Complete methodology for preventing AI hallucinations. Use when accuracy is critical and you need Claude to verify before claiming, cite before asserting, and admit uncertainty instead of guessing.
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Canvas API Connection Skill
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Complete guide to connecting and using Canvas LMS API for Whitecliffe courses.
| Item | Value |
|---|---|
| User ID | 2396 |
| User Name | Mohamed Abdul Latiff (Ariff) |
| Canvas URL | https://learn.mywhitecliffe.com |
| MCP Server | canvas-mcp-sse.ariff.dev |
| Course | ID | Code |
|---|---|---|
| Applied Project | 2366 | IT8107 |
| Cybersecurity Architecture | 2368 | IT8109 |
The Canvas MCP server is already configured and provides 12 tools:
Course Tools:
get_courses - List all enrolled coursesget_modules - Get course modulesAssignment Tools:
get_assignments - List assignments for a courseget_upcoming_assignments - Next 7 days deadlinesGrade Tools:
get_grades - Current grades for a courseget_submissions - Submission detailsDiscussion Tools:
get_discussion_topics - List discussionsget_announcements - Course announcementsQuiz Tools:
get_quizzes - List quizzes# Get all courses
mcp__canvas-mcp__get_courses()
# Get assignments for IT8107
mcp__canvas-mcp__get_assignments(course_id=2366)
# Check grades
mcp__canvas-mcp__get_grades(course_id=2366)
# Get upcoming deadlines
mcp__canvas-mcp__get_upcoming_assignments()
For custom integrations or when MCP is unavailable:
Base URL: https://learn.mywhitecliffe.com/api/v1
Authentication:
# Store token securely (never commit!)
export CANVAS_TOKEN="your-token-here"
# API request
curl -H "Authorization: Bearer $CANVAS_TOKEN" \
"https://learn.mywhitecliffe.com/api/v1/courses"
Common Endpoints:
| Endpoint | Description |
|---|---|
/courses | List courses |
/courses/:id/assignments | Course assignments |
/courses/:id/grades | Course grades |
/courses/:id/modules | Course modules |
/users/self/upcoming_events | Upcoming events |
from canvasapi import Canvas
API_URL = "https://learn.mywhitecliffe.com"
API_KEY = os.environ.get("CANVAS_TOKEN")
canvas = Canvas(API_URL, API_KEY)
user = canvas.get_current_user()
courses = user.get_courses(enrollment_state='active')
Already configured in mcp-config.json:
{
"mcpServers": {
"canvas-mcp": {
"type": "sse",
"url": "https://canvas-mcp-sse.ariff.dev/sse"
}
}
}
macOS Keychain:
security add-generic-password -a "$USER" -s "canvas-api" -w "YOUR_TOKEN"
Environment variable:
# Add to ~/.zshrc
export CANVAS_TOKEN="$(security find-generic-password -a "$USER" -s "canvas-api" -w)"
⚠️ Always verify with MCP tools - Don't assume course/assignment details
⚠️ Token security - Never commit tokens to git
⚠️ Rate limits - Canvas has API rate limits, batch requests when possible
⚠️ Course IDs change - Each semester has new course IDs
"Unauthorized" errors:
MCP not responding:
Missing courses:
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Complete methodology for preventing AI hallucinations. Use when accuracy is critical and you need Claude to verify before claiming, cite before asserting, and admit uncertainty instead of guessing.
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Transform rough ideas into solid designs through structured questioning
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Forces every code-related claim to include file:line citations. No citation means the claim must be verified or removed. Use when building documentation, writing code reviews, or any output that others will rely on.
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Assign confidence scores (0-100) to every claim in a response. Helps users understand which parts are verified facts and which are educated guesses. Use when the user needs to know how much to trust each part of the answer.
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Forces Claude to extract exact quotes from files before making claims about them. Prevents hallucinations by grounding every statement in actual text from the codebase. Use when working with large files or unfamiliar code.
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Verify every claim from multiple independent angles -- code, docs, tests, git history. Use when you need high confidence that something is correct before acting on it.
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