SKILLEMALL.ai

AC github-repo-i18n

Internationalize the user-facing language layer of a GitHub repository, including its repository description, repository topics, README files, release notes, changelogs, and other explicitly selected documentation, with English as the default and any user-requested locales as mirrors. Use when a user asks to translate, localize, add language mirrors, or check cross-locale parity for a GitHub repository. Confirm the exact document and metadata scope when it is not explicit, identify existing locales, preserve facts, code, commands, links, images, and structure, and provide local Markdown plus GitHub-style previews. Do not use for product UI or runtime i18n, database content, visual README redesign or assets, public-release suitability or security scans, Issue or PR label taxonomies, Git tags, or Git, PR, release, deployment, or production operations.

ClawHub Agent Skills author: Ward Lu v0.1.1 MIT-0 8 files body ≈ 2 461 tokens Open the sourceclawhub.ai analyzed 2 d ago

Internationalize the user-facing language layer of a GitHub repository, including its repository description, repository topics, README files, release notes…

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureGitHubSoftware developmentWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

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: 8. 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 59/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 11 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 65Failures and branches. 3 branches
    • 100Steps. 26 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2461 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
    • +3Description length 861: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 26 items
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This skill is a scoped repository documentation localization helper with an offline audit script and explicit guardrails around user confirmation and remote changes.
    LLM: benign (high) · VirusTotal: · 10 Sept 2026