SKILLEMALL.ai
GitHub ↗ RU Sign in

CC pr-review

Review Nemotron pull requests for code correctness, instructions that match the implementation, first-user setup, and reproducible notebooks and cookbooks.

The skillemall take

This skill reviews pull requests in the Nemotron repository for code correctness, instruction-implementation alignment, first-user setup, and reproducible examples. In practice, it analyzes a single file without scripts; quality score sits at 68 out of 100. No critical errors found, but no serious issues either: medium and low findings came up empty.

No real model runs or sandbox testing. The skill supports a broad range of platforms from Claude to DeepSeek. Worth installing if you need basic PR automation—it handles routine checks fine, but don't rely on it for complex bugs.

NVIDIA-NeMo/Nemotron Agent Skills author: NVIDIA-NeMo 1 file body ≈ 1 486 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Review Nemotron pull requests for code correctness, instructions that match the implementation, first-user setup, and reproducible notebooks and cookbooks.

As a process C 63/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
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.
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 · 0

✓ No critical or high findings

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")

Process rating: all ten parameters 63/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 11 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1486 tokens

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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 155: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 11 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.