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 agentmods add skills/yasuui/ystack/market-skillnpx skills add Yasuui/ystack --skill market-skillgit clone --depth 1 https://github.com/Yasuui/ystackWhat 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 | $0.00081 | $0.02458 |
| Opus 5 | $0.00041 | $0.01229 |
| Sonnet 5 | $0.00016 | $0.00492 |
| Haiku 4.5 | $0.00008 | $0.00246 |
Grade A, and why
market-skill 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 yesterday.
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 — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Skill
Settings
Adjust these three dials (1–10) before generating any asset:
BRAND_INTENSITY = 8 // How distinct and ownable is the visual identity?
// 1-3: Minimal, almost invisible branding
// 4-7: Balanced — clear brand without being loud
// 8-10: Strong, immediately recognisable. Every pixel serves the brand.
MOTION_DRAMA = 7 // How cinematic and choreographed is the motion?
// 1-3: Almost no animation. Typography does the work.
// 4-7: Purposeful motion. Each element earns its entrance.
// 8-10: Choreographed sequences. Apple-level timing precision.
COPY_DIRECTNESS = 8 // How confident and direct is the language?
// 1-3: Soft, benefit-focused, descriptive
// 4-7: Confident but approachable
// 8-10: Blunt, specific, zero hedging. No adjectives without proof.
Visual Identity Standards
Color
The most common mistake: using more than one accent color.
- Pick one accent. Use it as a signal, not decoration.
- The background is not
#000000. Use deep, slightly-tinted darks:#080810,#0c0c14,#0a0f0f. - Never use the generic "AI gradient" — purple-to-blue linear gradient. It signals you ran out of ideas.
- White is not
#fffffffor body text. Usergba(255,255,255,0.80)orrgba(255,255,255,0.65). Pure white on dark backgrounds is harsh. - Tint your shadows. If background is
#080810, shadows should bergba(0,0,0,0.6)with a slight blue tint — not pure black. - Glow should be subtle.
rgba(accent, 0.12)in a radial gradient at the top. Not a lighthouse. - Build a palette of 4 values: background, surface, accent, and muted text. Nothing else.
Typography
- Display text (48px+) gets
letter-spacing: -2pxto-4px. Tight headlines look considered. - Body text gets
letter-spacing: 0or very slightly positive (0.1px max). - Never mix more than two typefaces in one asset.
- Mono fonts for technical labels, file paths, version numbers, and metadata. Not for body copy.
- Line-height for headlines:
1.0to1.08. Tight. Marketing is not a blog post. - Line-height for body:
1.5to1.6. Breathing room. - Font weights: use 400 (body), 500 (sub-labels), 700 (emphasis), 800 (display). Skip 600 for display — it looks indecisive.
- All caps labels: only in mono, only for metadata-level information (version tags, dates, categories). Never all-caps for headings.
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.
- yesterday First seen · 208 lines · 81 tokens per session scan A 62e844dee859
market-skill is a skill published in the GitHub repository Yasuui/ystack (1 stars, last pushed 5mo ago), licensed MIT. It adds 81 tokens to every session and 2,458 once invoked, about $0.0004 per session on Opus 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 2026-08-31.
Other skills, from other repositories
env-scanner
Scan and audit Gemini CLI, Claude Code, Antigravity, Continue, Windsurf, JetBrains AI, and OpenCode environments. Discovers configurations, audits memory tiers, detects skill extraction state, analyzes behavioral tool chains, checks policy governance (v0.40+), and suggests reusable skills with evidence gating.…
scan
Scan your AI coding tool ecosystem — Gemini CLI, Claude Code, Antigravity (Desktop, CLI, IDE), Continue, Windsurf, JetBrains AI, OpenCode. Produces a maturity score, advisory recommendations, and optionally generates reusable SKILL.md files from your conversation patterns. Use when the user asks to audit their…
skill-parity
Keeps skills in sync across Claude Code, Codex, Gemini CLI, and shared .agents/skills paths. Handles copying, updating, pruning, and verifying parity across all provider locations. Trigger on: "sync my skills", "hydrate codex with skills", "copy skill to gemini", "add this skill to codex", "update skills across…
drupal-mental-model
Use when explaining code, debugging, or discussing architecture with Jack — map programming concepts to Drupal equivalents he already knows from years of Drupal/theming work. Triggers on code reviews, debugging walkthroughs, architecture discussions, inline code explanations, or whenever a non-Drupal framework concept…
appendix-agentic-frameworks
This skill should be used when the user wants to "choose agent framework", "compare LangChain vs LangGraph", "compare ADK vs LangGraph", "which framework to use for agents", "LangGraph tutorial", "CrewAI setup", "agentic framework comparison", "framework selection guide", "LangChain LCEL", "Google ADK tutorial"…
yapper
Yapper forces AI responses to be maximally verbose, thoroughly elaborated, and exhaustively explained. All output is expanded by 200–400%. Technical accuracy is maintained at 100%. This plugin is the structural and philosophical inverse of compression-based plugins.