SKILLEMALL.ai

AC skill-guard

Stage, scan, and install a ClawHub skill when the user requests a guarded ClawHub installation. Blocks installation on security findings or incomplete scans.

ClawHub Agent Skills author: hola v1.0.3 MIT-0 6 files · 4 scripts body ≈ 1 239 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

Stage, scan, and install a ClawHub skill when the user requests a guarded ClawHub installation.

As a process C 64/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

AnalyzerSoftware developmentData and analyticsAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
64/100
Has gaps
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
40
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 64/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python, node) 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
    • 75Steps. 3 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1239 tokens
    • low The response is described with custom markup (3 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 157: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 3 items
    • +4Has examples (1 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    In the sandbox The scripts kept to themselves

    The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.

    Запущено 2 скрипта; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.

    Из них 1 не дошёл до работы, и об их поведении мы ничего не узнали.

    scripts/safe_install.pyне запустился: usage: safe_install.py [-h] [--version VERSION] [--force] slug
    scripts/safe-install.shзавершился с кодом 1

    21 Oct 2026

    External checks

    ClawHub: clean
    This skill is a guarded installer that openly downloads, scans, and installs one requested ClawHub skill, with no evidence of hidden execution or exfiltration beyond the disclosed Snyk scan upload.
    LLM: benign (high) · VirusTotal: · 16 Sept 2026