SKILLEMALL.ai

AD last-login-janitor

Use when you have developer debt - stacks of repos, venvs, node_modules, Docker images, caches, model files - and need to know what is safe to delete. Scans last-access and last-commit times, sizes every artifact, flags stale-vs-protected, and produces size-sorted deletion candidates with one-command cleanup.

ClawHub Agent Skills author: voronindenis5 v1.0.1 MIT-0 6 files · 1 script body ≈ 1 603 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

Scans last-access and last-commit times, sizes every artifact, flags stale-vs-protected, and produces size-sorted deletion candidates with one-command cleanup.

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureDockerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
D
49/100
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: 6. 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 49/100

    • 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
    • 30Running it twice. 11 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, git, python) that frontmatter does not declare
    • 100Steps. 28 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1603 tokens
    • 100Progress reporting. Reports progress

    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
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 310: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: suspicious
    The skill is a coherent disk-cleanup scanner, but broad scans can execute Git-controlled helper code inside repositories it inspects, so it needs review before installation.
    LLM: suspicious (high) · VirusTotal: · 28 Sept 2026