BC geoskill-generative-adversarial-rs
GAN (U-Net + PatchGAN) 云去除 / 影像增强，torch+CUDA GPU 训练与推理
GAN (U-Net + PatchGAN) 云去除 / 影像增强，torch+CUDA GPU 训练与推理
As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches
GeneratorSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
How to improve
- 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: 20. 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")
Process rating: all ten parameters 57/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 60Result and completion. Output format stated, no completion criterion
- 85Steps. 18 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1423 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 14 top-level sections: this looks like several domains in one skill
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)
- +3Description length 54: 120–800 characters recommended
- +2Single-language instructions
- +4Structure: 24 headings
- +3Step-by-step instructions: 18 items
- +3Output format is stated explicitly
- +4Has examples (10 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.
External checks
ClawHub: suspicious
The main image-processing script is mostly coherent, but the package includes under-disclosed network geocoding and credential-handling code, including a hardcoded password.
LLM: suspicious (high) · 4 Aug 2026