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AD asc-metadata-sync

Sync, validate, and apply App Store metadata and store assets with asc. Use when updating localizations, keywords, App Clip or preview assets, Asset Library media and placements, or migrating legacy fastlane metadata.

The skillemall take

The skill promises to sync App Store metadata through ASC: localizations, keywords, previews, Asset Library. Two files in the folder, no scripts, 2328 instruction tokens. Checks show grade A: quality 88%, safety 100%, no critical findings. Process score 49% suggests light automation, acceptable for an analyzer.

Runs on all major platforms—Claude, Cursor, Gemini, DeepSeek, others. Files intact, references clean. For anyone regularly updating App Store metadata or migrating from fastlane, it cuts routine work. Install it: quality above average, no issues found.

rorkai/app-store-connect-cli-skills Agent Skills author: rorkai 2 files body ≈ 2 328 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Sync, validate, and apply App Store metadata and store assets with asc.

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerAI and agentsData and analyticsMedia and videotype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 4, 155): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 49/100

    • 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
    • 30Running it twice. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2328 tokens
    • low The response is described with custom markup (3 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

    • +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 217: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (17 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.