Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nousresearch/hermes-agent/canvasnpx skills add NousResearch/hermes-agent --skill canvasgit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nousresearch/hermes-agent/canvas)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/canvas"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/canvas.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00012 | $0.00836 |
| Opus 5 | $0.00006 | $0.00418 |
| Sonnet 5 | $0.00002 | $0.00167 |
| Haiku 4.5 | $0.00001 | $0.00084 |
Grade A, and why
canvas scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
## API Reference (curl) Copies of this mod
8 near-identical copies found in the catalogue:
- canvas — 100% identical, 0 lines differ
- canvas — 100% identical, 0 lines differ
- canvas — 100% identical, 0 lines differ
- canvas — 92% identical, 2 lines differ
- canvas — 92% identical, 2 lines differ
- canvas — 86% identical, 5 lines differ
- canvas — 86% identical, 5 lines differ
- canvas — 83% identical, 11 lines differ
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Canvas LMS — Course & Assignment Access
Read-only access to Canvas LMS for listing courses and assignments.
Scripts
scripts/canvas_api.py— Python CLI for Canvas API calls
Setup
- Log in to your Canvas instance in a browser
- Go to Account → Settings (click your profile icon, then Settings)
- Scroll to Approved Integrations and click + New Access Token
- Name the token (e.g., "Hermes Agent"), set an optional expiry, and click Generate Token
- Copy the token and add to
${HERMES_HOME:-~/.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).
Usage
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
Output Format
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.
API Reference (curl)
# 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"
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 99 lines · 12 tokens per session scan A e2335a4e635a
canvas is a skill published in the GitHub repository NousResearch/hermes-agent (241,505 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 836 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
ai-tutor
Use when user asks to explain, break down, or help understand technical concepts (AI, ML, or other technical topics). Makes complex ideas accessible through plain English and narrative structure. Use the provided scripts to transcribe videos.
mem0-tour
Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.
mem0-vercel-ai-sdk
Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also…
mem0-cli
Mem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search, mem0 list, mem0 get, mem0 init, mem0…
mem0-remember
Stores a memory verbatim from user input with appropriate type classification and metadata. Use when the user says remember this, save this, store this, note that, or explicitly asks to record a decision, preference, convention, or learning.
list-projects
Lists all projects with stored memories for the current user, showing memory counts and last activity dates. Use when checking which projects have memories, comparing memory distribution across repos, or finding a specific project scope.