Borrowing it
Nothing to install: this file belongs to ctxpipe-ai/ctxpipe. 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/ctxpipe-ai/ctxpipe/main/.cursor/skills/improve-codebase-architecture/SKILL.mdgit clone --depth 1 https://github.com/ctxpipe-ai/ctxpipeWrote 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/ctxpipe-ai/ctxpipe/improve-codebase-architecture)<a href="https://agentmods.dev/skills/ctxpipe-ai/ctxpipe/improve-codebase-architecture"><img src="https://agentmods.dev/badge/skills/ctxpipe-ai/ctxpipe/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/ctxpipe-ai/ctxpipe/improve-codebase-architecture"><img src="https://agentmods.dev/badge/skills/ctxpipe-ai/ctxpipe/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 | $0.01394 |
| Opus 5.5 | $0.00013 | $0.00558 |
| Sonnet 5.5 | $0.00006 | $0.00279 |
| Haiku 4.5 | $0.00003 | $0.00139 |
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 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.
This is a copy
83% identical to improve-codebase-architecture — 51 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve Codebase Architecture
Surface architectural friction and propose deepening opportunities — refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.
This command is informed by the project's domain model and built on a shared design vocabulary:
- Run the
/codebase-designskill for the architecture vocabulary (module, interface, depth, seam, adapter, leverage, locality) and its principles (the deletion test, "the interface is the test surface", "one adapter = hypothetical seam, two = real"). Use these terms exactly in every suggestion — don't drift into "component," "service," "API," or "boundary." - Domain language and ADRs live in
.ai/memory/— follow.ai/agents/domain.md. Do not re-litigate stack ADRs. - Leftover analogue or unused generality is the
simplicity-auditaudit branch, not a deepening card.
Process
1. Explore
Scope before you scan — YAGNI. Deepening a module pays off by making future changes to it easier, so put extra weight on the parts of the codebase that have recently changed. Decide where to look before you look:
- If the user named a direction — a module, a subsystem, a pain point — take it, and skip the inference below.
- Otherwise, walk back a good stretch of the commit history (
git log --oneline) to find the codebase's hot spots — the files and areas that keep coming up — and let those paths pull your attention first. If the changes are scattered with no clear hot spot, widen the net.
Read .ai/memory/glossary.md and the matching ADR from .ai/memory/decisions/ first.
Then spawn a sub-agent to walk the codebase. Don't follow rigid heuristics — explore organically and note where you experience friction:
- Where does understanding one concept require bouncing between many small modules?
- Where are modules shallow — interface 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 (no locality)?
- Where do tightly-coupled modules leak across their seams?
- Which parts of the codebase are untested, or hard to test through their current interface?
What ships with it
2 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.
- 1mo ago First seen · 73 lines · 32 tokens per session scan A fdf017f103e2
improve-codebase-architecture is a skill published in the GitHub repository ctxpipe-ai/ctxpipe (26 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 1,394 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 83% identical to improve-codebase-architecture, differing in 51 lines, and is treated as a copy.
Other skills, from other repositories
qa-review
Validate that implemented code fully satisfies Story acceptance criteria, respects rules, and introduces no regressions. This is the hard quality gate — no pass means no delivery. Activate after implementation is complete.
consistency-check
Detect inconsistencies across related artefacts and governance constraints. Activate after story generation, after plan preparation, before implementation, or after remediation attempts. Reports issues — does not fix them.
code-review
Performs multi-dimensional code review covering security, quality, spec compliance, and maintainability. Used when reviewing completed implementations or before merging changes.
maxsim-simplify
Reviews changed code for reuse opportunities, quality issues, and efficiency improvements, then fixes actionable items. Used after implementing features or when code feels over-engineered.
improve-codebase-architecture
Surface deepening refactors that turn shallow modules into deep ones, informed by CONTEXT.md and docs/adr/. Use when the user asks to improve architecture, find refactor candidates, raise testability, or make a codebase more agent-navigable. Skip for single localized fixes.
security-review
Adversarial security audit — STRIDE, OWASP Top 10, supply-chain (CVE/SBOM), secrets scan, auth/authz analysis. Use on changes touching auth, input parsing, deserialization, network I/O, dependencies, or secrets; before any production release or external-surface PR.