BC asc-build-lifecycle
Track build processing, find latest builds, and clean up old builds with asc. Use when managing build retention or waiting on processing.
This skill promises to track build processing in App Store Connect, find latest builds, and clean up old ones using the asc utility. Useful if you need to manage retention or wait for a build to finish processing.
The file contains 446 tokens of code with no critical errors. Quality score sits at 80, but process score dropped to 51—looks like the instructions are incomplete or commands lack examples. No model runs or sandbox tests. Supports nearly all platforms including Claude and Cursor.
Worth installing if you already have asc-cli set up and can read the docs yourself. Risky for newcomers to App Store Connect.
Track build processing, find latest builds, and clean up old builds with asc.
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. 4 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 446 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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 137: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 20 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.