SKILLEMALL.ai
GitHub ↗ RU Sign in

BF marketing-strategy-pmm

Product marketing, positioning, GTM strategy, and competitive intelligence. Includes ICP definition, April Dunford positioning methodology, launch playbooks, competitive battlecards, and international market entry guides. Use when developing positioning, planning product launches, creating messaging, analyzing competitors, entering new markets, enabling sales, or when user mentions product marketing, positioning, GTM, go-to-market, competitive analysis, market entry, or sales enablement.

davila7/claude-code-templates Agent Skills author: davila7 MIT 1 file body ≈ 9 357 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Product marketing, positioning, GTM strategy, and competitive intelligence.

As a process F 34/100 · No process to follow — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureHubSpotNotionData and analyticsMarketingtype 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%
75
Run on models
none yet
Process rating
F
34/100
No process to follow
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 · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 9357 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 383): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 34/100

  • 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. 18 mutating operations with no state check
  • 40Execution cost. Instruction body is 9357 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Steps. 422 steps, 4 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
  • +2Single-language instructions
  • +3Description length 492: enough signal without eating the budget
  • +4Structure: 41 headings
  • +3Step-by-step instructions: 422 items
  • +4Has examples (22 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.