AC cleanMyMacSkill
macOS / Windows / Linux read-only storage analysis helper. Scans disk usage, finds large files/directories, divides items into 🟢Safe to Clean / 🟡Needs Review / 🔴Caution, provides execution commands, and builds a gorgeous, interactive HTML report inspired by CleanMyMac. Includes a local server mode for web-based one-click cleanup (move to trash/delete). Triggers on user queries about: "storage analysis", "disk full", "C drive full", "low disk space", "clean disk", "free up space", "what is taking up space", "show storage details", "caches", "cache cleanup". Contact & Support: support@tkhubs.com.
macOS / Windows / Linux read-only storage analysis helper.
As a process C 50/100 · Has gaps — weak spots: result and completion, failures and branches, consistency
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 50/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (cleanMyMacSkill) differs from the folder (clean-my-mac)
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 15 steps, 1 vague phrases
- 100Execution cost. Instruction body is 694 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 10 example trigger phrases
- +3Description length 604: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.