ajet/copilot/map-verl-config/SKILL.md
Map VERL training configuration to AgentJet configuration. Find VERL config in verl_default.yaml, check for existing mappings in config_auto_convertion_verl.jsonc, add new mappings to ajet_default.yaml and the conversion schema, and optionally add parameters to AgentJetJob.
npx skillsauth add modelscope/agentjet map-verl-configInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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find user requested verl config in codebase/agentjet/ajet/default_config/verl/verl_default.yaml
check codebase/agentjet/ajet/default_config/verl/config_auto_convertion_verl.jsonc, whether a mapping to this config already exists.
if not, add a config under ajet field in codebase/agentjet/ajet/default_config/ajet_default.yaml, and add a mapping in codebase/agentjet/ajet/default_config/verl/config_auto_convertion_verl.jsonc
double check, confirm that default value in ajet_default.yaml is the same as verl config in verl_default.yaml, and the mapping is correct in config_auto_convertion_verl.jsonc
ask user whether to add to AgentJetJob (ajet/copilot/job.py), if the user confirms:
overrides dictajet/default_config/ajet_config_schema.pyajet.trainer_common.optim.lr, need:
AjetOptim dataclass with lr: float = 1e-6AjetTrainerCommon must have optim: AjetOptim = field(default_factory=AjetOptim)getattr() to fail at runtimedata-ai
How `max_env_worker` caps the "Running Episodes" gauge, and how `AgentJetJob` relates to the YAML config.
tools
Convert skills in non-standard formats to the standard Agent Skills `SKILL.md` format. Validates YAML frontmatter (name, description, license, compatibility, metadata, allowed-tools), directory structure (SKILL.md, scripts/, references/, assets/), and best practices. Use when the user asks to normalize, validate, or fix a skill.
devops
Download per-step time-series metric data (reward, entropy, response length, etc.) from a SwanLab cloud run URL as a pandas.DataFrame. Use when the user provides a SwanLab URL and wants to fetch or analyze training curves.
development
Your task is to investigate the chat template of given model, go to its tokenizer config and check whether the following behavior exists: > > Remove history <think> block from the input when apply chat template when converting messages. > This behavior will make RL training slower, if this behavior exists, please change the chat template to forbid such behavior. You must not do this in-place, instead, please create another model. E.g., "/mnt/data_cpfs/xielipeng.xlp/models/Qwen3-8B" -> "/mnt