AD speech-analyzer
口头表达分析与优化：20维度量化分析直播/播客/演讲的表达质量，输出结构化分析报告。Invoke when user asks 直播分析、表达分析、口才分析、演讲评估、口癖检测.
口头表达分析与优化：20维度量化分析直播/播客/演讲的表达质量，输出结构化分析报告。Invoke when user asks 直播分析、表达分析、口才分析、演讲评估、口癖检测.
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
Analyzertype and topics are labelled automatically from the skill text
How to improve
For the model run — optional
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
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 (speech-analyzer) differs from the folder (13-speech-analyzer)
- 100Tools and files. No external tools needed
- 100Steps. 7 steps
- 100Execution cost. Instruction body is 228 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)
- +3Description length 89: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +4Structure: 8 headings
- +3Step-by-step instructions: 7 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.
External checks
ClawHub: clean
This is a straightforward speech-analysis skill with no executable code, though users should treat recordings as sensitive personal data.
LLM: benign (high) · VirusTotal: · 9 Aug 2026