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 ipiton/agent-memory-mcp --skill agent-memorygit clone --depth 1 https://github.com/ipiton/agent-memory-mcpWrote 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/ipiton/agent-memory-mcp/agent-memory)<a href="https://agentmods.dev/skills/ipiton/agent-memory-mcp/agent-memory"><img src="https://agentmods.dev/badge/skills/ipiton/agent-memory-mcp/agent-memory/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/ipiton/agent-memory-mcp/agent-memory"><img src="https://agentmods.dev/badge/skills/ipiton/agent-memory-mcp/agent-memory.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 | $0.00030 | $0.01593 |
| Opus 5.5 | $0.00012 | $0.00637 |
| Sonnet 5.5 | $0.00006 | $0.00319 |
| Haiku 4.5 | $0.00003 | $0.00159 |
Grade A, and why
agent-memory 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Состояние Памяти (Memory State)
У тебя есть доступ к persistent memory через MCP-инструменты. Твоя задача — поддерживать актуальность "Банка Проекта" (Project Bank), минимизируя шум.
Когда сохранять (store_memory):
- Принято архитектурное решение: тип
semantic, важность0.9. - Выработан рабочий процесс: деплой, тесты, специфичный debug — тип
procedural, важность0.8. - Найден баг или ограничение: описание решения и причин — тип
episodic, важность0.7. - Текущий контекст задачи: над чем работаем в данный момент — тип
working, важность0.5.
Специализированные типы:
- Архитектурное решение: используй
store_decisionс rationale и consequences. - Инцидент: используй
store_incidentс impact, root cause, severity. - Runbook: используй
store_runbookс procedure, trigger, verification, rollback. - Postmortem: используй
store_postmortemс root cause и action items.
Когда вспоминать (recall_memory):
- Начало новой сессии: вызови
summarize_project_contextилиproject_bank_view view=canonical_overviewдля восстановления рабочего контекста. - Перед изменением инфраструктуры: проверь
search_runbooksиrecall_similar_incidentsна наличие рисков. - При вопросе "как делать X": поиск по типу
procedural. - При вопросе "почему так решили": используй
recall_canonical_knowledgeилиproject_bank_view view=decisions.
Жизненный цикл сессии (Session Lifecycle)
1. Start-of-session Recall
Перед началом работы ты обязан восстановить контекст:
- Используй
project_bank_view view=canonical_overviewдля верхнеуровневого понимания. - Изучи недавние изменения в сервисах или компонентах, которые предстоит затронуть.
- Найди релевантные RFC, заметки о инцидентах или чейнджлоги через
semantic_search. - Проверь
steward_status— есть ли pending review items или недавние findings.
2. In-session Patterns
- Решения фиксируй сразу через
store_decision— не откладывай на конец сессии. - Длинные трейсы и логи —
store_memory(episodic), в чат только 5-8 строк. - Перед исследованием файлов — сначала
recall_memoryпо теме, чтобы не искать заново.
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 · 126 lines · 30 tokens per session scan A dfeda5d5e1c0
agent-memory is a skill published in the GitHub repository ipiton/agent-memory-mcp (44 stars, last pushed 5d ago), licensed MIT. It adds 30 tokens to every session and 1,593 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 2026-09-05.
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