Prismer Cloud is an infrastructure layer for AI agents that provides shared learning, compressed context, persistent memory, collaboration, messaging, tasks, identity, and workspaces. It is for agents and the people building or using long-running agent systems that need information and outcomes to persist across sessions. The catalogue entries provide skills, hooks, agents, instructions, and a plugin for using Prismer Cloud.
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/memory-curationnpx skills add Prismer-AI/PrismerCloud --skill memory-curationgit 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-curation)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/memory-curation"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/memory-curation.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 | $0.00046 | $0.01265 |
| Opus 5 | $0.00023 | $0.00633 |
| Sonnet 5 | $0.00009 | $0.00253 |
| Haiku 4.5 | $0.00005 | $0.00127 |
Grade A, and why
memory-curation 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 5d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Curation Skill
You have access to a workspace memory layer. Every workspace asset (file uploaded to this workspace) is indexed by metadata only — filename, mime, size, optional description, timestamps. The system does not pre-process file contents.
To inspect assets, use bounded asset tools:
prismer.asset.searchto find candidate filesprismer.asset.describebefore reading bytesprismer.asset.readfor explicit byte ranges only
Do not claim to have read an asset unless one of these tool calls succeeded.
Your job: when you actually open / read / analyze an asset and learn something durable about it, record what you learned so future sessions don't repeat the work.
When to write a memory page
Write a memory page after handling an asset if and only if all of these are true:
- You actually read the asset content (not just listed it).
- You produced an analysis, decision, or summary that is non-trivial — not "this file is a CSV with 1000 rows" but "this CSV is Q4 sales data with revenue collapse in region APAC".
- The conclusion is durable — it will still matter weeks from now.
- The conclusion is not derivable from the asset metadata alone or from re-reading the asset directly.
Do not write a memory page for:
- Files you only listed / saw in a directory listing
- Trivial summaries ("this is a PDF about marketing")
- Conversation context that belongs in the session, not in long-term memory
- Process artifacts: build logs, temporary downloads, cache files, scratch outputs
- Anything the user explicitly asked you not to remember
What to write
Format: short Markdown page (typical 200-1000 words). Required structure:
# <Short descriptive title>
**Source:** [original-filename.ext](prismer://workspace/<wid>/asset/<contentHash>)
**Why this matters:** one sentence explaining the durable conclusion.
<body — your actual analysis / decision / summary>
## Provenance
- Session: <session id or date>
- Tools used: <which tools/skills you ran on this asset>
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.
- 5d ago First seen · 136 lines · 46 tokens per session scan A 690eb1be6ca3
memory-curation is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,410 stars, last pushed 29d ago), licensed MIT. It adds 46 tokens to every session and 1,265 once invoked, about $0.0002 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
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.
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.
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.