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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Canvas LMS integration — fetch enrolled courses and assignments using API token authentication.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Read-only access to Canvas LMS for listing courses and assignments.
scripts/canvas_api.py — Python CLI for Canvas API calls~/.hermes/.env:CANVAS_API_TOKEN=your_token_here
CANVAS_BASE_URL=https://yourschool.instructure.com
The base URL is whatever appears in your browser when you're logged into Canvas (no trailing slash).
CANVAS="python $HERMES_HOME/skills/productivity/canvas/scripts/canvas_api.py"
# List all active courses
$CANVAS list_courses --enrollment-state active
# List all courses (any state)
$CANVAS list_courses
# List assignments for a specific course
$CANVAS list_assignments 12345
# List assignments ordered by due date
$CANVAS list_assignments 12345 --order-by due_at
list_courses returns:
[{"id": 12345, "name": "Intro to CS", "course_code": "CS101", "workflow_state": "available", "start_at": "...", "end_at": "..."}]
list_assignments returns:
[{"id": 67890, "name": "Homework 1", "due_at": "2025-02-15T23:59:00Z", "points_possible": 100, "submission_types": ["online_upload"], "html_url": "...", "description": "...", "course_id": 12345}]
Note: Assignment descriptions are truncated to 500 characters. The html_url field links to the full assignment page in Canvas.
# List courses
curl -s -H "Authorization: Bearer $CANVAS_API_TOKEN" \
"$CANVAS_BASE_URL/api/v1/courses?enrollment_state=active&per_page=10"
# List assignments for a course
curl -s -H "Authorization: Bearer $CANVAS_API_TOKEN" \
"$CANVAS_BASE_URL/api/v1/courses/COURSE_ID/assignments?per_page=10&order_by=due_at"
Canvas uses Link headers for pagination. The Python script handles pagination automatically.
$CANVAS list_courses — if it fails with 401, guide the user through setupX-Rate-Limit-Remaining header if hitting limits| Problem | Fix |
|---|---|
| 401 Unauthorized | Token invalid or expired — regenerate in Canvas Settings |
| 403 Forbidden | Token lacks permission for this course |
| Empty course list | Try --enrollment-state active or omit the flag to see all states |
| Wrong institution | Verify CANVAS_BASE_URL matches the URL in your browser |
| Timeout errors | Check network connectivity to your Canvas instance |
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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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