SKILLEMALL.ai

BC multi-platform-crosspost

Automatically cross-post blog content to 7+ platforms (LinkedIn, Dev.to, Hashnode, Twitter/X, Reddit, Substack, Pinterest) with tracking, deduplication, and platform-specific formatting. Production-tested pipeline.

ClawHub Agent Skills author: Muhammad H.M. Alvi v1.2.3 4 files body ≈ 1 597 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureGoogle SheetsZapierInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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: 4. 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")

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 9 mutating operations with no state check
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 50 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1597 tokens
  • low 13 top-level sections: this looks like several domains in one skill

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
  • +2Single-language instructions
  • +3Description length 214: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 50 items
  • +4Has examples (3 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
This skill is a coherent blog cross-posting workflow, but it can automatically publish and redistribute content through several external services with weak webhook scoping.
LLM: suspicious (high) · VirusTotal: · 29 May 2026