「codebase」の検索結果

Meta-skill for internal codebase exploration at varying depths (quick/deep/architecture)

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。

Codebase'i knowledge graph olarak analiz et. Dependency, call graph, hotspot analizi.

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。

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

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。

Build cross-platform .NET applications with Uno Platform targeting WebAssembly, iOS, Android, macOS, Linux, and Windows from a single XAML/C# codebase. USE FOR: building cross-platform apps from a single C# and XAML codebase; targeting WebAssembly, iOS, Android, macOS, Linux, and Windows simultaneously; migrating WPF or UWP. 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.

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。

A 5-minute gate the coder runs at project kickoff (and again whenever the repo shape changes). Classifies the workspace into Greenfield / Brownfield-large / Mid-size-familiar / Library-SDK, then maps the state to an agent strategy (autonomy, context primitive, tool choice). Use BEFORE picking Cursor vs Claude Code vs Aider, BEFORE turning on repo-map, BEFORE writing the first prompt. Skip only if the same repo was gated within the last day and nothing changed. Search keywords: greenfield vs brownfield, new project vs existing codebase, project setup strategy, legacy codebase agent, where to start a coding agent.

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。

Statistical rule discovery from Go codebase patterns.

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。

Analyze codebase with tokei (fast line counts by language) and difft (semantic AST-aware diffs). Get quick project overview without manual counting. Triggers on: how big is codebase, count lines of code, what languages, show semantic diff, compare files, code statistics.

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 when auditing a codebase for semantic duplication - functions that do the same thing but have different names or implementations. Especially useful for LLM-generated codebases where new functions are often created rather than reusing existing ones.

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。

Scans an existing codebase and generates project-specific skills that capture inferred conventions such as naming, file organization, framework usage, data access, error handling, and testing style. Writes into the project's chosen skill directory (e.g., `.claude/skills/`, `.agents/skills/`, or a custom path). Use when the user asks to "extract skills from the codebase", "create project skills", "infer project conventions as skills", "codify patterns as skills", or "mine the repo for best practices".

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。

Analyze a codebase and produce a structured threat model at .turbo/threat-model.md covering assets, trust boundaries, attack surfaces with existing mitigations, attacker stories, and calibrated severity. Use when the user asks to "create a threat model", "threat model", "threat model this codebase", "security analysis", "analyze the attack surface", "what are the threats", or "identify security risks".

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。

Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on source code — never implements, fixes, or refactors anything itself. Use when asked to audit a codebase, find improvement opportunities (bugs, security, performance, test coverage, tech debt, migrations, DX), suggest features or where to take the project next (roadmap, product direction), or generate handoff plans for another agent to implement.

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。

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

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。

Provides autonomous project pattern learning by analyzing the codebase to discover development conventions, architectural patterns, and coding standards, then generates project rule files in .claude/rules/. Use when user asks to "learn from project", "extract project rules", "analyze codebase conventions", "discover project patterns", or wants to auto-generate Claude Code rules for the current project.

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。

Runs a three-tier codebase audit (git history, targeted scans, full review) with gating. Use when auditing a codebase before release or after incidents.

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。

Detects codebase bloat via dead code, duplication, complexity, and doc bloat scans. Use when codebase feels large or before a release.

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。

Presents adaptive codebase challenge questions with multiple-choice and trace exercises. Use when testing contributor knowledge of the codebase.

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。

Uses 1M context window to ingest an entire codebase and output a file-by-file migration plan. Supports JS to TS, React class to hooks, framework migrations, and more. Generates migration-plan.md with file inventory, dependency graph, migration order, file-by-file changes, estimated effort, and risk assessment.

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。

Generate project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, confidence scoring, and command validation.

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。

Transform legacy codebases into AI-ready projects with Claude Code configurations. Use when (1) analyzing old projects to generate AI coding configurations, (2) creating CLAUDE.md, skills, subagents, slash commands, hooks, or rules for existing projects, (3) user wants to enable vibe coding for a codebase, (4) onboarding new team members with AI-assisted development, (5) user mentions "make project AI-ready", "generate Claude config", or "create coding standards for AI".

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。

Quickly analyzes Python repositories to understand their purpose, structure, and setup requirements. Use when Claude needs to onboard to a new Python codebase, understand project structure, identify entry points, determine dependencies, or generate setup instructions. Trigger when users ask to "analyze this Python repo", "understand this codebase", "how do I run this project", "what does this repo do", or provide a Python repository path for quick start guidance.

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。

Generate concise summaries of source code at multiple scales. Use when users ask to summarize, explain, or understand code - whether it's a single function, a class, a module, or an entire codebase. Handles function-level code by explaining intention and core logic, and large codebases by providing high-level overviews with drill-down capabilities for specific modules.

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。

Inspect codebases: LOC, languages, ratios via pygount.

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。

Scan a codebase for deepening opportunities, present them as a report — HTML on request — then grill through whichever one you pick.

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。

Onboard a new QA engineer to an existing codebase, or audit an existing test architecture. Produces a 30-day ramp plan: codebase orientation, framework walkthrough, test architecture audit, mentorship pairing, and first-test guidance. Use when: "QA onboarding," "new tester," "ramp up," "test architecture audit," "first 30 days," "QA mentorship," "joining QA team." Not for: setting up QA on a brand-new project from scratch — use `qa-start`. Related: qa-start, qa-project-context, shift-left-testing, ai-qa-review.

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。