AgentSkillsCN

uv-bootstrap-ml-knowledge-authoring

在此仓库中创建并维护新的 ML 领域知识技能。当您需要新增 `knowledge/ML/*` 技能、扩展经精心梳理的 ML 分类体系（模型架构、训练、分布式、推理、论文、内核、智能体），或搭建新的技能文件夹时，可运用此技能，并确保命名规范（`uv-*`）、许可协议，以及生成的 `skills/` 镜像保持一致。

SKILL.md
--- frontmatter
name: uv-bootstrap-ml-knowledge-authoring
description: "Create and curate new ML domain knowledge skills in this repo. Use when adding a new `knowledge/ML/*` skill, extending the curated ML taxonomy (model-architecture, training, distributed, serving, paper, kernel, agents), scaffolding a new skill folder, and ensuring naming (`uv-*`), licensing, and the generated `skills/` mirror stay consistent."

Author ML knowledge skills (pkbllm)

Goal

Add a new ML knowledge skill under knowledge/ML/ that matches this repo’s conventions and stays high-signal.

Curated ML taxonomy (fixed)

Put new ML skills under exactly one of these categories:

  • •model-architecture/
  • •training/ (post-training)
  • •distributed/
  • •serving/
  • •paper/
  • •kernel/ (to fill)
  • •agents/ (to fill)

If it doesn’t fit, don’t add it. Extend taxonomy only with explicit repo-level intent.

House style (baked-in)

This repo’s ML skills follow a consistent structure. Do not “study exemplars at runtime”; instead, apply these rules:

1) Frontmatter schema

All ML skills must have name: starting with uv-. Prefer this full schema (extra fields are allowed):

yaml
---
name: uv-<skill-slug>
description: "<what it is>. Use when <trigger phrases and contexts>."
license: MIT
tags: [Short, Tags, Here]
dependencies: [optional, list, of, python, packages]
---

Notes:

  • •license: is optional but recommended (most ML skills here are MIT-derived).
  • •Keep description: focused on when to use triggers; the body is loaded later.

2) Minimum viable sections (copy/paste template)

Use these headings in SKILL.md:

  1. •## Quick start — the shortest command/code snippet that works
  2. •## When to use — 5–12 bullets of trigger phrases
  3. •## Core concepts — 1–2 screens; define key terms precisely
  4. •## Workflows — common tasks as checklists
  5. •## Pitfalls — failure modes + debugging checks
  6. •## References — primary docs/papers/repos (prefer authoritative)

3) High-signal rubric (avoid doc dumps)

Include a new skill only if it meaningfully improves at least one of:

  • •Workflow: a repeatable procedure with decision points and commands
  • •Debuggability: concrete failure modes + how to diagnose
  • •Implementation: minimal runnable snippets + integration points
  • •Comparative clarity: when to choose this over alternatives

Avoid:

  • •Pasting entire upstream docs (low signal, hard to maintain)
  • •Vague “overview only” skills with no commands/checklists
  • •Duplicating an existing skill’s scope (prefer updating it)

Scaffold a new skill folder

Use the scaffold script to create a new skill directory + template SKILL.md:

bash
python bootstrap/ml-knowledge-authoring/scripts/scaffold_ml_knowledge_skill.py \
  --category model-architecture \
  --dir flashinfer \
  --name uv-flashinfer-kernels \
  --description "Kernel-level guidance for FlashInfer attention/kernels. Use when profiling/optimizing FlashInfer, understanding operator paths, or integrating into serving stacks."

The script:

  • •Creates knowledge/ML/<category>/<dir>/
  • •Writes SKILL.md with uv-* naming
  • •Optionally creates references/, scripts/, assets/

Category-specific guidance (distilled)

model-architecture/

Focus on:

  • •the one core idea (e.g., state-space recurrence, routing, draft/verify)
  • •minimal pseudocode or algorithm sketch
  • •what changes at inference time (KV cache? batching? memory shape?)

training/ (post-training)

Focus on:

  • •objective + data requirements
  • •training loop topology (actors/critics/rollouts, preference pairs, etc.)
  • •scaling knobs and common instabilities

distributed/

Focus on:

  • •parallelism axes (DP/TP/PP/CP/EP) and what each breaks
  • •sharding/checkpointing patterns
  • •“first failure” debugging (NCCL hangs, OOMs, divergence)

serving/

Focus on:

  • •request lifecycle (prefill vs decode), batching, cache semantics
  • •deployment shapes (single node vs multi node), observability hooks
  • •latency/throughput tradeoffs and failure modes

paper/

Focus on:

  • •reproducible writing workflow and citation correctness
  • •camera-ready checklists, positioning, and common reviewer objections

kernel/ and agents/ (to fill)

Start with:

  • •a minimal workflow + tooling (profilers, tracing, reproduction harness)
  • •a small glossary + “where to look in code”

References (kept in this skill)

Read bootstrap/ml-knowledge-authoring/references/ml-skill-style-guide.md for a short, copy-ready template and checklists.

Keep the repo consistent

  1. •Regenerate the mirror and README tables:
bash
python bootstrap/scripts/update_skills_mirror.py all
  1. •Validate the repo is still installable:
bash
npx -y skills add . --list
  1. •Licensing:
  • •If you imported/adapted material from a third-party repo, ensure it’s distributable and update THIRD_PARTY_NOTICES.md.