“exploration”的搜索结果

AI intuitive exploration for approaching unknown codebases, problems, or systems without preconceptions. Adapts the Coordinate Remote Viewing protocol to AI investigation: cooldown (clear assumptions), staged data gathering (raw signals → dimensional → analytical), AOL management (separating observations from premature labels), and structured review. Use when investigating an unfamiliar codebase with unknown architecture, debugging a problem where premature hypotheses could mislead, exploring a domain with limited context, or when previous attempts have been led astray by assumptions and "beginner's mind" would be more productive.

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Apply ant colony optimization and foraging theory to resource search, exploration-exploitation tradeoffs, and distributed discovery. Covers scout deployment, trail reinforcement, diminishing returns detection, and adaptive foraging strategy selection. Use when searching a large solution space where brute-force enumeration is impractical, balancing investment between exploring new approaches and deepening known good ones, optimizing resource allocation across uncertain opportunities, or diagnosing premature convergence on local optima.

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AI solution exploration using ant colony optimization — deploying scout hypotheses, reinforcing promising approaches, detecting diminishing returns, and knowing when to abandon a strategy. Use when facing a problem with multiple plausible approaches and no clear winner, when the first approach is not working but alternatives are unclear, when debugging with no obvious root cause requiring parallel hypothesis investigation, or when previous attempts have converged prematurely on a suboptimal approach.

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AI unconstrained creative exploration — deliberately dropping structure to let associations form freely. The one skill that inverts the standard procedure format: instead of inputs, steps, and validation, dream opens space for emergence without evaluation. Use before design work, before naming things, before choosing between approaches, or anywhere that premature structure kills the best option.

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Explore a feature or CLI, repair verified defects, review adversarially, and leave one existing or new PR ready for its authorized reviewer. Use for "explore X", "test and ship X", "run the pipeline on X", "stress-test X", or "take X through the full pipeline" when exploration and integration matter. Supports lean autonomous, structured economy (default), and explicit hard-gated runs. Routine checkpoints do not require user approval; actual founder, L4, security, schema and merge gates always apply. Not for a one-file edit with no exploration need. Uses scripts/state.sh for resumable checkpoints.

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Rapid UI prototyping — generates 3 visually distinct HTML/CSS components from a text prompt and serves them in a live browser gallery. Use when user runs /prototype, asks to "prototype a UI", "mock up a component", "generate HTML mockups", "create UI variations", "design exploration", "quick UI concept", or wants to see multiple visual approaches to a component. Also trigger when user says "show me different ways to build...", "explore different designs for...", or wants rapid visual exploration of a UI idea — even without saying "prototype" explicitly.

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Summary statistics, data exploration, and descriptive analysis. Use for: summary stats, describe data, data exploration, distributions, means, medians, correlations, cross-tabs.

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复杂探索资产化 / Complex Exploration：当用户面对复杂、不确定、多轮迭代的产品策略、Roadmap、商业化定价、竞品定位、复杂 PRD 前置探索、项目复盘或方法论沉淀任务时使用。它先判断任务类型，暴露隐含假设，重构真正问题，规划探索路径和中间产物，并在结束后沉淀认知、结构、方法论、工具和影响力资产。不用于简单润色、翻译、摘要、明确执行任务；问题只需早期认知校准时优先用 ai-collaboration-calibration，已有方案要压力测试时用 grill-me，系统专题研究用 research-topic-compiler。

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Standard Operating Procedures for read-only codebase exploration, symbol search, dependency mapping, code tracing, code review, architecture analysis, technical research, source evaluation, and evidence-based reporting.

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Performs runtime mobile security exploration of iOS applications using Objection, a Frida-powered toolkit that enables security testers to interact with app internals without jailbreaking. Use when assessing iOS app security posture, bypassing client-side protections, dumping keychain items, inspecting filesystem storage, and evaluating runtime behavior. Activates for requests involving iOS security testing, Objection runtime analysis, Frida-based iOS assessment, or mobile runtime exploration.

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Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a live Jupyter kernel. No new tools required.

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An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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Performs runtime mobile security exploration of iOS applications using Objection, a Frida-powered toolkit that enables security testers to interact with app internals without jailbreaking. Use when assessing iOS app security posture, bypassing client-side protections, dumping keychain items, inspecting filesystem storage, and evaluating runtime behavior. Activates for requests involving iOS security testing, Objection runtime analysis, Frida-based iOS assessment, or mobile runtime exploration.

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在反复搜索和比较候选方案时，将每轮依据与判断追加到台账中。核对与上次结果的一致性及复验结果，明确进入真实交易或生产操作前须满足的条件。

• 通过多轮试验比较候选方案
• 记录每次判断的依据
• 用新信息重新评估上次入选方案

将演讲笔记或PowerPoint文件制作成可在浏览器中播放的HTML幻灯片，通过预览选择设计，并调整布局和动画，使每页内容完整显示在屏幕内。

• 制作演讲或内部汇报幻灯片
• 将PowerPoint转换为HTML
• 通过预览选择演示文稿设计

让AI操作模拟器中的Pokemon，结合屏幕截图和内存状态完成移动、对话与战斗，并支持浏览器观看、进度记录以及游戏状态的保存与恢复。

• 观看AI游玩Pokemon
• 使用自行准备的ROM开始游戏
• 从已保存的状态继续游戏

使用p5.js，通过粒子运动和数学规则创作原创生成艺术。作品配有交互界面，可调整数值、比较变化，使用种子复现相同条件下的画面，并保存为PNG。

• 用代码创作原创艺术
• 尝试粒子与流动效果
• 调整种子和参数比较变化

Create a hand-drawn HTML wireframe for exploring screen layouts before detailed design. Graph paper, sticky notes, sketch tabs, and rough chart placeholders present an early concept with annotations for discussion and review.

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• Sketching an initial portal or dashboard layout
• Presenting layout options with labeled sketch tabs
• Adding review notes to an early screen concept

Generate images in real time through fal.ai, with streaming generation for exploring visual directions. Use it to develop moodboards, create draft variations, and try successive ideas during early creative work.

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• Exploring visual directions for a moodboard
• Creating variations of a draft image
• Trying successive ideas during early creative work

Analyze project code, configuration, and documentation to build a graph for interactive architecture exploration. It maps components and dependencies, supports Japanese descriptions, and can update the analysis based on changes since the previous analyzed commit.

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• Understand the structure of an unfamiliar project
• Explore dependencies between components
• Generate architecture descriptions in Japanese

Analyze Excel and CSV files to summarize statistics, filter records, compare groups, and join data across sheets or files. Export results as CSV, JSON, or Markdown tables for further work or sharing.

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• Compare monthly sales and top-selling products
• Combine order records with customer data
• Check missing values and statistical summaries

Start a named browser session and preserve its initial screen, page structure, and action history. Keep a trace of site exploration and establish a reusable session for later extraction, form filling, or browser testing.

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• Explore a site while preserving an action trace
• Save an initial screenshot and accessible page structure
• Prepare a session for extraction or form filling

Turn a broad coding request into a concrete work plan grounded in the codebase. After user approval, it documents affected files, decisions, dependencies, and verification steps so a separate worker can carry out the implementation.

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• Planning a large feature before coding
• Turning a vague request into defined tasks
• Preparing an implementation handoff

Analyze existing evolutionary trees and other hierarchical trees with ETE 4. Edit branches, add annotations, compare branching structures, explore large trees interactively, and render figures for publication using the appropriate optional visualization components.

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• Prune selected species and change a tree's root
• Compare branching structures between two trees
• Add NCBI or GTDB taxonomy information