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 siarhei-belavus/agent-public --skill improve-codebase-architecturegit clone --depth 1 https://github.com/siarhei-belavus/agent-publicWrote 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/siarhei-belavus/agent-public/improve-codebase-architecture)<a href="https://agentmods.dev/skills/siarhei-belavus/agent-public/improve-codebase-architecture"><img src="https://agentmods.dev/badge/skills/siarhei-belavus/agent-public/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/siarhei-belavus/agent-public/improve-codebase-architecture"><img src="https://agentmods.dev/badge/skills/siarhei-belavus/agent-public/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 | $0.00064 | $0.01012 |
| Opus 5.5 | $0.00026 | $0.00405 |
| Sonnet 5.5 | $0.00013 | $0.00202 |
| Haiku 4.5 | $0.00006 | $0.00101 |
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 2mo 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.
This is a copy
83% identical to deep-module-refactor — 23 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
Improve Codebase Architecture
Explore a codebase like an AI would, surface architectural friction, discover opportunities for improving testability, and write module-deepening refactor RFCs into a local .rfcs/ folder.
A deep module (John Ousterhout, "A Philosophy of Software Design") has a small interface hiding a large implementation. Deep modules are more testable, more AI-navigable, and let you test at the boundary instead of inside.
Process
1. Explore the codebase
Use Pi's subagent tool to navigate the codebase naturally. Prefer builtin scout first for fast recon; if needed, follow with worker for deeper analysis. Do NOT follow rigid heuristics — explore organically and note where you experience friction:
- Where does understanding one concept require bouncing between many small files?
- Where are modules so shallow that the interface is nearly as complex as the implementation?
- Where have pure functions been extracted just for testability, but the real bugs hide in how they're called?
- Where do tightly-coupled modules create integration risk in the seams between them?
- Which parts of the codebase are untested, or hard to test?
The friction you encounter IS the signal.
2. Present candidates
Present a numbered list of deepening opportunities. For each candidate, show:
- Cluster: Which modules/concepts are involved
- Why they're coupled: Shared types, call patterns, co-ownership of a concept
- Dependency category: See REFERENCE.md for the four categories
- Test impact: What existing tests would be replaced by boundary tests
Do NOT propose interfaces yet. Ask the user: "Which of these would you like to explore?"
3. User picks a candidate
4. Frame the problem space
Before spawning sub-agents, write a user-facing explanation of the problem space for the chosen candidate:
- The constraints any new interface would need to satisfy
- The dependencies it would need to rely on
- A rough illustrative code sketch to make the constraints concrete — this is not a proposal, just a way to ground the constraints
What ships with it
1 file 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.
- 2mo ago First seen · 86 lines · 64 tokens per session scan A 25d2df164c1f
improve-codebase-architecture is a skill published in the GitHub repository siarhei-belavus/agent-public (2 stars, last pushed 5mo ago), licensed MIT. It adds 64 tokens to every session and 1,012 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 83% identical to deep-module-refactor, differing in 23 lines, and is treated as a copy.
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