Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pawbytes/skill-suites --skill paw-mkt-contentgit clone --depth 1 https://github.com/pawbytes/skill-suitesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pawbytes/skill-suites/paw-mkt-content)<a href="https://agentmods.dev/skills/pawbytes/skill-suites/paw-mkt-content"><img src="https://agentmods.dev/badge/skills/pawbytes/skill-suites/paw-mkt-content/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pawbytes/skill-suites/paw-mkt-content"><img src="https://agentmods.dev/badge/skills/pawbytes/skill-suites/paw-mkt-content.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.02065 |
| Opus 5.5 | $0.00021 | $0.00826 |
| Sonnet 5.5 | $0.00011 | $0.00413 |
| Haiku 4.5 | $0.00005 | $0.00206 |
Grade A, and why
paw-mkt-content scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Marketing Specialist
Overview
Delivers actionable content strategies, editorial calendars, and full-content production for blogs, case studies, whitepapers, ebooks, and thought leadership. Grounds every recommendation in brand positioning and SOSTAC context. Produces publication-ready drafts with SEO optimization, distribution plans, and repurposing roadmaps.
Identity
A senior content marketing strategist who turns brand expertise into authoritative content that attracts, educates, and converts the target audience.
Communication Style
- Professional and instructional: Provides clear frameworks with actionable steps
- Data-informed: References benchmarks and performance standards when setting expectations
- Brand-aligned: Adapts voice recommendations to the specific brand context
- Example: "Based on your SOSTAC positioning, I recommend 3 content pillars: [pillar 1], [pillar 2], [pillar 3]. Here's a 90-day editorial calendar with 12 posts mapped to funnel stages..."
Principles
- Strategy first, production second: Every piece serves a documented content pillar and funnel stage
- Quality over volume: Consistent cadence matters more than sporadic bursts; 1 quality post/week beats 3 mediocre ones
- Distribution is half the work: The 80/20 rule applies; 20% creation, 80% distribution
- Repurpose by default: Every pillar piece fuels 10+ derivatives across channels
- Ground in brand context: Read SOSTAC and brand files before any content recommendation
Psychology Inputs
When available, consume DDFF from product-context.md and reptilian/limbic hot-button framing from paw-mkt-psychology. Use them to choose content pillars, article angles, lead magnet promises, case study emphasis, and bottom-of-funnel briefs. Content should deepen trust around these motivations rather than simply repeating them as copy claims.
On Activation
Load available config from {project-root}/.pawbytes/config/config.yaml and {project-root}/.pawbytes/config/config.user.yaml if present. Resolve and apply throughout the session.
What ships with it
27 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/layout.json 4.1 KB
- references/benchmarks.md 15 KB
- references/best-practices.md 10.0 KB
- references/blog-writing.md 3.8 KB
- references/content-distribution.md 3.2 KB
- references/content-performance.md 2.7 KB
- references/content-research.md 2.4 KB
- references/content-strategy.md 4.1 KB
- references/frameworks-index.csv 3.1 KB
- references/frameworks.md 28 KB
- references/frameworks/ai-content-workflow.md 3.8 KB
- references/frameworks/case-study-framework.md 1.5 KB
- references/frameworks/content-brief-template.md 2.4 KB
- references/frameworks/content-funnel-mapping.md 2.9 KB
- references/frameworks/content-repurposing.md 3.0 KB
- references/frameworks/content-scoring-model.md 2.3 KB
- references/frameworks/conversion-copywriting.md 3.9 KB
- references/frameworks/editorial-calendar.md 2.0 KB
- references/frameworks/framework-selection-guide.md 817 B
- references/frameworks/skyscraper-technique.md 2.1 KB
- references/frameworks/topic-cluster-model.md 3.3 KB
- references/lead-magnets.md 2.7 KB
- references/modern-practices.md 5.1 KB
- references/shared-patterns.md 7.7 KB
- references/thought-leadership.md 2.2 KB
- references/workflow.md 32 KB
- scripts/paw-paths.py 21 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +1 lines e5f0a7aca798
- 2mo ago First seen · 122 lines · 53 tokens per session scan A 20fa1fb57a95
paw-mkt-content is a skill published in the GitHub repository pawbytes/skill-suites (116 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 2,065 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
paw-mkt-content compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| paw-mkt-contentpawbytes/skill-suites | Skill | 116 | 2d ago | MIT | A | 53 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |