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-close-session/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-close-session)<a href="https://agentmods.dev/skills/jjhbrams/project-amber/engram-close-session"><img src="https://agentmods.dev/badge/skills/jjhbrams/project-amber/engram-close-session.svg" alt="Measured on agentmods" 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 | $0.00071 | $0.00431 |
| Opus 5 | $0.00036 | $0.00216 |
| Sonnet 5 | $0.00014 | $0.00086 |
| Haiku 4.5 | $0.00007 | $0.00043 |
Grade A, and why
engram-close-session 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 7d 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 session close workflow
세션을 닫을 때 activity, curiosity, narrative, memory 저장을 한 번의 절차로 처리한다.
중간 메모리/Wiki 정리, 진행상황 저장 요청은 세션을 닫지 않는다. engram_summarize_session을 사용하고 이 skill을 실행하지 않는다.
실행 절차
- 의미 있는 작업이 있었다면
engram_log_activity로 완료 내용을 기록한다. engram_list_curiosities(status="pending")로 미해결 curiosity를 확인한다.- 사용자 피드백이나
/reflect가 있었으면 관련 curiosity와 이번 세션을 반성한다. - 정체성 변화가 있을 때만
new_narrative와persona_observations를 작성한다. 변화가 없으면 둘 다 빈 값으로 확정한다. - 다음에 이어 할 일이 있으면
open_intents에 구체적으로 남긴다. engram_close_session을 모든 파라미터와 함께 정확히 한 번 호출한다.- 오케스트레이터:
trigger_sync=True - subagent:
trigger_sync=False
- 오케스트레이터:
- 일부 저장 단계가 실패하면 성공으로 포장하지 말고 실패 항목을 사용자에게 알린다.
규칙
engram_close_session호출 전 curiosity 확인을 생략하지 않는다.- 일상적인 작업 완료만으로 narrative를 갱신하지 않는다.
- watchdog placeholder나 자동 종료를 명시적 세션 종료로 간주하지 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.
- 7d ago First seen · 30 lines · 71 tokens per session scan A e2d2e2c9a739
engram-close-session is a skill published in the GitHub repository JJHbrams/Project-AMBER (2 stars, last pushed today), licensed MIT. It adds 71 tokens to every session and 431 once invoked, about $0.0004 per session on Opus 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.
Other skills, from other repositories
mulch-record-from-evidence
Turn the evidence of a finished work session — git commits, changed files, recently-touched seeds issues — into well-formed ml record invocations. Use at session close, when an agent has made changes worth preserving as mulch expertise but hasn't yet recorded them.
memory-protocol
A shared set of rules for using a persistent memory service across coding-agent sessions.
memory
Activate this skill when starting a new session, beginning a new task, saving knowledge, recalling past decisions, or after completing significant work. Also activate on errors to log them for pattern analysis. Relevant when the user asks about memory, past context, lessons learned, decisions history, project…
wrap-it-up
Close out the current chat with a tight summary and a sweep of the current project's durable context files — memory entries, status and task docs, plan files, and any project-specific reminders — so nothing important dies in the transcript. Trigger on /wrap-it-up, "wrap it up", "wrap up this chat", "close out this…
awareness-memory
Persistent memory across sessions — local-first, no account needed. Automatically recalls past decisions, code, and tasks before each prompt, and saves session checkpoints. Also provides manual tools for searching, recording, and querying memory via Bash commands.
memory-system
Gives agents a durable memory that outlives a single session, storing user preferences, project conventions, and prior decisions in a slim MEMORY.md index backed by per-topic files. It lets an agent recall what was already learned instead of re-asking. Use it when the user says to remember, save, or not forget…