CD course-study
跟课程进度逐讲伴读：拿到一讲 PDF 就写顺原讲义顺序的中文伴读文档（每 1–3 页一节、讲透、标考试怎么考与证据级别），并把新知识落进课程文件夹 study/记忆库；追问已入库概念、期末统一总结。用于"给这个 PPT 写辅助文档""把 lecture N 入库""write a companion doc for this lecture"；触发词 $course-study。不代写要提交的作业答卷（同型题只在附录里演练并标明来源）；物理题的费曼式讲解归 $feynman-physics-distiller；「总结今天的记忆」不属本 skill。
跟课程进度逐讲伴读：拿到一讲 PDF 就写顺原讲义顺序的中文伴读文档（每 1–3 页一节、讲透、标考试怎么考与证据级别），并把新知识落进课程文件夹 study/记忆库；追问已入库概念、期末统一总结。用于"给这个 PPT 写辅助文档""把 lecture N 入库""write a companion doc for…
As a process D 38/100 · Unfinished process — weak spots: steps, result and completion, when it triggers
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-agent-memory-dumprules/memory.mdAgent memory / workspace files bundled with the skill (1) — likely a workspace dump with personal data or tokensrules/memory.md
Files scanned: 16. 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 38/100
- 0Steps. Prose only: no discrete steps
- 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
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 805 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
- +4Description does not say when NOT to use the skill (false activations)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 277: enough signal without eating the budget
- +4Structure: 8 headings
- +4Reference files are cited in the instructions (10 of 10)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.