Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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。
Delegate coding tasks to Blackbox AI CLI agent. Multi-model agent with built-in judge that runs tasks through multiple LLMs and picks the best result. Requires the blackbox CLI and a Blackbox AI API key.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Delegate coding tasks to Blackbox AI via the Hermes terminal. Blackbox is a multi-model coding agent CLI that dispatches tasks to multiple LLMs (Claude, Codex, Gemini, Blackbox Pro) and uses a judge to select the best implementation.
The CLI is open-source (GPL-3.0, TypeScript, forked from Gemini CLI) and supports interactive sessions, non-interactive one-shots, checkpointing, MCP, and vision model switching.
npm install -g @blackboxai/cligit clone https://github.com/blackboxaicode/cli.git
cd cli && npm install && npm install -g .
blackbox configure and enter your API keypty=true in terminal calls — Blackbox CLI is an interactive terminal appterminal(command="blackbox --prompt 'Add JWT authentication with refresh tokens to the Express API'", workdir="/path/to/project", pty=true)
For quick scratch work:
terminal(command="cd $(mktemp -d) && git init && blackbox --prompt 'Build a REST API for todos with SQLite'", pty=true)
For tasks that take minutes, use background mode so you can monitor progress:
# Start in background with PTY
terminal(command="blackbox --prompt 'Refactor the auth module to use OAuth 2.0'", workdir="~/project", background=true, pty=true)
# Returns session_id
# Monitor progress
process(action="poll", session_id="<id>")
process(action="log", session_id="<id>")
# Send input if Blackbox asks a question
process(action="submit", session_id="<id>", data="yes")
# Kill if needed
process(action="kill", session_id="<id>")
Blackbox CLI has built-in checkpoint support for pausing and resuming tasks:
# After a task completes, Blackbox shows a checkpoint tag
# Resume with a follow-up task:
terminal(command="blackbox --resume-checkpoint 'task-abc123-2026-03-06' --prompt 'Now add rate limiting to the endpoints'", workdir="~/project", pty=true)
During an interactive session, use these commands:
| Command | Effect |
|---|---|
/compress | Shrink conversation history to save tokens |
/clear | Wipe history and start fresh |
/stats | View current token usage |
Ctrl+C | Cancel current operation |
Clone to a temp directory to avoid modifying the working tree:
terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && gh pr checkout 42 && blackbox --prompt 'Review this PR against main. Check for bugs, security issues, and code quality.'", pty=true)
Spawn multiple Blackbox instances for independent tasks:
terminal(command="blackbox --prompt 'Fix the login bug'", workdir="/tmp/issue-1", background=true, pty=true)
terminal(command="blackbox --prompt 'Add unit tests for auth'", workdir="/tmp/issue-2", background=true, pty=true)
# Monitor all
process(action="list")
Blackbox's unique feature is running the same task through multiple models and judging the results. Configure which models to use via blackbox configure — select multiple providers to enable the Chairman/judge workflow where the CLI evaluates outputs from different models and picks the best one.
| Flag | Effect |
|---|---|
--prompt "task" | Non-interactive one-shot execution |
--resume-checkpoint "tag" | Resume from a saved checkpoint |
--yolo | Auto-approve all actions and model switches |
blackbox session | Start interactive chat session |
blackbox configure | Change settings, providers, models |
blackbox info | Display system information |
Blackbox automatically detects images in input and can switch to multimodal analysis. VLM modes:
"once" — Switch model for current query only"session" — Switch for entire session"persist" — Stay on current model (no switch)Control token usage via .blackboxcli/settings.json:
{
"sessionTokenLimit": 32000
}
pty=true — Blackbox CLI is an interactive terminal app and will hang without a PTYworkdir — keep the agent focused on the right directorybackground=true and monitor with process toolpoll/log, don't kill sessions because they're slowblackbox CLI is installed before attempting delegationまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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 skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer 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。
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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。
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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。