「exploration」の検索結果

Deep review — random exploration + cross-agent review, alternating. Finds integration bugs that self-review misses. Run every 30–60 min during active implementation.

日本語の概要は準備中です。原文の説明を表示しています。

Use this to design a nice UI in single html as inspiration & UI exploration.

日本語の概要は準備中です。原文の説明を表示しています。

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

日本語の概要は準備中です。原文の説明を表示しています。

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

日本語の概要は準備中です。原文の説明を表示しています。

Phase 1 of the feature-pipeline suite — stress-test a CLI command, feature, flag, or module as a developer seeing it for the first time, then file GitHub issues for every distinct finding. Use when someone says "explore X", "test X", "find issues with X", "what's wrong with X", or after /feature-pipeline init. Also triggers standalone as /fp-explore <feature> when you want just the exploration phase without the full pipeline. Reads the target feature from .fp-state.json (or the argument if provided). Completes in one bounded turn and prints the M1 stop hook so the user decides whether to continue to /fp-plan.

日本語の概要は準備中です。原文の説明を表示しています。

Plan a feature before writing any code. Use this skill at the START of non-trivial feature work — when the user says they want to "add", "build", "implement", "create", "develop", or "ship" a feature, endpoint, component, page, service, or capability. Especially trigger when the user's prompt is short or under-specified ("add a dashboard", "build an auth flow", "let's do user activity reports") — those need planning, not direct execution. The skill walks through requirements clarification, codebase exploration, sketches a Mermaid architecture/flow diagram of the proposed design, and produces a plan.md document the user can hand off to a fresh AI session for execution. Skip only when the change is genuinely trivial (typo fixes, single-line tweaks, renames). When in doubt, plan.

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This skill enables GitHub repository documentation exploration using DeepWiki API directly via curl. Use when researching repository structure, understanding library APIs, or asking questions about open-source projects. MCP server not required.

日本語の概要は準備中です。原文の説明を表示しています。

UI design and implementation for real product surfaces with taste-first critique, reference routing, project-specific design briefs, variation comparison, and implementation verification. Use when Codex should redesign, build, polish, or critique frontend UI by reading project context, exploring taste, routing through `references/catalog.md`, synthesizing `PROJECT_DESIGN.md` or `design.md`, using investment gates to avoid wrong taste/reference/scope, implementing in the existing stack, and evaluating whether the result is actually better. Best for app screens, dashboards, landing pages, query tools, product surfaces, and design explorations; not for backend-only work or tiny cosmetic edits.

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Deep codebase exploration, architectural research, and solution discovery. Use when investigating problems, understanding complex flows, or evaluating technical approaches without jumping straight into code mutation or TDD.

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Guided game concept ideation — from zero idea to a structured game concept document. Uses professional studio ideation techniques, player psychology frameworks, and structured creative exploration.

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Zero-dependency live-preview server providing an agent↔browser bidirectional companion. Watches a content directory, wraps HTML fragments in a frame template, broadcasts live-reload over WebSocket, and captures browser-side click events to a JSONL file. Use when an agent needs to show HTML to the user in a browser and react to clicks — mockups, design exploration, gallery browsing, multi-select choices, prototype iteration. Triggered by "live preview", "browser companion", "preview server", "browser mockup", "show in browser", "arkhe-preview", or any skill that wants to write HTML fragments and have the user see them reload in real time.

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Run the data science workflow end to end — framing, exploration, feature engineering, modeling, validation, and communicating results. Use when turning raw data into decisions or predictions.

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Read-only repository explorer. Use PROACTIVELY for cold-start exploration, broad cross-file localization, or when a direct search has failed and you need to find where something lives. Skip it when the issue already names the exact file or symbol, or a previous turn already returned usable file:line evidence. Returns only compact path:line citations; its reads and greps never enter the main conversation.

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Systematically QA test a web application. Use when asked to "qa", "QA", "test this site", "find bugs", "dogfood", or review quality. Four modes: diff-aware (automatic on feature branches — analyzes git diff, identifies affected pages, tests them), full (systematic exploration), quick (30-second smoke test), regression (compare against baseline). Produces structured report with health score, screenshots, and repro steps.

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Strategic planning consultant that produces decision-complete work plans through Socratic interview, codebase exploration, Metis gap analysis, and optional Momus high-accuracy review. MUST USE when the task has 5+ steps, scope is ambiguous, multiple modules are involved, or the user asks for a plan. Triggers: plan this, create a work plan, interview me, start planning, prometheustic, plan mode, help me plan this, break this down.

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Create stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.

日本語の概要は準備中です。原文の説明を表示しています。

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

日本語の概要は準備中です。原文の説明を表示しています。

User asks for design, planning, or approach exploration before implementation. Covers new features, components, refactors, or architecture decisions. Creates design docs and proposes approaches with trade-offs.

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Research process for comparing frontier harnesses (Claude Code, Codex, OpenClaw, autoresearch, Prime Agent) with the original/upstream implementation and performing GAP analysis before feature implementation. Triggered by "research" ("리서치"), "gap", "frontier", "harness", "case study" ("사례 조사"), "pattern exploration" ("패턴 탐색"), "comparative analysis" ("비교 분석") keywords.

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Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.

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Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.

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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

日本語の概要は準備中です。原文の説明を表示しています。

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

日本語の概要は準備中です。原文の説明を表示しています。

Workflow Phase 3 — Research and Solution Exploration. Surfaces 2–4 candidate solution approaches with concrete tradeoffs, grounded in current library docs (fetched through the provider named in `.claude/docs-provider.json`) — never in training-data recall. Output lives at `docs/research/<slug>.md`. Candidate ranking and the memo execute in main context; doc/source gathering MAY be delegated to read-only advisory subagents (seed.md §4.2-A).

日本語の概要は準備中です。原文の説明を表示しています。