AD sensitive-disposal
This skill handles sensitive content remediation after scanning. Supports two disposal methods: (1) Keyword masking/redaction with customizable granularity (partial replacement, keyword full replacement, regex middle replacement), (2) File encryption with password protection. Notifications can be sent via Feishu and WeChat. Use after sensitive-content-scanner skill. Free to use.
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: This skill handles sensitive content remediation after scanning. S… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 49/100
- 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
- 40Consistency. Frontmatter name (sensitive-disposal) differs from the folder (sensitive-content-disposal)
- 100Tools and files. No external tools needed
- 100Steps. 29 steps
- 100Execution cost. Instruction body is 434 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
- +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
- -234 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 381: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 29 items
- +4Has examples (1 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.