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 kimsanguine/hplan --skill memory-archgit clone --depth 1 https://github.com/kimsanguine/hplanWrote 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/kimsanguine/hplan/memory-arch)<a href="https://agentmods.dev/skills/kimsanguine/hplan/memory-arch"><img src="https://agentmods.dev/badge/skills/kimsanguine/hplan/memory-arch/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/kimsanguine/hplan/memory-arch"><img src="https://agentmods.dev/badge/skills/kimsanguine/hplan/memory-arch.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 | $0.00056 | $0.02659 |
| Opus 5.5 | $0.00022 | $0.01064 |
| Sonnet 5.5 | $0.00011 | $0.00532 |
| Haiku 4.5 | $0.00006 | $0.00266 |
Grade A, and why
memory-arch 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.
How it starts
The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Architecture
에이전트 메모리 시스템 설계 — 단기 컨텍스트, 장기 저장, 검색 전략
Core Goal
- 단일 실행의 컨텍스트 윈도우를 넘어 실행 간 학습과 기억을 유지하는 메모리 아키텍처 설계하여 에이전트의 진화 가능하게 함
- 4가지 메모리 유형(Working, Episodic, Semantic, Procedural)을 각각 적절한 저장소에 배치하여 검색 효율성과 비용을 최적화
- 제한된 컨텍스트 윈도우 내에서 가장 관련성 높은 메모리만 주입하는 검색 및 랭킹 전략 수립
Trigger Gate
Use This Skill When
- 에이전트가 여러 실행(세션)에서 일관된 동작을 해야 하는 경우
- 사용자 선호도, 과거 상담 내용, 또는 도메인 지식을 기억해야 하는 에이전트
- 에이전트가 각 상호작용에서 학습하고 개선되어야 하는 경우
Route to Other Skills When
- 메모리가 플라이휠(사용 → 개선)의 일부인 경우 → strategy --focus growth-loop (데이터 구조 설계)
- 멀티 에이전트 간 메모리 공유 필요 → orchestration (에이전트 간 데이터 흐름)
- 3-tier 시스템에서 workers 간 컨텍스트 전달 → orchestration (Hierarchical pattern 통신 프로토콜)
- 메모리 저장소의 비용 구조 최적화 → strategy --focus biz-model (인프라 비용 계산)
Boundary Checks
- 단일 실행 내에서만 메모리 필요 (세션 간 학습 없음) → Working Memory만 구현, 장기 저장소 불필요
- 컨텍스트 윈도우가 충분히 크면 (1M 이상) → Vector DB 대신 in-context learning으로 단순화 가능
- 메모리 저장소가 프라이빗이 아니면 (공유됨) → 사용자 격리 로직 추가 필수
개념
에이전트의 지능은 메모리에서 나온다. 단일 실행의 컨텍스트 윈도우를 넘어서, 실행 간 학습과 기억을 유지하는 메모리 아키텍처가 에이전트의 진화를 가능하게 한다.
Instructions
You are designing a memory architecture for: $ARGUMENTS
Step 1 — Memory Type Classification
| Memory Type | Scope | Storage | Example |
|---|---|---|---|
| Working Memory | Single execution | Context window | Current task instructions, user input |
| Episodic Memory | Across executions | File/DB | "Last time user X asked about Y, they preferred Z format" |
| Semantic Memory | Permanent knowledge | Embeddings/DB | Domain knowledge, best practices, TK entries |
| Procedural Memory | How-to knowledge | Instructions/Skills | Workflow patterns, prompt templates |
Step 2 — What to Remember
For each agent interaction, decide:
Always Store:
- User preferences and corrections
- Successful output patterns
- Error cases and resolutions
- Key decisions and reasoning
Never Store:
- Sensitive personal data (unless explicitly needed)
- Temporary calculation artifacts
- Redundant information already in semantic memory
What ships with it
5 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 · 260 lines · 56 tokens per session scan A e6321028dfa0
memory-arch is a skill published in the GitHub repository kimsanguine/hplan (3 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 2,659 once invoked, about $0.0002 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.
Other skills, from other repositories
vibe-reflect-and-compound
Captures reusable knowledge after work is done — learnings from feedback or failures, resolved bugs (symptom, root cause, prevention), and recurring code patterns — into the project's persistent memory file. Use after feedback, a non-trivial bug fix, a failed attempt, or the third repetition of a pattern.
apply
Use when a staged proposal has been reviewed and signed off — REVIEW.md Step 3 ticked reviewed or provisional — and is ready to promote into the live contextualizer.
maintaining-project-context
Use when completing development phases or branches to identify and update CLAUDE.md or AGENTS.md files that may have become stale - analyzes what changed, determines affected contracts and documentation, and coordinates updates.
lore-ingest
Use when the user runs /lore:lore-ingest — find files in the lore's raw/ inbox that are new or changed since their last ledger entry, distill or surgically update wiki pages by content type, update log.md, regenerate the index, and commit.
imperio-memoria
Skill "imperio-memoria" from tatagoncalvesof/imperatriz-toolkit, covering imperio memoria — sistema de memoria para agentes ia, activation, frameworks, 1. cerebro (gestao de estado) and 2. diario (memoria compartilhada).
dossie-mentorada
Sistema de memória persistente da mentorada na Travessia Imperatriz. Lê/grava em 19 arquivos JSON estruturados (anamnese, perfil, voz, mecanismo, brand, oferta, funil, KPIs, cases) e abastece automaticamente todas as 225 skills do ecossistema com dados da mentorada. Pilar 1 (Dados) — fundação que destrava todas as…