BC translate-explanation
Translate existing code-explanation docs into another language, Chinese by default. Translates a whole `CODE_EXPLANATION/` folder into a parallel `CODE_EXPLANATION_<LANG>` copy (e.g. `CODE_EXPLANATION_ZH`), or a single file — either bilingually in place (English + translation interleaved) for small files or as a `<name>_<lang>.<ext>` sidecar copy for larger ones. Keeps filenames, code, identifiers, file paths, and technical terms in English — only the explanatory prose is translated. Use this skill whenever the user wants to translate, localize, or produce a non-English version of code-explanation / codebase documentation: phrases like "translate-explanation", "translate the code explanation", "translate the CODE_EXPLANATION folder", "localize these docs to Chinese", "make a Chinese version of the explanation", or naming any target language for the explanation files. Default to Chinese (ZH) when no language is specified.
The skill promises to translate documentation from a CODE_EXPLANATION folder into another language (Chinese by default), creating a parallel copy like CODE_EXPLANATION_ZH or embedding the translation inline. Code, identifiers, and paths stay in English — only explanatory prose gets translated.
The file contains 2095 tokens, structure is clean, no critical errors. Grade B and quality score 86 suggest a working tool without obvious gaps. It runs on all major platforms from Claude to DeepSeek. The catch: process_score 53 hints the translation logic may need tuning for specific languages, and there's no data from actual text runs.
Install if you're localizing technical docs and can verify output in your target language. For one-off translation it works; for ongoing multi-language support, test first.
Translate existing code-explanation docs into another language, Chinese by default.
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
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 53/100
- 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
- 30Running it twice. 5 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 70When it triggers. States when to use, but not when not to
- 85Steps. 22 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2095 tokens
- low The response is described with custom markup (13 tags): a typed call is more reliable
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 934: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +4Structure: 12 headings
- +3Step-by-step instructions: 22 items
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.