AD forecast-simulation
基于已有数据进行趋势预测、目标缺口分析、What-if 模拟和耗尽/饱和预测。当用户希望了解指标的未来走势、评估目标是否可达、模拟假设场景的影响时，必须使用此 Skill。 触发场景包括但不限于：用户提到"预测""预估""月底能到多少""能不能完成目标""还差多少""如果XX会怎样""提升10%会怎样""库存还能撑多久""按这个趋势""达标需要多少""推演""模拟""What-if""情景分析""按目前的速度""还需要多久""够不够""能不能达成""离目标还有多远""日均需要多少""按目前进度""趋势外推""预计""估算"，或用户对未来走势、目标可行性、假设场景表达了疑问时，都应使用此 Skill。 即使用户没有直接说"预测"，只要其意图是了解"未来会怎样"或"如果改变某个条件结果会怎样"，都应触发此 Skill。例如用户说"这个月能卖到 1000 万吗""如果投放加倍呢""照这样下去年底会怎样"，都应触发。 **触发判定关键**：用户的意图是了解**未来**的事，或模拟**假设**场景。区分方式： - "上月销售额多少" → metric-query（看过去） - "月底销售额能到多少" → forecast-simulation（看未来） - "销售额正常吗" → anomaly-detection（判断现在） - "销售额为什么跌了" → metric-attribution（解释过去） **与相邻 Skill 的区分**： - metric-query / anomaly-detection / metric-attribution 都是看过去和现在 - forecast-simulation（本 Skill）是看未来 **重要：本 Skill 基于历史数据做简单数学推算（趋势拟合、公式模拟），不是机器学习预测模型。推算结果应附带前提假设和不确定性说明。**
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 2. 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 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 36 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2652 tokens
- 100Running it twice. No mutating operations
- low 10 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
- +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
- +5Description quotes 19 example trigger phrases
- +3Description length 796: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 36 items
- +4Has examples (16 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.