BC agent-experience
Use when writing or changing a model-facing tool definition (name, description, parameter schema, or its system-prompt section), and when designing skills, context loading, or multi-step workflows, to make information discoverable and use context efficiently.
The skill helps write model-facing tool definitions: names, descriptions, parameter schemas, system prompts. Claims to make information discoverable and save context when designing skills and multi-step workflows.
Grade B with quality score 80 sits in the middle. One file, no critical errors, but process score 51 signals gaps in structure or completeness. No sandbox or model runs recorded. Supports a wide range of platforms from Claude to DeepSeek.
Worth installing if you write tool definitions regularly and want to speed things up. Without actual model runs, though, it's unclear how well the skill optimizes context—might just be a template.
Use when writing or changing a model-facing tool definition (name, description, parameter schema, or its system-prompt section), and when designing skills…
As a process C 51/100 · 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: 1. 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 51/100
- 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. 2 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 11 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 447 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)
- +4Structure: 1 headings, hard to scan
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 259: enough signal without eating the budget
- +3Step-by-step instructions: 11 items
- +4Has examples (0 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.