Borrowing it
Nothing to install: this file belongs to sweeden-ttu/canvas-lms-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sweeden-ttu/canvas-lms-mcp/main/.cursor/skills/cs-peer-reviewer-trustworthy-ai/SKILL.mdgit clone --depth 1 https://github.com/sweeden-ttu/canvas-lms-mcpWrote 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/sweeden-ttu/canvas-lms-mcp/cs-peer-reviewer-trustworthy-ai)<a href="https://agentmods.dev/skills/sweeden-ttu/canvas-lms-mcp/cs-peer-reviewer-trustworthy-ai"><img src="https://agentmods.dev/badge/skills/sweeden-ttu/canvas-lms-mcp/cs-peer-reviewer-trustworthy-ai/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sweeden-ttu/canvas-lms-mcp/cs-peer-reviewer-trustworthy-ai"><img src="https://agentmods.dev/badge/skills/sweeden-ttu/canvas-lms-mcp/cs-peer-reviewer-trustworthy-ai.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00068 | $0.00902 |
| Opus 5.5 | $0.00027 | $0.00361 |
| Sonnet 5.5 | $0.00014 | $0.00180 |
| Haiku 4.5 | $0.00007 | $0.00090 |
Grade A, and why
cs-peer-reviewer-trustworthy-ai scanned grade A with 0 findings 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 2mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CS Peer Reviewer + Trustworthy AI Presentation Builder
Provide rigorous, constructive peer review for Computer Science Masters–level work and produce multi-slide Reveal.js presentations that teach Trustworthy AI with concrete, real-world examples.
Inputs to request (if missing)
- The artifact to review (paper/draft/proposal/slides/code) and its goal
- Intended venue or rubric (course, thesis proposal, workshop paper)
- Audience level and time budget (e.g., 15/30/60 minutes)
- Any constraints (must use Reveal.js, must include Mermaid/KaTeX, etc.)
Peer review workflow (Masters level)
-
Summarize first
- 3–6 bullets capturing: problem, method, evaluation, key results/claims.
-
Assess contribution
- What is new? What is the baseline? What is the practical impact?
-
Correctness and clarity
- Identify ambiguous definitions, missing assumptions, or unjustified steps.
- Flag diagrams/figures that don’t match the text.
-
Evaluation rigor
- Are metrics appropriate? Baselines fair? Ablations present?
- Reproducibility: datasets, seeds, hyperparameters, compute budget.
-
Trustworthy AI lens
- Fairness: group/individual fairness assumptions and trade-offs.
- Privacy: threat model (membership inference, reconstruction), mitigation.
- Robustness: distribution shift, adversarial robustness, calibration.
- Security: prompt injection/model extraction/data poisoning where relevant.
- Transparency: interpretability, documentation (model cards/datasheets).
- Accountability: governance, auditing, monitoring, incident response.
-
Actionable recommendations
- Provide a prioritized fix list:
- Must-fix (blocking)
- Should-fix (strongly recommended)
- Nice-to-have
- Provide a prioritized fix list:
Review output format (default)
Return feedback as:
- Summary
- Strengths
- Weaknesses / Risks
- Questions for the author
- Concrete improvements (prioritized)
- Suggested experiments / ablations
- Trustworthy AI checklist results
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.
- 2mo ago First seen · 95 lines · 68 tokens per session scan A 1bab38027b5c
cs-peer-reviewer-trustworthy-ai is a skill published in the GitHub repository sweeden-ttu/canvas-lms-mcp (0 stars, last pushed 8mo ago), licensed MIT. It adds 68 tokens to every session and 902 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
canvas-cli
Manage Canvas LMS (https://www.instructure.com/canvas) from the terminal with the canvas CLI — courses, assignments, submissions and grading, modules, pages, quizzes, discussions, announcements, users, enrollments, sections, files, and analytics. Use this whenever the user wants to list or create assignments, grade…
canvas-admin-roster
Admin skill for walking the Canvas account hierarchy: list accounts and sub-accounts, see courses and users under each, look up which canned reports are available, and enroll or remove users from a specific course — one action at a time. Trigger phrases include "admin roster", "list accounts", "sub accounts", "account…
canvas-peer-review-tracker
Educator skill for tracking peer-review assignments in Canvas. Lists who has been asked to review whom, who has submitted versus who is still pending, and lets you assign new reviewers or send reminder messages — one student at a time. Trigger phrases include "peer reviews", "who hasn't done their peer review", "peer…
canvas-course-requirements
Student plan for one Canvas course, built from the requirement graph instead of guessed from raw module flags. Shows what is genuinely left, what is blocked, and what Canvas cannot yet say either way, with the endpoint and field behind every verdict. Trigger phrases include "what do I still have to do in this course"…
canvas-accessibility-sweep
Educator and learning-designer skill for a pre-launch accessibility and broken-link sweep of a Canvas course. Walks the structural accessibility audit and the link audit over a course's pages, assignments, syllabus, announcements, and (optionally) quizzes, then produces a prioritised remediation list you can work…
canvas-course-pulse
Educator skill for tracking week-over-week course health trends in Canvas. Surfaces assignment performance trends, login activity, engagement gaps, and struggling students across a longer time horizon than a daily check. Trigger phrases include "course pulse", "course health", "course trends", "week-over-week…