Borrowing it
Nothing to install: this file belongs to DDS-Solutions/AI-TadPole-OS. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DDS-Solutions/AI-TadPole-OS/main/.agent/skills/improve-codebase-architecture/SKILL.mdgit 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/improve-codebase-architecture)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/improve-codebase-architecture"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/improve-codebase-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/improve-codebase-architecture"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/improve-codebase-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 | $0.00032 | $0.00657 |
| Opus 5 | $0.00016 | $0.00329 |
| Sonnet 5 | $0.00006 | $0.00131 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
improve-codebase-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 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 — 59 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 / improve-codebase-architecture
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Information drift, legacy terminology, or documentation mismatch.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
Codebase Architecture Improvement Protocol
Scan the codebase for architectural friction and propose deepening opportunities—refactors that transform shallow modules into deep ones.
1. Deep Module Vocabulary
Every proposal must strictly use deep module design terms:
- Module: Scale-agnostic unit with an interface and implementation (function, struct, package, tier slice).
- Interface: Everything a caller must know (type signature, invariants, error modes, performance bounds).
- Depth: Ratio of implementation capability to interface complexity. (Deep = high capability behind small surface).
- Seam: Location where behavior can be altered without editing callers.
- Adapter: Concrete implementation filling a seam slot.
- Leverage: Capability callers gain per unit of interface learned.
- Locality: Concentration of changes, bugs, and verification in one place.
2. Exploration Phase
- Scan Commit Hot Spots: Run
git log --oneline -n 30to identify high-churn modules. - Apply Deletion Test: If you delete a module, does it concentrate complexity (deep module), or merely scatter it (shallow module)?
- Identify Friction:
- Shallow interfaces almost as complex as implementations.
- Pure functions extracted for testing where real bugs hide in caller wiring (poor locality).
- Tightly-coupled modules leaking across seams.
3. Generate Visual HTML Report
Write an offline HTML report to .tmp/reports/architecture-review-<timestamp>.html.
HTML Report Requirements
- CDN Styling: Tailwind CSS CDN + Mermaid CDN.
- Visual Cards: Render cards for each refactoring candidate:
- Modules/Files Involved: Relative file paths.
- Problem Statement: Architecture friction described in terms of locality & depth.
- Proposed Solution: Clear refactoring description.
- Before / After Diagrams: Mermaid sequence/flowcharts showing structural deepening.
- Recommendation Badge:
Strong,Worth Exploring,Speculative.
- Top Recommendation: Clear summary of the highest-leverage candidate.
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 · 59 lines · 32 tokens per session scan A c318798132f2
improve-codebase-architecture is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 657 once invoked, about $0.0002 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 2026-09-03.
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