SKILLEMALL.ai

BC Pre-Order Planner

Plan and execute pre-order campaigns with waitlist sequencing, early-bird incentive design, and launch communication timing for new product drops.

ClawHub Agent Skills author: LeroyCreates v1.1.0 MIT-0 5 files body ≈ 2 186 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureMarketingInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
C
50/100
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 50/100

  • 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
  • 30Running it twice. 20 mutating operations with no state check
  • 40Consistency. Frontmatter name (Pre-Order Planner) differs from the folder (pre-order-planner)
  • 65Failures and branches. 3 branches
  • 85Steps. 60 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Execution cost. Instruction body is 2186 tokens

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 146: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 60 items
  • +4Has examples (0 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: clean
The artifact appears to be a coherent development/workflow skill package with disclosed commands and no evidence of hidden exfiltration, persistence, or deceptive behavior.
LLM: benign (medium) · VirusTotal: · 8 Jun 2026