awesome-student-ai-skills
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_student_skillsA | |
| route_student_skillB | |
| get_skill_infoA | |
| get_skill_requirementsA | |
| list_school_templatesA | 列出当前内置支持的高校特定排版样式模板。 支持清华、北大、浙大、武大等高校的标准页边距、字号和红头格式。 |
| generate_student_docA | |
| simulate_student_reviewA | |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Each tool has a distinct role: listing templates, listing tracks, routing a query, fetching metadata, fetching data-collection requirements, generating a document, and simulating review. The only near-overlap is get_skill_info vs get_skill_requirements, but the descriptions clearly separate metadata/config from required-field checklists.
All seven tools follow a consistent snake_case verb_noun pattern (list_*, route_*, get_*, generate_*, simulate_*). The naming is predictable and readable throughout.
Seven tools is well-scoped for the domain: discovery, routing, requirements gathering, generation, and review are each covered by one or two tools. No tool feels redundant or missing at this count.
The set covers the core lifecycle from finding a track and gathering requirements to generating a .docx and simulating review. Minor gaps remain around explicitly editing/updating an already generated document or inspecting existing outputs, but agents can work around this by regenerating after review.