Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Triage GitHub issues by applying type, effort, priority, and area labels. Runs in an isolated context to avoid polluting the main conversation with issue details. Delegates to a specialized triage agent with label validation hooks.
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安装前可以查看交给 AI 代理的指令内容。
Triage issues for this repository by applying appropriate labels.
gh repo view --json nameWithOwner -q .nameWithOwner 2>/dev/null || echo "unknown"gh issue list --state open --json number --jq 'length' 2>/dev/null || echo "?"gh label list --json name -q '.[].name' 2>/dev/null | head -20 || echo "none found"Determine which issues to triage based on the arguments: $ARGUMENTS
| Argument | Behavior |
|---|---|
| (empty) | Only unlabeled issues (default) |
unlabeled | Only issues without any labels |
all | All open issues |
N | Specific issue (e.g., 67) |
N-M | Range of issues inclusive (e.g., 60-67) |
Fetch available labels — run gh label list --json name,description to understand
the label taxonomy. Labels are organized into type, effort, priority, and area categories.
Fetch target issues — based on the scope above. For each issue, fetch the full body:
gh issue view {number} --json number,title,body,labels
For each issue:
Glob, Grep, Read) to understand the affected codegh issue edit {number} --add-label "type,effort/level,P#,area.domain"
Output a triage summary:
## Triage Summary
| Issue | Title | Labels Applied | Reasoning |
|-------|-------|----------------|-----------|
| #67 | ... | bug, effort/low, P1, core.config | ... |
**Totals:**
- Issues triaged: X
- Already labeled (skipped): Y
- By priority: P0(n), P1(n), P2(n), P3(n)
## Relationships Discovered
| Issues | Relationship | Notes |
|--------|--------------|-------|
| #61, #62 | Related | Both involve config/logging UX |
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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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".
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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.
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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.
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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.
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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.
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