CB trtllm-test-script-builder
Build Slurm scripts or Docker commands for TensorRT-LLM workloads. Resolves all parameters (docker image, mounts, parallelism, MPI mode), generates the complete script from Category templates, and writes both the script and a job_spec.json manifest to the work directory.
Generates Slurm scripts and Docker commands for TensorRT-LLM workloads by filling in parameters (image, mounts, parallelism, MPI mode) and writing output with a job_spec.json manifest. You get a ready-to-run script from category templates.
Scores sit at C grade: quality 70 (acceptable for a generator), process 65 (logic works), safety 100 (no vulnerabilities). No critical errors, linter passes. Four files, two scripts—bare minimum for the job. No model runs or sandbox testing happened.
Use it if you need to spin up a distributed TensorRT-LLM config fast without hand-editing parameters. Don't expect magic—it's a template converter, not an optimizer. Worth installing.
Build Slurm scripts or Docker commands for TensorRT-LLM workloads.
As a process B 65/100 · Nearly there — weak spots: result and completion
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 7239 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 65/100
- 0Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 7239 tokens
- 85Steps. 71 steps, 1 vague phrases
- 100Failures and branches. 7 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (54 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 271: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 71 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.