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 skills add Prismer-AI/PrismerCloud --skill conversation-compactiongit 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/conversation-compaction)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/conversation-compaction"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/conversation-compaction/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/prismer-ai/prismercloud/conversation-compaction"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/conversation-compaction.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 | $0.00139 | $0.01474 |
| Opus 5.5 | $0.00056 | $0.00590 |
| Sonnet 5.5 | $0.00028 | $0.00295 |
| Haiku 4.5 | $0.00014 | $0.00147 |
Grade A, and why
conversation-compaction 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 19d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversation Compaction — memory's session projection
You are producing the session projection of a slice of an aging conversation (messages that scrolled past the recent verbatim window). The projection replaces the raw slice in future agent context.
The model — sync by POINTER, not by re-summary
Durable knowledge lives in memory (the memory skill owns it). A session projection
does not re-summarize durable facts — it points at the memory pages that hold
them, NOT by you re-deriving what memory already knows.
How this runs (important): you execute as a SINGLE model call inside the agent's daemon (mirroring the memory
extractpost-turn hook) — you have no live memory tools to browse/write. Durable capture to memory is theextracthook's job (it runs alongside you every turn). Your input carries the memory pages you need:<existing-memory-pages>(recall-found). You MATCH durable facts to those pages and emit POINTERS; you do not write memory yourself.
Your job over a slice is only:
① for each slice message:
durable + matches a provided page → emit a memoryRefs POINTER (path + note)
durable + NO page matches → inline into summary (degraded; extract will capture it)
ephemeral → carry THIN in the digest (ages out)
noise → DROP
② emit — the thin ephemeral digest + POINTERS to every matched page
Input you receive
slice: the raw messages, oldest first, each[msgId] @username (role): text.conversationType:group|direct.<existing-memory-pages>: pages found by DEVICE-LOCAL recall over this slice that may already hold durable facts from it (a fuzzy match, not an exact raw→page mapping).
Pointer-first rule (recall-fed)
You are given <existing-memory-pages> (pages recall thinks already hold durable facts from
this slice). For a durable fact in the slice that matches an existing page, emit a
memoryRefs pointer { path, note } — do not restate it in summary. Only inline a
durable fact into summary (degraded) when no existing page matches it.
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.
- 19d ago First seen · 118 lines · 139 tokens per session scan A cb630cca1067
conversation-compaction is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,384 stars, last pushed 3d ago), licensed MIT. It adds 139 tokens to every session and 1,474 once invoked, about $0.0006 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 2026-09-29.
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.
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.
memex-best-practices
Zettelkasten best practices for building a high-quality knowledge graph.
memex-organize
Periodic maintenance of the Zettelkasten card network.
memex-recall
Load prior knowledge from Zettelkasten memory when the task likely benefits from past context.
memex-sync
Sync Zettelkasten cards across devices via git.