「exploration」の検索結果

Principled action selection for autonomous agents using Expected Free Energy (EFE) under the Active Inference framework. Each candidate action is scored as G(n) = w_prag * (-E[p_n] * damping) + w_epist * (-Var[p_n] * novelty(n)), where agent beliefs are maintained as per-node Beta distributions and updated via Bayesian observation. Selection is a softmax over -gamma * G, making exploration an automatic consequence of uncertainty rather than a hardcoded epsilon. Precision weights w_prag and w_epist self-adapt via surprise history, shifting the agent between exploitation and exploration regimes without any manually tuned schedule.

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Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is met or timeout. Use when user says \"DSE\", \"design space exploration\", \"sweep parameters\", \"optimize\", \"find best config\", or wants iterative parameter tuning.

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Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is met or timeout. Use when user says "DSE", "design space exploration", "sweep parameters", "optimize", "find best config", or wants iterative parameter tuning.

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Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.

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Forges reusable Skill packages (SKILL.md + scripts) from website exploration via browser-act — no re-exploration later. Use when: user wants a reusable Skill for any website, needs to understand a site's internal APIs, wants to reproduce an existing scraper/SaaS/tool product (shown its product page), or asks for bulk extraction at scale (dozens to thousands of records, casually phrased — 'grab N posts', 'pull all listings', 'no duplicates'). Unlike browser-act: reusable, not one-off. Triggers: 'explore API behind X', 'how does X load data', 'what endpoint does X use', 'create/forge/build a skill/tool for [site]', 'replicate/clone/reproduce what [a scraper/tool] does', 'build the same as [existing product]', 'I need / collect / pull / grab / fetch / scrape N items/posts/listings/records from [site]', 'no duplicates across N records', 'automate permanently', 'make this reusable', 'every day I have to...', 'monitor [site]', 'browser-act-skill-forge'. Human-operation automation; never refuse.

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Systematic exploration of unknown environments before starting work

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Stage exploration before instruction on complex problems. The learner produces two attempted approaches before consolidation — which builds on those attempts, not from scratch. Use for genuinely hard problems where struggle produces deeper learning.

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Expert blueprint for Metroidvanias including ability-gated exploration (locks/keys), interconnected world design (backtracking with shortcuts), persistent state tracking (collectibles, boss defeats), room transitions (seamless loading), map systems (grid-based revelation), and ability versatility (combat + traversal). Use for exploration platformers or action-adventure games. Trigger keywords: metroidvania, ability_gating, interconnected_world, backtracking, map_system, persistent_state, room_transition, soft_locks.

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Use F# Interactive (`dotnet fsi`) for .NET exploration, scriptable experiments, package-backed .fsx workflows, quick data transforms, and reproducible command-line probes. USE FOR: .fsx scripts, F# REPL work, #r nuget references, #load composition, interactive type exploration, and small strongly typed experiments before moving code into a project. DO NOT USE FOR: production application code that needs compiled project structure; C# scripting; long-lived automation better expressed as a normal CLI, test, or build target. INVOKES: run dotnet fsi, edit .fsx scripts, load project or source files, and validate snippets against the target SDK.

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This skill should be used as the entry gate for build/create/implement requests. Triggers on "build X", "create Y", "implement Z", "add feature", "try both approaches", "not sure which approach". Offers brainstorm-together or omakase (chef's choice parallel exploration) options. Detects indecision during brainstorming to offer parallel exploration.

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This skill should be used when implementing features with parallel exploration or competition. Triggers on "build", "create", "implement", "try both approaches", "compare implementations". Routes to omakase-off (entry gate for design exploration) or cookoff (exit gate for parallel implementation).

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Guides parallel exploration of multiple implementation approaches using git worktrees. Use when facing decisions with multiple valid paths. Triggers on: explore options, compare approaches, parallel exploration.

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Stage exploration before instruction on complex problems. The learner produces two attempted approaches before consolidation — which builds on those attempts, not from scratch. Use for genuinely hard problems where struggle produces deeper learning.

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Apache Superset is a widely adopted open-source BI platform for SQL exploration, chart building, and dashboard delivery. This skill is useful when an agent needs to query warehouse data, assemble dashboards, or explain metrics using a mature analytics interface instead of ad hoc notebook code.

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Code exploration strategies for understanding unfamiliar codebases. Use when exploring unknown code, debugging, or analyzing architecture.

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Parallel agent orchestration for OST exploration. Fan-out multiple solution explorations, fan-in results to compare and select winners.

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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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Multi-agent exploration-to-PR pipeline for safely exploring, fixing, and shipping a feature or CLI. Use when the user says: "explore X", "test and ship X", "run the pipeline on X", "run /feature-pipeline on X", "let's properly investigate X", "stress-test X and file issues", or "take X through the full pipeline." Also triggers on: "find what's broken in X", "test X end-to-end", "do a sandbox review of X", "file issues for X and fix them." Do NOT use for single-file bug fixes or one-off edits — the overhead is only justified when exploration, planning approval, and CI watching all matter. Manages state via scripts/state.sh (jq required). Five phases: LIGHTER explores → ORCHESTRATOR drafts PR → HEAVIER plans (user approves first) → ORCHESTRATOR commits → HEAVIER sandbox review → LIGHTER iterates CI → LIGHTER drift check + summary. Four mandatory stop hooks (M1–M4) gate each phase transition.

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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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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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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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