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BB create-an-asset

Generate tailored sales assets (landing pages, decks, one-pagers, workflow demos) from your deal context. Describe your prospect, audience, and goal — get a polished, branded asset ready to share with customers.

w95/awesome-claude-corporate-skills Agent Skills author: w95 MIT 3 files body ≈ 6 068 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate tailored sales assets (landing pages, decks, one-pagers, workflow demos) from your deal context.

As a process B 66/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, running it twice

ProcedureMarketingtype 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
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
65
Run on models
none yet
Process rating
B
66/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: RA-Skills

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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: 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")
  • warning body-long SKILL.md body ≈ 6068 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 66/100

  • 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. 9 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 6068 tokens
  • 85Steps. 156 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Result and completion. Output format and completion criterion are stated
  • 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 211: enough signal without eating the budget
  • +4Structure: 65 headings
  • +3Step-by-step instructions: 156 items
  • +3Output format is stated explicitly
  • +4Has examples (25 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.