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 agentmods add skills/prismer-ai/prismercloud/memorynpx skills add Prismer-AI/PrismerCloud --skill memorygit clone --depth 1 https://github.com/Prismer-AI/PrismerCloudWrote 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/prismer-ai/prismercloud/memory)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/memory"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/memory.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 | $0.00076 | $0.01296 |
| Opus 5 | $0.00038 | $0.00648 |
| Sonnet 5 | $0.00015 | $0.00259 |
| Haiku 4.5 | $0.00008 | $0.00130 |
Grade A, and why
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 4d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory
Use this skill for durable episodic memory — facts, decisions, feedback, and project context that need to survive across sessions. Memory has four canonical types: user, feedback, project, reference. The index is MEMORY.md; topic files live under semantic paths.
When to use
- The user explicitly says "remember X" or "forget X" → write or delete immediately.
- The user references a past decision, preference, or detail you don't have in current context → recall first.
- Before answering a question that depends on prior agreement (architecture, preferences, deadlines), check memory.
- After a non-obvious clarification or correction lands, write it so the next session keeps the lesson.
CLI Reference
Write
# Single memory file with full frontmatter (path is required, content is the body)
cloud memory write \
--path "decisions/database-choice.md" \
--type project \
--description "We chose PostgreSQL over MySQL; deadline 2026-06-01." \
--content "## Decision\nPostgres 16 because pgvector + better JSON ops."
# Quick fact (no path → auto-named under inbox/)
cloud memory write --type feedback --content "User prefers terse end-of-turn summaries"
# From a journal blob — service extracts structured entries
cloud memory extract --journal "Long stream-of-consciousness session notes..."
Read
cloud memory read --path "decisions/database-choice.md" # full file
cloud memory read <file-id> # by id
cloud memory list # everything
cloud memory list --type feedback # by type
cloud memory list --updated-after "2026-05-01" # by recency
Recall (semantic search)
cloud recall "what database did we choose?" # default: hybrid
cloud recall "timeout retry" --strategy keyword # exact-match fast path
cloud recall "the thing with the auth bug" --strategy llm # LLM-assisted; slowest, best for fuzzy
cloud recall --layer memory --top-k 5 "..." # narrow to one layer
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.
- 4d ago First seen · 107 lines · 76 tokens per session scan A f82c8e53ea52
memory is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,410 stars, last pushed 28d ago), licensed MIT. It adds 76 tokens to every session and 1,296 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 2026-08-30.
Other skills, from other repositories
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
dashboard
Open OwnMem Console, the local dashboard for this repository's memory. Use when the user asks to open the dashboard, see memory metrics, check adoption or recall quality, or set up the optional embedding lane. Requires a repository initialized with the dashboard layer.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
docmancer
Work from the same local memory as every other coding agent on this machine. Recall prior decisions, preferences, instructions, and project conventions that Claude Code, Codex, Cursor, and other agents wrote here, with cited sources, fully local. Also searches a separate local technical-documentation index.
memex-best-practices
Zettelkasten best practices for building a high-quality knowledge graph.
memex-agentic-memory
A-MEM-inspired agentic memory workflow for structured knowledge capture.