BD lobster-skill-radar
智能扫描用户电脑中的 WorkBuddy Skill 资产， 自动发现散落在各处的 Skill，卡片式展示后支持多选， 无缝对接 Skill矩阵分发助手 一键上传六大平台。 解决"做了 N 个 Skill 却找不到"的核心痛点。 内置隐私授权弹窗，确保用户数据主权。
智能扫描用户电脑中的 WorkBuddy Skill 资产， 自动发现散落在各处的 Skill，卡片式展示后支持多选， 无缝对接 Skill矩阵分发助手 一键上传六大平台。 解决"做了 N 个 Skill 却找不到"的核心痛点。 内置隐私授权弹窗，确保用户数据主权。
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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 contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Dangerous commands
cmd-pipe-to-shellSKILL.md:285Downloads and executes remote code from an unrecognised host (pipe to shell) (quoted — discussed, not commanded)> `curl -fsSL https://skillhub.cn/install/install.sh | bash`
quoted
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 134 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 "display_name" - note
frontmatter-keyunknown frontmatter key "icon" - note
frontmatter-keyunknown frontmatter key "language" - note
frontmatter-keyunknown frontmatter key "entry" - note
frontmatter-keyunknown frontmatter key "requirements"
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
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 100Steps. 19 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2474 tokens
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
- -228 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 133: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 19 items
- +4Has examples (14 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.