BC skill-reviewer-pro
Comprehensive skill review and validation for OpenClaw skills. Performs multi-level review: (1) Format validation, (2) Writing quality assessment (structure, description, examples, scoring), (3) Functional verification (templates match OpenClaw specs), (4) Best practices check, (5) Optimization recommendations, (6) Workflow validation (for workflow tools). Use when auditing skills before publishing, evaluating downloaded skills, or improving existing skills. IMPORTANT: Always respond in the same language as the user's request (auto-adapt to user's language).
As a process C 63/100 · Has gaps — 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: 3. 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: Comprehensive skill review and validation for OpenClaw skills. Per… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 63/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 70Failures and branches. 4 branches
- 100Tools and files. No external tools needed
- 100Steps. 124 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3331 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
- -4Absolute local paths (C:\Users, /home/…): not portable
- -221 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 564: enough signal without eating the budget
- +4Structure: 36 headings
- +3Step-by-step instructions: 124 items
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.