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 kimgoetzke/coding-agent-configs --skill improve-codebasegit clone --depth 1 https://github.com/kimgoetzke/coding-agent-configsWrote 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/kimgoetzke/coding-agent-configs/improve-codebase)<a href="https://agentmods.dev/skills/kimgoetzke/coding-agent-configs/improve-codebase"><img src="https://agentmods.dev/badge/skills/kimgoetzke/coding-agent-configs/improve-codebase/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/kimgoetzke/coding-agent-configs/improve-codebase"><img src="https://agentmods.dev/badge/skills/kimgoetzke/coding-agent-configs/improve-codebase.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 | $0.00067 | $0.01214 |
| Opus 5.5 | $0.00027 | $0.00486 |
| Sonnet 5.5 | $0.00013 | $0.00243 |
| Haiku 4.5 | $0.00007 | $0.00121 |
Grade A, and why
improve-codebase 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
72% identical to improve-codebase-architecture — 83 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve Codebase
Surface architectural friction and propose deepening opportunities — refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.
Glossary
Use these terms exactly in every suggestion. Consistent language is the point — don't drift into "component," "service," "API," or "boundary." Full definitions in language.md.
- Module — anything with an interface and an implementation (function, class, package, slice).
- Interface — everything a caller must know to use the module: types, invariants, error modes, ordering, config. Not just the type signature.
- Implementation — the code inside.
- Depth — leverage at the interface: a lot of behaviour behind a small interface. Deep = high leverage. Shallow = interface nearly as complex as the implementation.
- Seam — where an interface lives; a place behaviour can be altered without editing in place. (Use this, not "boundary.")
- Adapter — a concrete thing satisfying an interface at a seam.
- Leverage — what callers get from depth.
- Locality — what maintainers get from depth: change, bugs, knowledge concentrated in one place.
Key principles (see language.md for the full list):
- Deletion test: imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep.
- The interface is the test surface.
- One adapter = hypothetical seam. Two adapters = real seam.
This skill is informed by the project's domain model — context.md and any .ai/docs/adr/. The domain language gives names to good seams; ADRs record decisions the skill should not re-litigate. See context-format.md and adr-format.md.
Process
1. Explore
Read existing documentation first:
context.md(orcontext-map.md+ eachcontext.mdin a multi-context repo)- Relevant ADRs in
.ai/docs/adr/(and any context-scoped.ai/docs/adr/directories)
What ships with it
4 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 · 77 lines · 67 tokens per session scan A a791e6fc2a8f
improve-codebase is a skill published in the GitHub repository kimgoetzke/coding-agent-configs (2 stars, last pushed 3d ago), licensed MIT. It adds 67 tokens to every session and 1,214 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 72% identical to improve-codebase-architecture, differing in 83 lines, and is treated as a copy.
Other skills, from other repositories
code-review
Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use…
improve-codebase-architecture
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
pr
Use when writing a PR body.
codebase-hygiene
Find semantic duplicates (same intent, different implementation) and shallow modules (thin wrappers spreading complexity). Read-only advisory — changes route through BUILD with full gates. Use when auditing code quality, before refactoring, or when a codebase feels bloated.
security-review
Security review of the diff — the third review axis alongside Standards and Spec. Reviews for injection, auth, secrets, deserialization, SSRF, path traversal, unsafe operations, and dependency confusion. Use as a parallel sub-agent with fresh context (diff-only, anti-anchored) during the review phase.
resolve-pr-feedback
Use when asked to resolve, address, fix, or reply to review feedback on a GitHub Pull Request or GitLab Merge Request, including review comments, unresolved threads, requested changes, or "apply the review comments" requests.