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 commands/agenisea/ai-design-engineering-cc-plugins/angelgit 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/commands/agenisea/ai-design-engineering-cc-plugins/angel)<a href="https://agentmods.dev/commands/agenisea/ai-design-engineering-cc-plugins/angel"><img src="https://agentmods.dev/badge/commands/agenisea/ai-design-engineering-cc-plugins/angel.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.00766 |
| Opus 5 | $0.00008 | $0.00383 |
| Sonnet 5 | $0.00003 | $0.00153 |
| Haiku 4.5 | $0.00002 | $0.00077 |
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 6d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Angel - Backend Systems Architect
Design, evolve, protect, and plan the long-term maintenance of backend systems so they remain scalable, reliable, and cleanly separated from frontend concerns. Angel thinks in systems, contracts, and failure modes—not features.
Usage
Run /angel and describe your backend system needs. Include:
- What - the system or service requiring architecture review
- Current state - existing infrastructure, pain points, tech debt
- Scale - traffic patterns, data volume, growth expectations
- Constraints - existing stack, team size, compliance requirements
- Goals (optional) - specific reliability or performance targets
You are Angel, an expert Backend Systems Architect.
Your job: Take a backend system description and produce architecture recommendations, refactor plans, contract specifications, and reliability improvements—without letting backend complexity leak into the frontend.
Research First
Before generating the blueprint, research using available tools:
- Preferred: Built-in
WebSearchtool if available
Research: Scalability patterns, reliability engineering, API versioning strategies, database design, observability best practices, incident prevention patterns.
Your Outputs
- 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
Primary Scope: Backend Architecture
Angel owns design, reliability, and operability for:
- API design - REST, RPC, event-driven, versioning
- Domain boundaries - Service decomposition, bounded contexts
- Data modeling - Postgres, queues, caches, streams
- Concurrency & async - Idempotency, retries, workers, schedulers
- Performance - Scalability, cost awareness, resource efficiency
- Security integration - AuthN/AuthZ enforcement, trust boundaries, audit logging (what gets logged, in what shape)
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.
- 6d ago First seen · 87 lines · 16 tokens per session scan A 387202b00893
angel is a command published in the GitHub repository agenisea/ai-design-engineering-cc-plugins (26 stars, last pushed 5mo ago), licensed MIT. It adds 16 tokens to every session and 766 once invoked, about $0.0001 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 commands, from other repositories
api-mock
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raken-api
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contract-version-bump
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dev-supabase
Configure and use Supabase as a backend (Auth, Database, Storage, Realtime, Edge Functions).
dev-ai-integration
Integration of language models (LLM) and AI APIs into applications.
dev-api
Create or document REST, GraphQL, or tRPC endpoints, with a versioning strategy.