BD scientific-meeting
基于一堂「科学开会」方法论 + 腾讯会议 tmeet CLI + 飞书多维表格，帮你把每场会议的ROI提升5-10倍。 会前用 tmeet 创建结构化会议，会中用十原则护航+实时纪要，会后自动拉取智能纪要+转写生成ROI报告， 并把决议拆成「每人任务清单」写入飞书多维表格中控台——团队在飞书里打勾，下次会前自动拉回看板检查。 独有武器：会议冰山图、科学开会画布、十大原则检查、会议ROI计算器、证据等级标注、任务中控台（飞书多维表格）。
基于一堂「科学开会」方法论 + 腾讯会议 tmeet CLI + 飞书多维表格，帮你把每场会议的ROI提升5-10倍。 会前用 tmeet 创建结构化会议，会中用十原则护航+实时纪要，会后自动拉取智能纪要+转写生成ROI报告，…
As a process D 45/100 · 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 220 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 "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "trigger" - note
frontmatter-keyunknown frontmatter key "dependencies"
Process rating: all ten parameters 45/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 60Tools and files. Uses tools (python, node) that frontmatter does not declare
- 100Steps. 40 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2627 tokens
- 100Progress reporting. Reports progress
- low The response is described with custom markup (7 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
- -222 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 219: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 40 items
- +4Has examples (10 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.