菜单
Evidence-first audit of dissertation automations, hooks, scheduled checks, MCP/connectors, browser/LMS monitors, and workflow wrappers to identify what is live, broken, redundant, missing, or unsafe before enabling anything.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | dissertation-automation-audit |
| description | Evidence-first audit of dissertation automations, hooks, scheduled checks, MCP/connectors, browser/LMS monitors, and workflow wrappers to identify what is live, broken, redundant, missing, or unsafe before enabling anything. |
Use this skill when auditing whether automations, hooks, monitors, MCP servers, connectors, scheduled tasks, or wrapper workflows are actually live and useful for the dissertation system.
Adapt ECC automation-audit-ops to this dissertation project. It is read-only by default and should produce an evidence-backed inventory before any fix or new automation is proposed.
| Category | Examples |
|---|---|
| Local hooks | pre/post tool hooks, shell scripts, app-specific triggers |
| Codex automations | heartbeat or cron automations |
| Browser/LMS monitors | page watchers, deadline checks |
| MCP/connectors | installed apps, connected services, exposed tools |
| Document pipeline | Word/PDF rendering helpers, batch converters |
| Knowledge sync | Obsidian, research-wiki, source register synchronization |
For every item, mark:
configuredauthenticatedrecently verifiedstale or brokenmissingnot intentionally enabledkeepmergecutfix nextdo not enableCurrent automation surface:
- item | source | live state | proof
Findings:
- active breakage:
- overlap:
- stale status:
- missing capability:
Recommendation:
- keep:
- merge:
- cut:
- fix next:
Design and review active learning implementation support for university teachers, including classroom activity structures, preparation workflow, student participation, assessment alignment, and AI-agent support points.
Turn dissertation evidence into an AI agent design specification for supporting active learning implementation, including user scenarios, functions, interaction flow, boundaries, failure modes, and design rationale.
Synthesize confirmed co-design, design-elicitation, prototype-feedback, or participant-generated design outputs into requirements, design principles, prototype changes, participant contribution maps, and design decision logs.
Design specifications for dissertation figures such as conceptual frameworks, concept-card/interview process diagrams, AI-agent workflow diagrams, adoption-condition models, and findings maps.
Maintain a lightweight dissertation research wiki with project memory, literature notes, methodology decisions, supervisor feedback, design rationale, and open questions.
Audit an AI agent prototype or design concept for usability, pedagogical fit, teacher feedback, active learning support, evidence quality, and next design iterations.