaming (load/unload regions dynamically), floating origin (prevent precision jitter beyond 5000 units), HLOD (hierarchical LOD for distant meshes), persistent state (track entity changes across unloaded chunks), POI discovery systems (compass, markers), and threaded loading (prevent stutters). Use for RPGs, sandboxes, or exploration games. Trigger keywords: open_world, chunk_streaming, floating_origin, HLOD, persistent_state, POI_discovery, threaded_loading.

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