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create-inspect-task

9
niznik-devniznik-dev

Create custom inspect-ai evaluation tasks through interacted, guided workflow.

193 days ago

run-experiment

9
niznik-devniznik-dev

Execute the complete experimental workflow - model optimization followed by evaluation - for all runs in a scaffolded experiment. Use after scaffold-experiment to submit jobs to SLURM.

193 days ago

summarize-experiment

9
niznik-devniznik-dev

Create a lightweight summary of experiment results from a completed (fine-tuned and evaluated) experiment. Use after run-experiment to capture key metrics from the experiment in textual form.

193 days ago

create-meeting-agenda

9
niznik-devniznik-dev

Create weekly software meeting agenda in the wiki repo.

193 days ago

design-experiment

9
niznik-devniznik-dev

Plan LLM fine-tuning and evaluation experiments. Use when the user wants to design a new experiment, plan training runs, or create an experiment_summary.yaml file.

193 days ago

scaffold-experiment

9
niznik-devniznik-dev

Set up complete experimental infrastructure for all runs in a designed experiment. Orchestrates parallel generation of fine-tuning configs (via scaffold-torchtune) and evaluation configs (via scaffold-inspect). Use after design-experiment to prepare configs before running experiments.

193 days ago

FAQ

AgentCC is a discovery hub for AI agent capabilities. We index Agent Skills as the primary dataset, and also organize MCP servers and selected AI tools so developers can quickly find, compare, and evaluate what to integrate into their workflows.
An Agent Skill is a reusable capability package for an AI agent. It can define workflows, tool usage patterns, domain knowledge, or execution rules that help the agent perform more reliably in a specific task or environment.
AgentCC is a resource and discovery layer, not a one-click installer. On each skill page, you should review the repository, file tree, install command, and usage notes, then integrate it according to the runtime or client you are using.
No. AgentCC primarily indexes external repositories and metadata. We help you understand what a skill is, where it comes from, and how it may be used, but execution and security decisions still belong to your own runtime environment.
No directory can guarantee absolute safety. AgentCC can help surface repository links, file structures, and metadata, but you should still verify permissions, external dependencies, API usage, and code quality before using a skill in production or on sensitive machines.
Yes. AgentCC is designed to be an evolving resource graph. As the submission and curation workflow matures, contributors will be able to recommend high-quality skills, MCP servers, and AI tools into the directory.
Can't find your answer here? Get in touch

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