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CD marketing-demand-acquisition

Multi-channel demand generation, paid media optimization, SEO strategy, and partnership programs for Series A+ startups. Includes CAC calculator, channel playbooks, HubSpot integration, and international expansion tactics. Use when planning demand generation campaigns, optimizing paid media, building SEO strategies, establishing partnerships, or when user mentions demand gen, paid ads, LinkedIn ads, Google ads, CAC, acquisition, lead generation, or pipeline generation.

davila7/claude-code-templates Agent Skills author: davila7 MIT 2 files · 1 script body ≈ 8 126 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Multi-channel demand generation, paid media optimization, SEO strategy, and partnership programs for Series A+ startups.

As a process D 38/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorHubSpotMarketingSales and CRMtype 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
C
89/100
safety, quality, tests
Safety 60%
98
Quality 40%
76
Run on models
none yet
Process rating
D
38/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: RA-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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token SKILL.md:87
    High-entropy token-like string (may be an id, hash or a credential)
    Campaign Name: [Q2-2…ise]
  • low Secrets in code secret-high-entropy-token SKILL.md:113
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Name: `Q2-2…ise`
    quoted

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8126 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 38/100

  • 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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 17 mutating operations with no state check
  • 40Execution cost. Instruction body is 8126 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 85Steps. 330 steps, 2 vague phrases
  • 100Consistency. Name and required fields are in place
  • 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
  • -227 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 473: enough signal without eating the budget
  • +4Structure: 52 headings
  • +3Step-by-step instructions: 330 items
  • +4Has examples (18 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.