BD spatiotemporal-analysis
时空智能分析法（v2.0 完整版）。纵向时间线 × 横向对照 × 交汇判断，必须先检索后下判断。含双交付模式（摘要→完整版）、当代实证研究不用学术库、引用覆盖率门槛、PDF 输出配方、松哥写作约定、5 子 agent 工程化流水线、数据采集脚本与模板等。触发：「时空智能分析」「时空分析」「spatiotemporal」「深度研究报告」「竞争格局分析」，或用户要求「研究一下 X 与 Y 的关系」这类带时间维度+对照维度的题目。
时空智能分析法（v2.0 完整版）。纵向时间线 × 横向对照 × 交汇判断，必须先检索后下判断。含双交付模式（摘要→完整版）、当代实证研究不用学术库、引用覆盖率门槛、PDF 输出配方、松哥写作约定、5 子 agent…
As a process D 43/100 · 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.
- 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: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 214 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
Process rating: all ten parameters 43/100
- 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 (web) that frontmatter does not declare
- 60Consistency. The Hermes dialect needs category and tags
- 100Steps. 116 steps
- 100Execution cost. Instruction body is 3569 tokens
- 100Running it twice. No mutating operations
- low 20 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 tags): a typed call is more reliable
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
- -236 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 214: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 116 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.