SKILLEMALL.ai

AC continuous-field-computation

连续场运算技能,根据任务语义密度自动适配处理深度,通过语义密度感知分析任务复杂度并确定处理深度,简单任务快捷响应完整结果,复杂问题自动整合全网络状态演化输出,支持状态演化追踪和用户可控的透明度,默认隐藏中间过程但用户可要求追溯内部状态演变。6域32种任务。触发词:连续场运算、语义密度、自适应处理、状态演化、透明度控制、meta-skill-system。

ClawHub Agent Skills author: 波动几何 v1.0.0 MIT-0 11 files body ≈ 450 tokens Open the sourceclawhub.ai analyzed 2 d ago

连续场运算技能,根据任务语义密度自动适配处理深度,通过语义密度感知分析任务复杂度并确定处理深度,简单任务快捷响应完整结果,复杂问题自动整合全网络状态演化输出,支持状态演化追踪和用户可控的透明度,默认隐藏中间过程但用户可要求追溯内部状态演变。6域32种任务。触发词:连续场运算、语义密度、自适应处理、状态演化、透明度控制…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

Proceduretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.
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: 11. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 450 tokens
  • 100Running it twice. No mutating operations

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 178: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: clean
This is a documentation-only skill for structuring task complexity and response detail, with no evidence of hidden code, credential access, or data movement.
LLM: benign (high) · VirusTotal: · 20 Jun 2026