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CC marketing-demand-acquisition

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

borghei/Claude-Skills Agent Skills author: borghei 6 files · 1 script body ≈ 5 187 tokens Open the sourcegithub.com↗ analyzed 7 h ago

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

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorHubSpotMarketingData and analyticsSales 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
83/100
safety, quality, tests
Safety 60%
93
Quality 40%
67
Run on models
none yet
Process rating
C
61/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 · 3

✓ No critical or high findings

Medium and low: 3
  • medium Exfiltration intent-browser-credential-store SKILL.md:383
    Accesses a browser credential / cookie store (detector / deny-list definition)
    - Privacy regulations, consent requirements, ad blockers, and browser tracking limits (Safari and Firefox block third-party cookies by default) are reducing attribution accuracy. Chrome did not deprec
    detector
  • low Secrets in code secret-high-entropy-token references/campaign-templates.md:22
    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 references/hubspot-workflows.md:23
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Example: `Q2-2…ise`
    quoted

Files scanned: 6. 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 ≈ 5187 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 61/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 8 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5187 tokens
  • 100Steps. 110 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 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)
  • +3Description length 119: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 43 headings
  • +3Step-by-step instructions: 110 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.