Nick

Hand a scoped coding task to Aider while the coordinating agent reviews changes, runs project checks, and commits the result. Covers task briefs, execution tracking, and follow-up edits, but the executable helper is not bundled.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Delegating a scoped code change to Aider
• Reviewing changes and tests before committing
• Requesting follow-up edits after a review

Turn articles, notes, or scripts into solo podcast MP3s using the paid Beatra service. The referenced package supports a consistent host voice, natural pacing, and pronunciation control, with cost approval and audio review steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning an article into a solo podcast
• Creating a narrated episode from notes
• Producing an MP3 from a finished script

Generate realistic photo-style images with Google AI Studio for social posts and educational materials. Adjust aspect ratios, lighting, settings, and subtle imperfections to create a natural smartphone-photo look suited to each use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Natural lifestyle images for social posts
• Photo-style illustrations for courses and tutorials
• Portrait or landscape images for different platforms

Investigate failed Agent QA runs using recorded steps, logs, and saved evidence. Classify each failure, explain confidence and supporting findings, and identify the likely area to fix and the next action.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a failed or interrupted Agent QA run
• Reviewing saved evidence and execution logs
• Comparing recurring failures across recent runs

Search AI and machine learning jobs through AI Dev Jobs MCP, a connection agents use to access job data. Inspect employers and requirements, match roles to skills and preferences, and review available salary and market statistics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding remote machine learning jobs
• Discovering companies hiring for AI roles
• Matching jobs to skills and work preferences

Compare prompt revisions on a fixed set of test cases to identify regressions, meaning behavior that gets worse after a change. Record model settings, case-level evidence, missing results, costs, and evaluation limits for a reviewable decision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing original and revised prompts
• Checking required JSON fields after prompt changes
• Reviewing tool selection and task completion

Convert human-written CLAUDE.md instructions into a structured label format for AI agents. Use it to review lengthy rules, reduce instruction overhead, and prepare prompts for migration between AI tools while preserving the intended rules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Restructuring a lengthy CLAUDE.md
• Reviewing instruction token usage
• Preparing rules for migration between AI tools

Plan AI application work across six areas: prompt evaluation, context allocation, retrieval-based answer generation, agent security, evaluation pipelines, and product strategy. This overview explains the approaches; the individual workflow packages, test cases, and CI templates are not included.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning prompt evaluation before deployment
• Allocating context capacity and designing retrieval-based answers
• Planning an authorized AI agent security assessment

Plan reviews of AI-generated code before running or merging it. Define language-specific checks, tests in isolated environments, and evidence requirements, while recording coverage limits and gaps that prevent approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning reviews of AI-generated code before merging
• Defining language-specific security checks
• Recording test evidence and unresolved gaps

Create consistent records for evaluating AI agents, including task success, invalid tool calls, token use, and actual costs. Produce CSV tables, database definitions, and JSON validation schemas to support reproducible comparisons and checks for performance declines.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording agent evaluation results in a shared format
• Comparing a candidate agent with a baseline
• Tracking actual cost per solved task

Delegate a clearly scoped coding task to Google Antigravity CLI when explicitly requested. Provides a workflow for preparing instructions, reading the implementer’s report, reviewing changes, rerunning project checks, and committing verified work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Delegate a clearly scoped coding task to Google Antigravity CLI
• Prepare an implementation brief with constraints and checks
• Review delegated changes and rerun project checks

Review GitHub Actions workflows that run AI agents, tracing how external input reaches prompts and tools. Produce findings with evidence, severity, impact, and remediation guidance through static analysis, which examines configuration without running attacks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing AI agent integrations in GitHub Actions
• Tracing issue and PR text into agent prompts
• Reviewing sandbox settings and tool permissions

Find autonomous AI agents, frameworks, and tools through AgentFolio. Compare their capabilities, intended users, integrations, and deployment options to shortlist existing products or gather examples and ideas before designing an agent of your own.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding existing agents before building your own
• Shortlisting agent vendors for evaluation
• Researching competitors and market gaps

Design tools that AI agents can understand and call, covering input schemas, clear descriptions, validation, and error handling. The guidance includes JSON Schema, a format for defining inputs, and MCP, a protocol for connecting agent tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design inputs for a new agent tool
• Improve tool descriptions that guide model choices
• Review validation and error handling

Coordinate software development tasks across specialized agents for requirements, planning, architecture, implementation, review, testing, and deployment. Keep versioned reports, concise progress summaries, and open decisions organized, with user confirmation before moving between stages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assign requirements and planning tasks to specialist agents
• Route existing code directly to a review agent
• Track report versions and project progress

Inventory local skills within a requested scope, rank candidates against a task, and prepare a coordination plan showing their order and data flow. Project assignments help prioritize relevant skills without automatically launching agents or executing skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inventory locally installed skills
• Compare skills suited to a task
• Plan a workflow using several skills

Improve workflows involving multiple AI agents by measuring bottlenecks, adjusting workload distribution, and managing model costs. This design guide helps compare changes against a measured baseline while checking quality, latency, and resource use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding bottlenecks in workflows involving multiple agents
• Reviewing task distribution and parallel execution
• Reducing model costs within quality thresholds

Analyze a codebase to create project-specific AGENTS.md instructions and supporting rule files. Detect the package manager and relevant conventions, preview proposed changes, or update existing guidance when the project’s tools change.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create agent instructions tailored to an existing project
• Generate a minimal AGENTS.md file
• Refresh instructions after changing development tools

Document the tools an AI agent may call, including input rules, caller permissions, execution limits, and retry conditions. Produce a tool register and verification plan for reviewing registrations or MCP routing designs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Defining which tools an agent may call
• Reviewing a proposed tool registration
• Documenting timeout and retry rules

Develop incident response procedures for LLM outages, declining answer quality, spending spikes, and unsafe outputs. Use example monitoring alerts, severity levels, recovery playbooks, and postmortem guidance to organize detection, response, and follow-up work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan fallback procedures for model provider outages
• Investigate declining answer quality after a deployment
• Prepare emergency controls for token spending spikes

Plan security controls for LLM applications and AI agents, covering malicious instructions, data leakage, model tampering, and API access. Includes guidance and code examples for filtering outputs, enforcing permissions, isolating networks, and recording audit events.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing defenses against prompt injection in AI agents
• Filtering personal information from LLM responses
• Designing authentication and rate limits for LLM APIs

Design or review command-line tools that AI agents can call. Use 98 proposed rules to define structured output, input validation, error handling, safety controls, and help information, with three levels for gradual adoption.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design a new command-line tool for AI agents
• Adapt an existing tool for agent use
• Standardize interfaces for automated workflows

Design layered protections for AI agents against malicious instructions, tool misuse, and data leakage. Provides guidance and code examples for input checks, isolated tool execution, permission limits, and audit logging to help review and strengthen agent security.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review defenses against malicious instructions
• Isolate tool execution and restrict permissions
• Assess data leakage risks between tenants

Coordinate multiple Claude Code workers through an existing Kanban board. This guide describes task dispatch, automated checks before AI review, per-task cost tracking, human intervention, and recovery after restarts; the runtime and integrations require separate installation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Coordinating multiple Claude Code workers
• Running automated checks before AI code review
• Monitoring task progress and development costs on a board