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 agentmods add skills/kimgoetzke/coding-agent-configs/improve-codebasenpx skills add kimgoetzke/coding-agent-configs --skill improve-codebasegit clone --depth 1 https://github.com/kimgoetzke/coding-agent-configsWhat 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 | $0.00067 | $0.01214 |
| Opus 5 | $0.00034 | $0.00607 |
| Sonnet 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 yesterday.
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.
- yesterday 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 12d ago), licensed MIT. It adds 67 tokens to every session and 1,214 once invoked, about $0.0003 per session on Opus 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
codebase-memory
Use the codebase knowledge graph for structural code queries. Triggers on: explore the codebase, understand the architecture, what functions exist, show me the structure, who calls this function, what does X call, trace the call chain, find callers of, show dependencies, impact analysis, dead code, unused functions…
using-pi-subagents
Operate pi-subagents jobs safely, including direct-work decisions, least-privilege tool selection, thinking-level selection, delegation, bidirectional messaging, parallel starts, timeout selection, waiting, cancellation, result handling, verification, and writer isolation.
skill-creator
Create or update Agent Skills (SKILL.md plus optional scripts, references, or assets). Use when someone asks to design a new Agent Skill, refine an existing one, or structure skills for Pi discovery, packaging, or other Agent Skills-compatible clients.
pi-ralph-wiggum
Long-running iterative development loops with pacing control and verifiable progress. Use when tasks require multiple iterations, many discrete steps, or periodic reflection with clear checkpoints; avoid for simple one-shot tasks or quick fixes.
accordion-context-folding
Read this skill if you see {# FOLDED} markers in your context (e.g. {#3f9a2c FOLDED}), or if earlier parts of your context look summarized. Accordion is a desktop tool that may compact older context blocks to keep you under a token budget. The unfold tool restores a folded block (open from your next turn); the recall…
accordion-context-recall
Read this skill when you need to read the full content of a folded context block RIGHT NOW as a tool result — without opening it in your standing context. Use recall(codes) when you only need a value once and do not want to permanently restore the block. Accordion may fold older parts of your context to keep token…