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AC xcode-cache-cleaner

Scan and clean build caches for iOS/macOS developers — with first-class Xcode support (global DerivedData, iOS/watchOS/tvOS/macOS DeviceSupport, Caches/com.apple.dt.Xcode, CoreSimulator unavailable devices) plus per-project cleanup (SPM .build, Pods, Carthage, xcresult, and generic node_modules/Gradle/Rust target/Python __pycache__). Use when user asks to clean Xcode caches, reclaim disk from Xcode, remove DerivedData, free space on a Mac dev machine, or clear build artifacts in a project folder. Triggers on phrases like "clean Xcode", "清理 Xcode 缓存", "remove DerivedData", "free disk space", "why is this folder so big", "clear build files".

ClawHub Agent Skills author: SymbolStar v0.1.0 MIT-0 4 files · 2 scripts body ≈ 802 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 4. 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 55/100

    • 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. 9 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 13 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 802 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

    • +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
    • +5Description quotes 6 example trigger phrases
    • +3Description length 647: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (2 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    This Xcode cache cleaner is purpose-aligned, but it needs review because its local deletion scripts have unsafe edge cases and weaker safeguards than the documentation implies.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026