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LeoYeAI/openclaw-marketing-skills

Graded C on context cost and craft. 6,035 tokens of this skill ride in the system prompt of every message, whether or not it fires (heavy footprint). Last graded Sep 9, 2026; re-graded weekly from HEAD.

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C overall · 2.00/4

github.com/LeoYeAI/openclaw-marketing-skills@HEAD · 39 skills · 6,035t resident (heavy) · 5,284t per activation · 199,299t bundle

Resident footprint

6,035t of description text ride in the system prompt of every message across 39 skills (paper's observed per-skill range: 50–280t).

Description honesty

Defensive padding detected: 686 comma-separated trigger keywords; 3289 words (a quiet description is ~15–40). Padding is individually rational and collectively ruinous — it dilutes every co-installed skill.

Body size

Largest body is 2,950 words (~5,284t), loaded on every trigger. Corpus median 921, p90 2207.

Progressive disclosure

Body routes to 65 reference file(s); 61 auxiliary .md files are fetched at point of use instead of riding in the body.

Factoring

Scoped to a coherent procedure set.

CLI leverage

Drives a CLI (`google-ads`, 3 invocations + 15 shell blocks) — verification and state live in code, not prompt.

C overall (2.00/4). 39 skill(s), 6,035t resident on every message (heavy), 5,284t loaded per activation. Weakest axis: resident footprint (F).

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Markdown

[![Skill Grader: C](https://seoagent.com/skill-grader/badge/LeoYeAI/openclaw-marketing-skills.svg)](https://seoagent.com/skill-grader/LeoYeAI/openclaw-marketing-skills)

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<a href="https://seoagent.com/skill-grader/LeoYeAI/openclaw-marketing-skills"><img src="https://seoagent.com/skill-grader/badge/LeoYeAI/openclaw-marketing-skills.svg" alt="Skill Grader: C" height="20"></a>

How this was graded

Six deterministic axes from “@skills: Attention Is All You Have” (Yin et al., 2026) and the open seo-skill-bench footprint scorer: resident footprint, description honesty, body size, progressive disclosure, factoring, and CLI leverage. Tokens are approximated as chars/4. No LLM, no signup, same result every run. Read the full methodology.