BD dca-decision-calculator
左侧定投决策计算器。输入当前指数点位、近一年最高点、持仓成本、汇率，输出是否触发定投、加仓倍数、换汇建议、风险提示。基于以近一年最高点为锚的左侧定投策略。触发词：定投、基金、纳斯达克、指数、加仓、换汇、人民币、汇率、投资、贬买、买入、锁定成本、持仓、成本。
左侧定投决策计算器。输入当前指数点位、近一年最高点、持仓成本、汇率，输出是否触发定投、加仓倍数、换汇建议、风险提示。基于以近一年最高点为锚的左侧定投策略。触发词：定投、基金、纳斯达克、指数、加仓、换汇、人民币、汇率、投资、贬买、买入、锁定成本、持仓、成本。
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
The same skill appears in 1 more place: ClawHub
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 128 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "triggers"
Process rating: all ten parameters 49/100
- 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
- 40Consistency. Frontmatter name (dca-decision-calculator) differs from the folder (nvc-communication-guide)
- 100Tools and files. No external tools needed
- 100Steps. 10 steps
- 100Execution cost. Instruction body is 771 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
- +2Single-language instructions
- +3Description length 128: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (7 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.