Uv Bootstrap Ml Knowledge Authoring

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):

---
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:

# Run from this skill directory (the folder containing this SKILL.md):
python 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:

python bootstrap/scripts/update_skills_mirror.py all

2. Validate the repo is still installable:

npx -y skills add . --list

3. Licensing:

• If you imported/adapted material from a third-party repo, ensure it’s distributable and update THIRD_PARTY_NOTICES.md.

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