Results for “resume-tailoring”

基于 Modular RAG MCP Server 项目生成定制化简历项目经历。结合项目技术亮点与用户业务场景，按简历编写原则输出高质量项目描述（中英文）。Use when user says '写简历', 'resume', '简历', 'write resume', '项目经历', 'project experience', '简历项目', or asks to generate resume content based on this project.

基于 Modular RAG MCP Server 项目生成定制化简历项目经历。结合项目技术亮点与用户业务场景，按简历编写原则输出高质量项目描述（中英文）。Use when user says '写简历', 'resume', '简历', 'write resume', '项目经历', 'project experience', '简历项目', or asks to generate resume content based on this project.

基于 Modular RAG MCP Server 项目生成定制化简历项目经历。结合项目技术亮点与用户业务场景，按简历编写原则输出高质量项目描述（中英文）。Use when user says '写简历', 'resume', '简历', 'write resume', '项目经历', 'project experience', '简历项目', or asks to generate resume content based on this project.

Screens resumes with skill matching, experience evaluation, and bias-free candidate ranking

Convert a resume PDF to clean markdown for LLM parsing or candidate pipelines.

Fine-tune and align language models using reinforcement learning with TRL, including SFT, DPO, PPO, GRPO, and reward model training.

HR & Recruiter IA — Expert en recrutement RH (job descriptions, screening, ATS, onboarding, culture fit)

Train or fine-tune sentence-transformers models for retrieval, similarity, clustering, classification, and reranking, with support for bi-encoders, cross-encoders, and sparse encoders.

Refines task prompts through iterative questioning about scope, deliverables, and constraints, then copies the final markdown to the clipboard.

Expert guidance for implementing GRPO/RL fine-tuning with TRL for reasoning and task-specific model training.

Run Karpathy-style autonomous ML search on a real training repo: choose the right mode (setup, program.md, bounded loop, results interpretation, or constrained-hardware adaptation), preserve the immutable prepare.py / 300-second / val_bpb contract, and route prompt/skill eval work away to LangSmith, Promptfoo, Braintrust, or skill-autoresearch.

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

Use an independently assigned `review-agent` for post-authoring review of changed rd-skills routing registries, fixtures, mappings, or owner conflicts without repair. Skip product work and in-task global rerouting.

`task-agent`/`review-agent`: use when CPU, memory, I/O, database, network, rendering, or cost needs measured bottleneck evidence; skip without a profiling need.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.