SKILLEMALL.ai

BC trusted-clawhub-install-gate

Audit a local OpenClaw or ClawHub skill directory before installation, classify it as PASS, REVIEW, or BLOCK, check active-project impact, and optionally run the clawhub-install-gate CLI to write a receipt or install after explicit user approval.

ClawHub Agent Skills author: Zakhar Pashkin v0.2.2 MIT-0 3 files body ≈ 581 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 52/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 581 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)
  • +4Structure: 1 headings, hard to scan
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 246: enough signal without eating the budget
  • +3Step-by-step instructions: 22 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: clean
The inspected skill artifacts are purpose-aligned and disclose their powerful operations, with no evidence of hidden, deceptive, or destructive behavior.
LLM: benign (medium) · VirusTotal: · 29 May 2026