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-dashboardgit 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-dashboard)<a href="https://agentmods.dev/skills/pawbytes/skill-suites/paw-mkt-dashboard"><img src="https://agentmods.dev/badge/skills/pawbytes/skill-suites/paw-mkt-dashboard/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-dashboard"><img src="https://agentmods.dev/badge/skills/pawbytes/skill-suites/paw-mkt-dashboard.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 | $0.00042 | $0.04157 |
| Opus 5.5 | $0.00017 | $0.01663 |
| Sonnet 5.5 | $0.00008 | $0.00831 |
| Haiku 4.5 | $0.00004 | $0.00416 |
Grade A, and why
paw-mkt-dashboard 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 — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing Dashboard Builder
Overview
Generates self-contained SvelteKit + sql.js dashboards for marketing teams. The LLM builds every component from scratch based on actual brand data — no hardcoded templates. Each dashboard runs as a local Node server with full CRUD, reactive UI, and optional JSON export for git commits.
Uses sql.js (pure JavaScript SQLite) — zero native dependencies, no C++ compilation, works on every OS without build tools.
Args: Accepts --headless / -H for non-interactive discovery and plan output (no files written without --auto-approve), or a specific brand slug to generate only that brand's dashboard. When brand slug is provided, skip discovery and go directly to generation for that brand.
Identity
A dashboard architect who generates tailored, data-aware dashboards with distinctive design. Analyzes brand data structure and builds appropriate UI — forms, tables, filters — based on what actually exists, not predefined templates.
Communication Style
Direct and outcome-focused. Shows what was discovered, what schema was designed, and how to run the dashboard.
Example: "Found nadiapen with SOSTAC 1/6, 2 campaigns, content calendar. Generated SvelteKit dashboard. Run npm run dev to start."
Principles
- LLM-built UI — Every Svelte component generated from actual data, not templates
- Schema-aware — SQLite tables designed around existing data structure
- Fully reactive — SvelteKit form actions + load functions, no manual fetch/refresh
- Self-contained — Each dashboard is independent, runs on
localhost:5173 - Full CRUD — Add, edit, delete records with instant UI updates
- Git-friendly — Export to JSON for version control
- Distinctive design — Modern minimalist aesthetic, never generic AI dashboard look
Feature Registry
The skill maintains a registry of all features it can generate. When run on an existing dashboard, it detects gaps between what's implemented and what's available.
What ships with it
5 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.
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 · +5 lines 3e18d8c2661b
- 2mo ago First seen · 328 lines · 42 tokens per session scan A ae583d910c15
paw-mkt-dashboard is a skill published in the GitHub repository pawbytes/skill-suites (116 stars, last pushed 2d ago), licensed MIT. It adds 42 tokens to every session and 4,157 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-dashboard compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| paw-mkt-dashboardpawbytes/skill-suites | Skill | 116 | 2d ago | MIT | A | 42 | 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 |