Results for “coding”

Install and configure ToolUniverse for any use case — MCP server (chat-based), CLI (command line with 9 subcommands), or Python SDK (Coding API with 3 calling patterns). Covers uv/uvx setup, MCP configuration for 12+ AI clients (Cursor, Claude Desktop, Windsurf, VS Code, Codex, Gemini CLI, Trae, Cline, etc.), full CLI reference (tu list/grep/find/info/run/test/status/build/serve), Coding API quickstart, agentic tools, code executor, API key walkthrough, skill installation, and upgrading. Use when user asks how to set up ToolUniverse, which access mode to use (MCP vs CLI vs SDK), configuring MCP servers, using the CLI, troubleshooting installation, upgrading, or mentions installing ToolUniverse or setting up scientific tools. Also triggers for "how do I use ToolUniverse", "what's the best way to access tools", "command line", "tu command", "coding API", "tu build".

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直近N日（デフォルト1日）のPRレビューで繰り返し指摘されている観点のうち「実際に修正すべき」と判断できるものを集約し、リポジトリルートのCODING_GUIDELINES.mdを生成・更新したうえで、commit-push + create-prでPRを作成する。サイズが膨らみすぎないよう既存ルールとの統合・圧縮も行う。

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Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

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Dense methodology covering DNS exfiltration (dnscat2, iodine, dns2tcp), HTTPS tunneling (domain fronting, CDN abuse, legitimate service channels), ICMP tunneling (icmpsh, ptunnel-ng), cloud storage dead drops (S3 presigned URLs, Azure Blob SAS tokens, GCS signed URLs), email-based exfil (SMTP, EWS, draft method), steganography (image, audio, document metadata), encoding/encryption (base64 chunking, XOR, AES), covert channels (custom protocol tunneling, HTTP header encoding, timing channels), and data staging (compression, splitting, encryption). Tools: dnscat2, iodine, dns2tcp, PacketWhisper, chisel, stunnel, icmpsh, ptunnel-ng, steghide, zsteg, OpenStego. MITRE ATT&CK: T1048 (Exfiltration Over Alternative Protocol), T1041 (Exfiltration Over C2 Channel), T1567 (Exfiltration Over Web Service), T1029 (Scheduled Transfer), T1030 (Data Transfer Size Limits), T1132 (Data Encoding), T1001 (Data Obfuscation). Use when planning or executing data exfiltration during authorized red team engagements or post-exploita...

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WAF bypass techniques checklist: encoding bypass (URL/HTML/Unicode/double encoding), case variation, comment injection, HTTP header manipulation, chunked encoding, IP rotation, timing attacks, and payload obfuscation per WAF vendor. Use when WAF is blocking payloads during web app tests.

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Audit a focused part of an existing codebase, reconstruct its behavior and past decisions, and record gaps in reasoning, coding practices, features, and technology choices. Use the findings to plan, test, implement, and verify improvements.

• Review an inherited codebase
• Adopt the ECC engineering workflow midway through a project
• Rethink a module before adding more features

Create an SVG usage card from local Claude Code, Codex, Gemini CLI, and OpenCode logs. It summarizes token counts, equivalent API cost, consecutive activity days, and each agent's share for inclusion in a README or documentation.

• Displaying coding-agent usage in a README
• Sharing an activity summary across multiple agents
• Showing token counts and equivalent API cost

Write and review Java services using conventions for naming, immutable data, exception handling, project structure, and tests. Detects Spring Boot or Quarkus from build files and applies the relevant framework rules.

• Reviewing Java code for consistent conventions
• Checking Optional and stream usage
• Organizing packages and project structure

Guide coding, reviews, and refactoring with explicit assumptions, simple implementations, focused changes, and testable goals. These behavioral guidelines help an AI agent avoid unnecessary complexity and verify that its changes meet the request.

• Clarifying assumptions before implementation
• Keeping bug fixes focused
• Reviewing code for unnecessary complexity

Guide coding tasks toward the smallest complete change by reusing existing code, platform features, and installed dependencies. Address affected callers and necessary checks, then explain skipped work, unchecked areas, and risks in a brief reply.

