AD wesley-acquisition-master
Agent becomes a master of client acquisition. Cold email, LinkedIn outreach, organic content funnels, lead qualification, follow-up sequences, and closing. Use when the principal asks to find clients, generate leads, build an audience, send cold emails, prospect on LinkedIn, create a funnel, or grow revenue. Inspired by Oussama Ammar, Yomi Denzel, and 2026 B2B outreach best practices.
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 7530 tokens (recommended < 5000); move details to references/
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
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (wesley-acquisition-master) differs from the folder (agent-acquisition-master)
- 70Execution cost. Instruction body is 7530 tokens
- 100Tools and files. No external tools needed
- 100Steps. 10 steps
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low 20 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 387: enough signal without eating the budget
- +4Structure: 47 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (48 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.