TenureAI
Official@tenureai
Offers structured governance and execution frameworks for managing complex research experiments, evidence verification, and academic documentation.
Agent Skills by TenureAI
Showing 9 vetted skills indexed across 1 GitHub repositories.
TenureAIhuman-checkpoint
Escalate critical AI R&D decisions to human operators for approval.
TenureAIresearch-workflow
Orchestrate mode-aware, evidence-driven AI R&D workflows from intake to completion.
TenureAIrun-governor
Manage AI research run execution policies, interaction modes, and safety allowances.
TenureAIdeep-research
Conduct auditable deep research with staged searches, evidence verification, and traceable citations.
TenureAIresearch-plan
Generate execution-ready research plans with experiments and expected outcomes.
TenureAImemory-manager
Retrieve and write back AI R&D memory across working, episode, procedure, insight, and persona types.
TenureAIexperiment-execution
Execute AI/ML experiments locally or remotely with explicit environment and runtime controls.
TenureAIproject-context
Initialize and maintain per-project private runtime context for AI R&D tasks.
TenureAIpaper-writing
Draft and revise academic paper sections with evidence-first writing and LaTeX output.
Frequently Asked Questions About TenureAI
FAQPage SchemaWhat specific research tasks does TenureAI enable?▼
TenureAI enables structured research planning, staged evidence verification, and the execution of controlled experiments. It supports the entire lifecycle from initial project context initialization and memory management to the final drafting of academic papers using LaTeX output.
Which personas benefit from using these research capabilities?▼
These capabilities are designed for research scientists, machine learning engineers, and academic investigators who require auditable, evidence-driven environments. It is specifically suited for teams needing to maintain strict governance over experimental parameters and documentation standards.
What are the prerequisites for deploying these research management functions?▼
Deployment requires an environment capable of supporting local or remote experimental execution and persistent memory storage. Users must configure project-specific runtime contexts and define safety policies within the governor to manage interaction modes and decision-making thresholds.