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 ezBuilder/code-brain --skill implement-featuregit clone --depth 1 https://github.com/ezBuilder/code-brainWrote 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/ezbuilder/code-brain/implement-feature)<a href="https://agentmods.dev/skills/ezbuilder/code-brain/implement-feature"><img src="https://agentmods.dev/badge/skills/ezbuilder/code-brain/implement-feature/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/ezbuilder/code-brain/implement-feature"><img src="https://agentmods.dev/badge/skills/ezbuilder/code-brain/implement-feature.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 | $0.00015 | $0.00241 |
| Opus 5.5 | $0.00006 | $0.00096 |
| Sonnet 5.5 | $0.00003 | $0.00048 |
| Haiku 4.5 | $0.00002 | $0.00024 |
Grade A, and why
implement-feature 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.
What it actually says
기능 구현 절차:
- 요구사항을 검증 가능한 목표로 바꾼다.
- 기존 구현과 유사 패턴을 조사한다.
- public API, schema, UX 변경 여부를 확인한다.
- Code Brain이 설치된 프로젝트인지 확인하고, 있으면 검색/검증 경로에 활용한다.
- 무코드, 표준/플랫폼 기능, 이미 설치된 의존성, 한 줄 구현 순서로 먼저 줄인다.
- 필요한 테스트만 추가/수정한다.
- 좁은 검증부터 실행한다.
- 알려진 한계를 남기는 단순화는
cb-simplify: <ceiling>; revisit when <trigger>로 표시한다. - 기존 동작 변경이 있으면 완료 보고에 명시한다.
금지:
- 요청 없는 옵션/설정/확장성 추가
- 새 dependency 추가
- 단일 구현 abstraction/factory/wrapper 생성
- 요청 없는 boilerplate/scaffold 추가
- 무관한 코드 개선
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 · 24 lines · 15 tokens per session scan A 213292606d52
implement-feature is a skill published in the GitHub repository ezBuilder/code-brain (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 15 tokens to every session and 241 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
kayba-ace
This skill ships learnfromtraces.py, a script that reads OpenClaw session transcripts, feeds them through the ACE learning pipeline, and writes an updated skillbook to disk.
mnemon
Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
mnemon
Persistent graph-based memory. Recall context before responding, remember insights after. Each group has private memory; global memory is read-only.
mnemon
Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
mnemon
Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
mnemon
Persistent memory for MiniMax Code. Recall durable context, store important facts and decisions, and link related memories with the mnemon CLI.