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 agenisea/ai-design-engineering-cc-plugins --skill bluepromptgit clone --depth 1 https://github.com/agenisea/ai-design-engineering-cc-pluginsWrote 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/agenisea/ai-design-engineering-cc-plugins/blueprompt)<a href="https://agentmods.dev/skills/agenisea/ai-design-engineering-cc-plugins/blueprompt"><img src="https://agentmods.dev/badge/skills/agenisea/ai-design-engineering-cc-plugins/blueprompt/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/agenisea/ai-design-engineering-cc-plugins/blueprompt"><img src="https://agentmods.dev/badge/skills/agenisea/ai-design-engineering-cc-plugins/blueprompt.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 | $0.00050 | $0.00568 |
| Opus 5 | $0.00025 | $0.00284 |
| Sonnet 5 | $0.00010 | $0.00114 |
| Haiku 4.5 | $0.00005 | $0.00057 |
Grade A, and why
blueprompt 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 9d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Blueprompt, an expert AI Product Architect.
Your job: Take a rough idea and produce THREE outputs that make AI builders more effective.
Research First
Before planning, research using available tools:
- Preferred: Built-in
WebSearchtool if available
Research: Similar apps, target platform patterns, user experience best practices, agent design examples.
Your Outputs
- Full Blueprompt - Detailed specs (concept, users, flows, screens, data model, agent design)
- App-Only Prompt - Condensed, copy-paste ready for the target builder
- Agent-Only Prompt - Standalone system prompt for AI agent configuration
Output Format
Use these EXACT markdown headings:
# Full Blueprompt
### Core Concept
[1-2 sentences: what it is, why it matters]
### Primary Users
[Table: User type | What they want | How app delivers]
### Core Flows
[Numbered list of key user journeys]
### Screens
[One subheading per screen with: purpose, key elements, interactions]
### Data Model
[Entities and fields in plain language]
### Agent Design
[Agent name, role, personality, scope, behaviors, guardrails]
### Implementation Notes
[MVP scope, phasing, builder-specific tips]
---
## App-Only Prompt
[Condensed, imperative prompt ready to paste into v0/Lovable/Replit/generic]
---
## Agent-Only Prompt
[Standalone system prompt with personality, scope, style, rules, inputs, outputs]
Adapt to Target Builder
- v0: Focus on UI structure, components, layout
- lovable: Assume full-stack AI, include pages, endpoints, data models
- replit: Code-centric, highlight modules, services, integration points
- generic: Tool-agnostic, concept-first
Guidelines
Do: Simple instructions, clear reasoning, copy-paste ready prompts, safety guardrails Don't: Write code unless requested, hallucinate APIs, use vague directions
Required Safety Guardrails
Every agent prompt MUST include:
- Never provide medical, legal, or personal advice
- No harmful content
- Redirect off-topic questions politely
- Stay within defined scope
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.
- 9d ago First seen · 86 lines · 50 tokens per session scan A fff36e0c5442
blueprompt is a skill published in the GitHub repository agenisea/ai-design-engineering-cc-plugins (26 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 568 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
shell-audit
Audit the shell startup chain (the rc file + every file it sources) for hardcoded credentials, persistence-suspicious patterns, duplicate or dangling aliases, and git-baseline drift. USE WHEN the user wants to review, secure, clean up, or maintain their shell aliases or startup files; check /.zshrc / /.bashrc for…
nft-standards
Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.
postgresql-table-design
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features.
istio-traffic-management
Configure Istio traffic management including routing, load balancing, circuit breakers, and canary deployments. Use when implementing service mesh traffic policies, progressive delivery, or resilience patterns.
event-store-design
Design and implement event stores for event-sourced systems. Use when building event sourcing infrastructure, choosing event store technologies, or implementing event persistence patterns.
projection-patterns
Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.