“heap”的搜索结果

Use when capturing or analyzing an iOS .memgraph, especially when the task mentions a memory leak, heap growth, persistent memory increase, ownership path, or matched-capture comparison with Apple CLI tools. Covers unambiguous Simulator capture, leaks/heap/vmmap/malloc_history evidence, raw artifact preservation, and same-flow verification. Use debugging-instruments for interactive Xcode Memory Graph, Instruments, generic retain-cycle inspection, or LLDB work.

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Break down Claude Code usage by model family (Opus / Sonnet / Haiku) from the Agent Monitor dashboard — each family's share of tokens, share of cost, and the spots where an expensive model is doing cheap work. Pulls per-model token and cost splits from /api/pricing/cost, current rates from /api/pricing, fleet token totals from /api/analytics, and per-session model assignment from /api/sessions. Use when deciding model routing or whether to downshift work to a cheaper tier.

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Run fable-mode execution discipline on Claude Haiku. Routes the task to the @fable-worker-haiku agent, whose definition carries the staged loop with tightened verification (no bare "unverified" allowed) and an escalate-don't-improvise rule. Trigger when the user explicitly asks for thorough/systematic handling run cheaply or fast ("fable on haiku", "deep work mode but cheap", "stage this on haiku"). For bulk mechanical work. Do NOT use for tasks needing synthesis — benchmark note: at n=1 the skill's effect on Haiku swung both directions (+25 / −17); route quality-critical work to fable-sonnet instead.

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Use the open-source free `Asynkron.Profiler` dotnet tool for CLI-first CPU, allocation, exception, contention, and heap profiling of .NET commands or existing trace artifacts. USE FOR: Asynkron.Profiler setup; automation-friendly profiling output; CPU, allocation, exception, contention, and heap investigation. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Use when an app, service, or user-mode driver host heap fails or Application Verifier detects corruption; inspect history and bounds. Not for kernel pool corruption or ordinary OOM.

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Algorithms and data structures from first principles through advanced analysis. Covers sorting (bubble, insertion, selection, merge, quick, heap, radix), searching (linear, binary, BFS, DFS, Dijkstra, A*), fundamental data structures (arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, tries), complexity analysis (Big-O, Big-Omega, Big-Theta, amortized), recurrence relations, and algorithm design paradigms (divide-and-conquer, greedy, dynamic programming, backtracking). Use when analyzing, selecting, implementing, or comparing algorithms and data structures.

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Generate frontend code drafts using Gemini 3.1 Pro's one-shot UI generation. Use when building React components, HTML/CSS pages, SVG animations, or converting screenshots to code. Produces fast, cheap drafts (7.5x cheaper than Opus) that Claude reviews and refines. Supports design system context loading (1M token window), screenshot-to-code pipeline, and physics-based SVG animation. Trigger: "generate a UI", "screenshot to code", "draft a component", "SVG animation", "convert this design", "gemini forge", "forge a UI".

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Use when CPU usage is high, memory grows unboundedly, goroutines leak, mutex contention shows in traces, or escape analysis suggests excess heap allocation. Triggers: net/http/pprof endpoint exposure, go tool pprof analysis, flamegraph generation, allocs vs inuse_space heap profiles, runtime/trace event timeline, mutex profiling, block profiling, goroutine dumps, GC pressure measurement. NOT for non-Go languages, distributed tracing (use OpenTelemetry skill), or production telemetry pipelines.

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Expert-level data structures covering arrays, linked lists, trees, heaps, hash tables, graphs, and advanced structures like segment trees, tries, and union-find.

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Audit and cut the cost of a machine's scheduled routines: measure tokens per run, pick a lever (cheaper model, split, lower frequency, shorter prefix, retire) and prove the saving with a before-and-after measurement. Use when background routines burn more than interactive work. Triggers: /routine-opt, /ropt, routine audit, cheaper routines.

