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 DDS-Solutions/AI-TadPole-OS --skill architecturegit clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSWrote 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/dds-solutions/ai-tadpole-os/architecture)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/architecture"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/architecture/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/dds-solutions/ai-tadpole-os/architecture"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/architecture.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 | $0.00028 | $0.00639 |
| Opus 5 | $0.00014 | $0.00319 |
| Sonnet 5 | $0.00006 | $0.00128 |
| Haiku 4.5 | $0.00003 | $0.00064 |
Grade A, and why
architecture 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Agent Skills Registry / architecture
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Information drift, legacy terminology, or documentation mismatch.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
Architecture Decision Framework
"Requirements drive architecture. Trade-offs inform decisions. ADRs capture rationale."
🎯 Selective Reading Rule
Read ONLY files relevant to the request! Check the content map, find what you need.
| File | Description | When to Read |
|---|---|---|
context-discovery.md |
Questions to ask, project classification | Starting architecture design |
trade-off-analysis.md |
ADR templates, trade-off framework | Documenting decisions |
pattern-selection.md |
Decision trees, anti-patterns | Choosing patterns |
examples.md |
MVP, SaaS, Enterprise examples | Reference implementations |
patterns-reference.md |
Quick lookup for patterns | Pattern comparison |
🔗 Related Skills
| Skill | Use For |
|---|---|
@[skills/database-design] |
Database schema design |
@[skills/api-patterns] |
API design patterns |
@[skills/deployment-procedures] |
Deployment architecture |
Core Principle
"Simplicity is the ultimate sophistication."
- Start simple
- Add complexity ONLY when proven necessary
- You can always add patterns later
- Removing complexity is MUCH harder than adding it
Validation Checklist
Before finalizing architecture:
- Requirements clearly understood
- Constraints identified
- Each decision has trade-off analysis
- Simpler alternatives considered
- ADRs written for significant decisions
- Team expertise matches chosen patterns
🏛️ Deep Module Design Principles
When designing or refactoring system components, favor Deep Modules over shallow abstractions:
- Module: Scale-agnostic unit with an interface and implementation.
- Interface: Everything a caller must know (types, ordering constraints, error modes).
- Depth: High implementation capability behind a minimal interface.
- Seam: Clear location where behavior can be altered without touching callers.
- Adapter: Role-based implementation satisfying a seam interface.
- Leverage: Capability callers gain per unit of interface learned.
- Locality: Concentration of changes, bugs, and tests in one place.
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.
- 9d ago First seen · 79 lines · 28 tokens per session scan A 8eb08a6971f8
architecture is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 639 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 2026-08-31.
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