BC language-coach
Multi-language writing coach for grammar, word choice, collocations, and idiom errors. Supports English, Chinese, Spanish, French, and Japanese. Activated via //en, //cn, //es, //fr, //ja slash commands.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
The same skill appears in 1 more place: ClawHub
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "permissions" - note
frontmatter-keyunknown frontmatter key "dataPolicy" - note
frontmatter-keyunknown frontmatter key "command-dispatch" - note
frontmatter-keyunknown frontmatter key "command-tool" - note
frontmatter-keyunknown frontmatter key "command-arg-mode"
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
- 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
- 40Consistency. Frontmatter name (language-coach) differs from the folder (english-check)
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. No external tools needed
- 100Steps. 84 steps
- 100Execution cost. Instruction body is 1707 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
- +1No license
- +2Single-language instructions
- +3Description length 203: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 84 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.
External checks
ClawHub: clean
This is a straightforward language-correction skill with no executable code, permissions, credential access, network behavior, or persistence.
LLM: benign (high) · VirusTotal: · 29 May 2026