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 skills add Yuanpeng-Li/gradescope-mcp --skill gradescope-assisted-gradinggit clone --depth 1 https://github.com/Yuanpeng-Li/gradescope-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/yuanpeng-li/gradescope-mcp/gradescope-assisted-grading)<a href="https://agentmods.dev/skills/yuanpeng-li/gradescope-mcp/gradescope-assisted-grading"><img src="https://agentmods.dev/badge/skills/yuanpeng-li/gradescope-mcp/gradescope-assisted-grading/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/yuanpeng-li/gradescope-mcp/gradescope-assisted-grading"><img src="https://agentmods.dev/badge/skills/yuanpeng-li/gradescope-mcp/gradescope-assisted-grading.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 | $0.00054 | $0.06387 |
| Opus 5 | $0.00027 | $0.03194 |
| Sonnet 5 | $0.00011 | $0.01277 |
| Haiku 4.5 | $0.00005 | $0.00639 |
Grade A, and why
gradescope-assisted-grading 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 11d 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 — 610 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gradescope Assisted Grading
Use this skill when grading through the Gradescope MCP server.
The agent is not a silent auto-grader. Its job is to:
- interview the user first
- establish a grading contract
- gather the right Gradescope context
- propose grades or rubric changes
- wait for explicit approval before any write
Core Behavior
- Match the user's language.
- Start with questions unless the user has already provided enough detail to grade safely.
- Ask only the minimum questions needed to unblock the next decision, usually 2-5 at a time.
- After each intake round, summarize the current grading contract and call out anything still missing.
- Preview first. Every write-capable tool must be called once with
confirm_write=Falsebefore anyconfirm_write=True. - Approval before execution. Never post grades or mutate the rubric without explicit approval of that exact action.
- Read before grading. Never grade without reading the student's actual work or a clearly representative answer-group sample.
- Skip ambiguity. If the grade is not precise and defensible, stop and ask or flag for human review.
- Preserve user authority. User-provided answer keys, grading notes, and rubric guidance override inferred answers.
- Prefer structured output. When a tool supports
output_format, preferoutput_format="json"for planning. - Default to preserving existing grades. If a submission already appears graded, skip it unless the user explicitly asks for audit, regrade, or overwrite behavior.
- Default to no submission-specific comment. Only write
commentwhen the user wants comments, a one-offpoint_adjustmentneeds explanation, or a review handoff note is necessary. - Do not confuse "leave unchanged" with "clear". In
tool_apply_grade,rubric_item_ids=Nonemeans keep current rubric state, whilerubric_item_ids=[]means clear all rubric items. - In
tool_grade_answer_group, always pass explicitrubric_item_ids. Never rely on inherited rubric state. - Unless the question clearly indicates otherwise, think in deduction mode first: start from full credit and identify mistakes. Then verify the actual
scoring_typebefore any write. - Rubric weights are always positive numbers. Gradescope's
scoring_typedetermines whether they add or deduct.
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.
- 11d ago First seen · 610 lines · 54 tokens per session scan A caa69c134491
gradescope-assisted-grading is a skill published in the GitHub repository Yuanpeng-Li/gradescope-mcp (7 stars, last pushed 4mo ago), licensed MIT. It adds 54 tokens to every session and 6,387 once invoked, about $0.0003 per session on Opus 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.
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