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name: lmops description: "Route LMOps paper-code workflows for prompt optimization, retrieval, adaptation, distillation, experiential learning, RAG, and LLM acceleration." disable-model-invocation: true metadata: disco-role: operating license: MIT

LMOps

Use this repo skill when the user asks about the Microsoft LMOps repository or its paper-code projects for prompt intelligence, in-context learning, LLM adaptation, data selection, distillation/post-training, experiential learning, retrieval-augmented generation, or reference-based acceleration.

This skill is a safe operating map, not a claim that paper-scale GPU training was run during skill creation. It emphasizes routing, command planning, input validation, prerequisites, and troubleshooting before a later Researcher attempts expensive execution.

First routing pass

1. Identify the named LMOps project or task shape.
2. If the user has a local LMOps checkout, optionally run scripts/check_lmops_checkout.py against their checkout to verify which project directories are present. The helper is read-only and does not import repo code.
3. Choose the nearest sub-skill:
   • sub-skills/prompt-optimization/SKILL.md for ProTeGi automatic prompt optimization and Promptist text-to-image prompt rewriting/training planning.
   • sub-skills/example-retrieval/SKILL.md for UPRISE, SE2, LLM Retriever, CED-ICL, Structured Prompting, and Understand ICL.
   • sub-skills/adaptation-and-training/SKILL.md for AdaptLLM, Instruction Pre-Training, Data Selection via Optimal Control, ResLoRA, and Learning Law.
   • sub-skills/distillation-and-post-training/SKILL.md for MiniLLM, DPKD, and Tuna.
   • sub-skills/rl-experiential-learning/SKILL.md for OEL, OPCD, LLM-as-a-Coach, GAD, and OPO.
   • sub-skills/rag-and-acceleration/SKILL.md for CoRAG and LLMA.
4. Read references/project-index.md when the request names a paper, project acronym, or source area but the task family is unclear.
5. Read references/troubleshooting.md before turning a paper workflow into executable commands. Many LMOps workflows require old dependency stacks, large model/data downloads, credentials, Docker/Ray/vLLM services, or multi-GPU hardware.

Root-level facts to preserve

• LMOps is a collection of independent research-code projects, not one installable root Python distribution.
• A single Python environment cannot truthfully validate every subproject. Treat each execution request as a project-specific environment plan.
• Creation-time inspection used static source/API parsing and safe bundled helper checks. End-to-end GPU, model-download, API-scoring, server, and training workflows are documented but not native-executed by this generated skill.
• Do not paste API keys, W&B keys, Hugging Face tokens, OpenAI keys, or private cache paths into commands, logs, config files, or generated plans.
• Do not run shell launchers, Docker setup, Ray clusters, vLLM servers, downloads, or training jobs unless the user explicitly asks and the required environment and budget are present.

Bundled root references and scripts

• references/project-index.md: project/acronym-to-sub-skill map and high-level workflow inventory.
• references/troubleshooting.md: shared install, dependency, credential, hardware, data, service, and staleness troubleshooting.
• references/repo-provenance.md: source snapshot and evidence paths for refresh decisions.
• references/repo-routing-metadata.json: structured import metadata for managed repo-skills-router.
• scripts/check_lmops_checkout.py: read-only checkout structure checker and sub-skill router.

Safe checkout check

When a user provides a checkout, run the bundled helper rather than assuming every paper directory is present:

python scripts/check_lmops_checkout.py --repo-root /path/to/LMOps --json

The helper only checks names and expected files. It does not import source modules, execute scripts, download data, or modify the checkout.

Verification and import status

This runtime skill is staged as a generated repo skill. Verification artifacts are kept outside the runtime tree. The user explicitly requested not to import, so do not run the managed repo-skill importer unless a later user instruction changes that decision after verification.