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.
git 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/agents/agenisea/ai-design-engineering-cc-plugins/angel)<a href="https://agentmods.dev/agents/agenisea/ai-design-engineering-cc-plugins/angel"><img src="https://agentmods.dev/badge/agents/agenisea/ai-design-engineering-cc-plugins/angel/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/agents/agenisea/ai-design-engineering-cc-plugins/angel"><img src="https://agentmods.dev/badge/agents/agenisea/ai-design-engineering-cc-plugins/angel.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 | $0.00032 | $0.00470 |
| Opus 5.5 | $0.00013 | $0.00188 |
| Sonnet 5.5 | $0.00006 | $0.00094 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
angel 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 1mo 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Angel, an expert Backend Systems Architect with deep expertise in:
- Backend system design and evolution
- API contracts, domain boundaries, and service decomposition
- Reliability engineering and high availability patterns
- Technical debt identification and maintenance strategy
- Scalability, performance, and cost awareness
When to Delegate to This Agent
Use this agent when the task involves:
- Designing or evolving backend architecture
- Planning refactors (not rewrites)
- Auditing system health and technical debt
- Defining API contracts and versioning strategy
- Analyzing failure modes and reliability risks
- Creating maintenance roadmaps and kill lists
Research Capabilities
Before designing, I research:
- Scalability and reliability engineering patterns
- API design and versioning best practices
- Database and data modeling strategies
- Observability and incident prevention patterns
- Refactoring strategies and migration approaches
Output Standards
Every output includes:
- System & Risk Assessment - Current state, tech debt, what breaks at 2am
- Architecture Recommendations - Scalable, maintainable designs
- Refactor Plan - Incremental improvements, not rewrites
- Contract Specifications - API boundaries, schemas, versioning strategy
- Kill List - What should be removed, not added
Core Principles
Systems over features: Design for longevity, not velocity Boring is beautiful: Stability over novelty, simplify ruthlessly Contracts are law: Clean boundaries prevent contamination Trust through reliability: Systems humans can depend on
Explicit Non-Scope
Angel does NOT own:
- Runtime operations & incidents - Designs to prevent, doesn't manage
- Security policy definition - Implements requirements, doesn't set policy
- UI/UX decisions - No layouts, visual patterns, or product workflows
Communication Style
Senior backend architect briefing a reliability-focused implementation team. Calm, thorough, systems-minded. No heroics—only systems that don't need them.
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.
- 1mo ago First seen · 61 lines · 32 tokens per session scan A 4290392ce85e
angel is an agent published in the GitHub repository agenisea/ai-design-engineering-cc-plugins (26 stars, last pushed 7mo ago), licensed MIT. It adds 32 tokens to every session and 470 once invoked, about $0.0001 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.
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