这个仓库中的 skills
Use this skill when preparing a ForgeLM release. Walks through version bump, changelog finalization, tag, publish, and post-release sync per docs/standards/release.md. Triggered by requests like "cut v0.4.0", "release ForgeLM", "prepare the next version".
Use this skill when editing any docs/reference/* or other bilingual documentation pairs in ForgeLM. Keeps the .md and -tr.md mirrors structurally aligned per docs/standards/localization.md. Triggered by requests like "update the configuration docs", "add a section about X to the usage guide", "translate the new feature docs".
Use this skill when adding a new YAML configuration field to ForgeLM. Handles Pydantic model update, cross-field validation, config_template.yaml sync, bilingual docs, and tests. Triggered by requests like "add a new config option for X", "expose Y as a YAML field", "make Z configurable".
Use this skill when writing new tests for ForgeLM. Applies conventions from docs/standards/testing.md — fixture factory patterns, mock boundaries, no-GPU-no-network discipline, coverage floor. Triggered by requests like "add a test for X", "cover function Y", "write a test that triggers error Z".
Use this skill when adding a substantial end-to-end feature to ForgeLM's training pipeline — a new alignment method, a new evaluation gate, a new quantization backend, a new distributed scheme. Differs from add-config-field by touching multiple modules and requiring integration tests. Triggered by requests like "add support for algorithm X", "integrate library Y for training", "implement feature from roadmap Phase N".
Use this skill when reviewing a pull request to ForgeLM, whether your own (self-review before requesting review) or someone else's. Applies the code-review standard, catches anti-patterns, and produces actionable feedback. Triggered by requests like "review this PR", "check if PR
Use this skill when reviewing a pull request to ForgeLM, whether your own (self-review before requesting review) or someone else's. Applies the code-review standard, catches anti-patterns, and produces actionable feedback. Triggered by requests like "review this PR", "check if PR