SKILLEMALL.ai
GitHub ↗ RU Sign in

BF nemo-automodel-model-onboarding

Guide for onboarding new model architectures into NeMo AutoModel, including architecture discovery, implementation patterns, registration, and validation.

ClawHub Agent Skills author: NVIDIA 1 file body ≈ 5 491 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Guide for onboarding new model architectures into NeMo AutoModel, including architecture discovery, implementation patterns, registration, and validation.

As a process F 36/100 · Will not run — References files that are not bundled: llm-patterns.md, moe-patterns.md, vlm-patterns.md

AnalyzerWriting and documentsSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
80/100
safety, quality, tests
Safety 60%
99
Quality 40%
51
Run on models
none yet
Process rating
F
36/100
Will not run
References files that are not bundled: llm-patterns.md, moe-patterns.md, vlm-patterns.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token SKILL.md:91
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - `architectures` -- determines the class name and registration key (e.g., `"LlamaForCausalLM"`, `"Qwen…lLM"`, `"Mist…ion"`)
    quoted

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5491 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: llm-patterns.md
  • warning missing-ref reference to a missing file: moe-patterns.md
  • warning missing-ref reference to a missing file: vlm-patterns.md
  • warning missing-ref reference to a missing file: capabilities-and-precision.md
  • warning missing-ref reference to a missing file: ../nemo-automodel-recipe-development/SKILL.md
  • warning missing-ref reference to a missing file: examples/llm_finetune/<name>/
  • warning missing-ref reference to a missing file: examples/vlm_finetune/<name>/

Process rating: all ten parameters 36/100

Will not run. References files that are not bundled: llm-patterns.md, moe-patterns.md, vlm-patterns.md
  • 0Tools and files. 7 referenced file(s) missing: llm-patterns.md, moe-patterns.md, vlm-patterns.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Steps. 94 steps, 6 vague phrases
  • 70Execution cost. Instruction body is 5491 tokens
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • low 12 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (7 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 154: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 94 items
  • +4Has examples (7 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 51.