CC shelbys-speech
把「谢尔比的记忆」档案库（含完整记忆系统：记忆库+重要md+日志+项目档案）按主题识别，一篇篇生成博士水平、SCI 格式的技术论文（中英文双语、自动检查修复 Word、问答式修改、确认后输出 PDF）。可识别多个主题→多篇论文，每篇先询问用户。选题明确后开放完整记忆系统访问权限（只读），素材更全。作者默认<用户名> & 谢尔比，可随时按用户要求修改。用户说写论文/技术论文/博士论文/SCI/投稿/演讲/分享/总结成文时使用。关键词：论文、SCI、博士论文、投稿、英文论文、PDF、演讲、分享、提炼总结、多主题、shelbys speech、完整记忆。
把「谢尔比的记忆」档案库（含完整记忆系统：记忆库+重要md+日志+项目档案）按主题识别，一篇篇生成博士水平、SCI 格式的技术论文（中英文双语、自动检查修复 Word、问答式修改、确认后输出 PDF）。可识别多个主题→多篇论文，每篇先询问用户。选题明确后开放完整记忆系统访问权限（只读），素材更全。作者默认<用户名>…
As a process C 53/100 · Has gaps — 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: 1. 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") - note
frontmatter-keyunknown frontmatter key "agent_created"
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. 23 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 754 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
- +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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 277: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 23 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.