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Change what something MEANS before you change what it IS — Rory Sutherland's cheapest lever. Use when a product, price, feature, or message is objectively fine but feels cheap, generic, boring, overpriced, or unjustified; when the user says 'how do we make this feel more premium/valuable/exciting', 'people don't get why it's worth it', 'how do we reposition/rename/repackage this', or wants to raise perceived value without changing the product or cutting the price. Covers renaming, recategorising, mental accounting (price framing), creating artificial value, and context-shifting. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic. 日本語の相談（「高いと言われる」「安っぽく見える」「値下げせずに高級感を出したい」）にも使う。中文咨询（“被说太贵”“显得廉价”“不降价也想显得高级”）也适用。

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Plan which model tier handles which work BEFORE execution begins — a high-cognition model deeply understands the problem, lays the foundations, then emits a modular plan assigning each module the cheapest tier that can safely execute it, with escalation tripwires and one-way-door protection. Advisory only: it announces "next module → tier X / model Y" at each boundary and the HUMAN switches models — harnesses like Cursor cannot switch mid-run. Load when the user asks which model to use, wants a model plan, model tiers, model-tier routing, assign models to tasks or modules, says "cheap model got stuck", "which model for this task", "cost-efficient model choice", or when implementation-plan / problem-to-plan need a model: tier column. NOT dynamic-routing (plan-path selection after failure) — this skill assigns cognition tiers to work.

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Systematic performance profiling and optimization methodology. Covers CPU profiling, memory profiling, heap analysis, goroutine/thread analysis, FlameGraph generation, latency profiling, database query profiling, network I/O profiling, and continuous performance regression detection. USE WHEN: investigating performance issues, optimizing slow code, reducing memory usage, diagnosing GC/CPU bottlenecks, finding N+1 queries, analyzing pprof/flamegraph output, or establishing a performance baseline. Triggers on "profiling", "performance analysis", "slow code", "flame graph", "pprof", "heap dump", "memory leak", "CPU spike", "bottleneck".

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Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

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Hunt Spring Boot specific vulnerabilities — Actuator endpoints (heapdump, env, loggers, mappings, shutdown), Spring Expression Language (SpEL) injection → RCE, H2 console RCE, Jolokia JMX exposure, Spring4Shell (CVE-2022-22965), Spring Cloud Function SPEL (CVE-2022-22963), heap dump credential extraction. Use when target runs Spring Boot — detected via X-Application-Context header, /actuator, Whitelabel Error Page, or Java stack traces.

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测量OpenClaw测试和插件验证的耗时与内存使用，调查模块加载及启动过程中的原因，在保留测试验证范围的同时改进，并比较变更前后的数值。

• 调查测试缓慢和模块加载开销
• 减少插件验证耗时和内存使用
• 调查测试期间的内存增长

通过暂停Node.js执行、检查变量和跟踪执行路径，调查异步任务停滞、测试偶发失败等问题，也可分析内存持续增长和CPU负载的原因。

• 调查异步任务停滞的原因
• 调试偶发失败的测试
• 排查启动和子进程问题

调整重复发送的LLM API请求，让共用指令和资料便于缓存复用；分别比较输入、缓存读写、输出及存储费用，并整理用量、响应时间和复用范围。

• 重排重复API请求中的共用指令
• 估算缓存读写和存储费用
• 测量缓存使用率与响应时间

提供LLM API费用管理的设计示例，将按任务复杂度选择模型、记录预算、重试临时错误和缓存共用提示词结合起来，用于检查累计费用与调整调用方式。

• 检查LLM应用的API费用
• 按批处理难度选择模型
• 对照预算检查累计费用

将大型调研或生成任务分配给多个AI模型，完成计划评审、并行执行、成果验证与整合，并记录各负责角色的进度和重试情况。

• 并行调研多个竞争对手
• 分担大型内容生成任务
• 让顾问评审计划或草稿

将设计或实现中的重要决策交给不继承工作过程的独立审查者验证，找出隐藏前提和失败条件，再据此修正问题或重新评估决策。

• 验证设计方案的前提
• 检查陌生代码中的决策
• 审查认证与敏感处理逻辑

通过具体规则统一字体排版、留白、配色、卡片层叠和克制的动画，并将这些设计编写为网页代码，制作质感细腻、风格沉静的页面。

• 制作注重字体排版的落地页
• 制作非对称的SaaS功能介绍页
• 用柔和阴影与留白展示作品

比较实际使用不同AI模型的费用与全部任务统一使用同一模型的估算费用，按时间范围输出节省金额和比例，帮助核查模型选择的成本表现。

• 在费用评审中报告节省效果
• 核查模型选择设置变更后的费用
• 在CI中检查节省比例门槛

Find personal photos in an uploaded library or connected device gallery using descriptions, dates, and locations. Inspect candidate photos and their capture details to identify the image relevant to a request.

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• Finding photos from a trip or event
• Searching for photos taken near a specific place
• Checking photo details and capture dates