SKILLEMALL.ai
GitHub ↗ RU Sign in

BA lead-magnet-design

Designing lead magnets that earn the email. The discipline of building gated content (ebooks, templates, checklists, swipe files, mini-courses, free tools) that delivers genuine standalone value while qualifying the lead and warming them for what comes next. Honest about thin-bait (overpromises, underdelivers), kitchen-sink-resource (everything, helps with nothing), and earned-value-magnet (delivers standalone value while qualifying the lead). Triggers on lead magnet, gated content, opt-in offer, ebook, checklist, template, swipe file, mini-course, free tool, content upgrade, freebie, opt-in, list-building offer. Also triggers when an audience is being asked for an email and the offer attached to that ask needs design discipline, when previous lead magnets converted but did not produce qualified leads, or when a lead magnet is being scoped for the first time.

rampstackco/claude-skills Agent Skills author: rampstackco 11 files body ≈ 5 960 tokens Open the sourcegithub.com↗ analyzed 8 h ago

Designing lead magnets that earn the email.

As a process A 82/100 · Runs to the end — weak spots: running it twice, progress reporting

GeneratorMarketingLearningtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
A
82/100
Runs to the end
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
60
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: claude-skills, claude-skills

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5960 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 82/100

  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 10 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5960 tokens
  • 85Steps. 62 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • low 14 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)
  • +3Description length 871: 120–800 characters recommended
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 62 items
  • +3Output format is stated explicitly
  • +4Reference files are cited in the instructions (9 of 9)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.