AD nsap-neural-sparse-processing
Neural Sparse Asynchronous Processing (NSAP): Apply brain-like sparse coding and asynchronous module activation for energy-efficient AI architecture. 神经稀疏异步处理架构：模拟人脑稀疏编码与异步模块激活，实现高效 AI 计算。 Use when asked to: optimize AI architecture, simulate neural modularity, reduce compute cost, design efficient multi-task systems, or understand brain-inspired computing. Covers modular decomposition, sparse activation, async execution, and dynamic resource allocation.
As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 48/100
- 0Result and completion. Does not say what the result is
- 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. 1 mutating operations with no state check
- 85Steps. 17 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1203 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
- -219 emoji in the instructions: noise for the model
- -31 of 5 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 459: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (9 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.