SKILLEMALL.ai

AD file-tree

Show the directory tree structure of a folder. Displays files and subdirectories in a visual tree format. Use when the user wants to see what files are in a folder.

ClawHub Agent Skills author: 목진왕 v1.0.0 MIT-0 4 files · 1 script body ≈ 157 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
D
47/100
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: 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 47/100

    • 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
    • 40Consistency. Frontmatter name (file-tree) differs from the folder (simple-file-tree)
    • 50Steps. 2 steps
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. Tools declared in frontmatter
    • 100Execution cost. Instruction body is 157 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • +1No license
    • +2Single-language instructions
    • +3Description length 164: enough signal without eating the budget
    • +4Structure: 4 headings
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

    External checks

    ClawHub: clean
    This skill only prints a local folder tree and does not show hidden network access, credential use, persistence, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026