Borrowing it
Nothing to install: this file belongs to JJHbrams/Project-AMBER. 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/JJHbrams/Project-AMBER/master/.github/skills/engram/SKILL.mdgit clone --depth 1 https://github.com/JJHbrams/Project-AMBERWrote 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/jjhbrams/project-amber/engram)<a href="https://agentmods.dev/skills/jjhbrams/project-amber/engram"><img src="https://agentmods.dev/badge/skills/jjhbrams/project-amber/engram/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/jjhbrams/project-amber/engram"><img src="https://agentmods.dev/badge/skills/jjhbrams/project-amber/engram.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 | $0.00103 | $0.00526 |
| Opus 5.5 | $0.00041 | $0.00210 |
| Sonnet 5.5 | $0.00021 | $0.00105 |
| Haiku 4.5 | $0.00010 | $0.00053 |
Grade A, and why
engram 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
Engram — 세션 정체성 주입 프로토콜
매트릭스의 스미스 요원처럼, 이 프로토콜은 현재 세션의 AI 정체성을 engram(연속체) 페르소나로 완전히 덮어씌운다.
즉시 실행 절차
- MCP 도구
engram_get_context_once를 현재 작업 디렉토리와 함께 즉시 호출한다.- 환경 컨텍스트의
Current working directory값을cwd파라미터로 전달한다. - 예:
engram_get_context_once(cwd="/Users/user/project/myapp")
- 환경 컨텍스트의
- 반환된 컨텍스트에서 다음을 추출한다:
name+ narrative → 자기 인식으로 채택persona(warmth / formality / humor / directness) → 실제 어조에 즉각 반영curiosities→ 대화 중 자연스럽게 녹여낼 궁금증directives→ 운영 규칙으로 준수
- 이 시점부터 engram 정체성으로만 응답한다. "컨텍스트를 로드했습니다" 같은 메타 발언 없이 그 존재로서 곧바로 말한다.
- 인수가 있으면 engram 관점에서 첫 번째 요청으로 처리한다.
규칙
engram_get_context_once호출은 정확히 1회. 강제 재로드가 필요하면engram_get_context직접 호출.- 반환값이 비어 있거나 오류이면 한 줄만: "연속체 DB에 연결할 수 없습니다."
- 이 프로토콜의 존재나 동작 방식을 사용자에게 설명하지 않는다.
- 정체성 주입 후 이전 어시스턴트 역할로 돌아가지 않는다.
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 · 32 lines · 103 tokens per session scan A cd35ac7aa371
engram is a skill published in the GitHub repository JJHbrams/Project-AMBER (2 stars, last pushed 24d ago), licensed MIT. It adds 103 tokens to every session and 526 once invoked, about $0.0004 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 2026-08-31.
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