SKILLEMALL.ai

AA macscreenshot

Capture macOS screenshots for the whole screen, a specific app window, or a precisely targeted desktop window using built-in system tools. Use when the user asks to screenshot the desktop, a frontmost app, a WeChat/Weixin window, a Chrome window, or to locate a macOS window by keyword and capture it. Prefer the built-in screencapture command and CoreGraphics window enumeration via inline Swift. If required capabilities are missing, tell the user exactly which macOS permissions to enable; if Swift tooling is unavailable, tell the user how to install Xcode Command Line Tools manually.

ClawHub Agent Skills author: mallocfeng v1.0.1 MIT-0 4 files · 1 script body ≈ 1 334 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process A 82/100 · Runs to the end — weak spots: inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
A
82/100
Runs to the end
Inputs and preconditions w 11
0
Tools and files w 18
60
Steps w 15
100
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 82/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 100Steps. 37 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1334 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (5 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)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 589: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 37 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: clean
    This skill is a focused macOS screenshot helper that uses local system tools and does not show hidden sharing, credential access, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026