• Build a minimal version that meets the request
• Fix a root cause in shared code
• Refactor while limiting unnecessary additions

Guide coding tasks toward the smallest complete change by checking affected code, reusing existing features, and avoiding unnecessary additions. It also calls for relevant checks and concise explanations of what changed, what remains unchecked, and any risks.

• Build the smallest version that meets a request
• Fix a shared bug across affected callers
• Avoid unnecessary dependencies and abstractions

Review an implementation plan before coding, checking system structure, data flow, diagrams, edge cases, test coverage, and performance. Discuss issues interactively with concrete recommendations to settle technical decisions before implementation begins.

• Reviewing an implementation plan before coding
• Checking system structure and data flow
• Reviewing edge cases and test coverage

Set up gbrain so a coding agent can call it. Install its command-line tool, initialize a local PGLite or Supabase brain, register MCP, the agent connection mechanism, and record trust policies for each remote.

• Setting up gbrain on a machine for the first time
• Initializing a local PGLite brain
• Configuring a Supabase brain

Turn an unclear feature idea into a structured specification with goals, boundaries, and testable success criteria. Split broader requests into independent modules when needed, then move through planning, task breakdown, and implementation with human review.

• Clarify an idea before coding
• Draft requirements with testable completion criteria
• Split a broad request into independent modules

Organize the information an AI coding agent needs for each task. This skill guides project rules, relevant file selection, conversation summaries, and session handoffs so the agent can follow existing conventions and retain key decisions.

• Set up project rules for AI coding agents
• Select relevant files before starting a task
• Trim stale information from a long conversation

Generate website design references as separate horizontal images for each section. Keep a consistent palette while varying layouts, spacing, imagery, and calls to action, giving developers or coding agents concrete visual references for implementation.

• Create section-by-section landing page design references
• Explore different hero layouts
• Share visual references with developers

Turn a feature request into a minimal implementation plan before coding. Identify which files need changes, explain why, define the smallest required interface, and flag broader effects or expanded permissions for the implementer.

• Plan a feature before coding
• Identify files affected by a change
• Review scope and permission changes

Work with AI as a coding partner, choosing who writes code and who guides the work. Supports role switching, test generation, ongoing code review, debugging, and step-by-step explanations for learning.

• Write code with ongoing AI review
• Direct AI implementation while reviewing its output
• Develop features by writing tests first

Keep coding changes small by questioning unnecessary work and reusing existing code, standard libraries, and native platform features. Apply this approach to implementation, bug fixes, reviews, and dependency choices while preserving validation, security, and accessibility.

• Reduce unnecessary code and dependencies
• Fix shared root causes of bugs
• Review code for excessive abstraction

Coordinate named agent teams on Grok Build using Ruflo MCP tools or a command-line message bus. Organize design, coding, testing, and review into ordered steps, with team-specific messages, status tracking, and shutdown.

• Coordinating design, coding, testing, and review
• Handing work between named agents on Grok Build
• Tracking messages and progress across multiple teams

Choose coding challenges matched to a learner’s level and goals within Flow Nexus. Validate submissions, review solution quality and efficiency, and track progress through rankings, achievements, badges, and challenge completion rewards.

• Finding coding challenges suited to a learner’s level
• Reviewing submitted solutions and improvement opportunities
• Tracking rankings, badges, and learning progress

Review GitHub pull requests with specialized AI agents that check security, performance, architecture, and coding conventions. Generate actionable comments with suggested fixes, and connect review findings to automated approval or change requests.

• Reviewing authentication changes for security issues
• Checking database changes for performance regressions
• Reviewing UI accessibility and coding conventions

Set up and operate Ruflo to coordinate multiple AI coding agents. This skill guides project initialization, shared memory, task assignment, health checks, and plugin discovery for work that needs coordinated or parallel execution.

• Initializing a multi-agent development environment
• Coordinating parallel work across agents
• Keeping searchable memory across sessions

Work with AI as a coding partner, choosing who writes code and who guides the work. Supports role switching, test generation, ongoing code review, debugging, and step-by-step explanations for learning.

• Write code with ongoing AI review
• Direct AI implementation while reviewing its output
• Develop features by writing tests first