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 ingestgit 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/ingest)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/ingest"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/ingest.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00079 | $0.01230 |
| Opus 5 | $0.00039 | $0.00615 |
| Sonnet 5 | $0.00016 | $0.00246 |
| Haiku 4.5 | $0.00008 | $0.00123 |
Grade A, and why
ingest 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 8d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ingest
Use this skill to bring external content into the LLM context window without copy-pasting raw HTML or burning tokens on uncompressed prose. Two paths:
- Web content →
cloud load/cloud search→ returns HQCC (a compressed, LLM-optimized form). Cache hits are free. - Documents (PDF, images) →
cloud parse→ OCR to Markdown. Two modes:fast(digital PDFs, clean images) andhires(scans, handwriting).
When to use
- The user pastes a URL or asks "what does this page say".
- The user asks to research a topic ("AI agent frameworks 2025") — use
searchto fetch top-K relevant pages. - The user attaches a PDF or image and the next step requires reading its contents.
- A task description contains URLs that need to be resolved into actual content before the assignee can act.
CLI Reference
Web content
# Single URL → HQCC
cloud load https://example.com
cloud load https://example.com --format raw # exact wording / code / tables (more tokens)
# Batch (up to 50 URLs)
cloud load https://a.com https://b.com https://c.com
# Search → load (fetch top-K relevant pages)
cloud search "AI agent frameworks 2025"
cloud search "topic" -k 10
# Pre-save to cache (e.g. content you scraped elsewhere)
cloud context save https://example.com "compressed content"
Documents (OCR)
# Fast mode — digital PDFs, clean images
cloud parse https://example.com/paper.pdf
# Hi-res — scans, handwriting, complex layouts
cloud parse https://example.com/scan.pdf -m hires
# Async — long parses return a task id; poll until ready
cloud parse <url> --async # → returns parseTaskId
cloud parse-status <parseTaskId> # check progress
cloud parse-result <parseTaskId> # fetch finished markdown
Supported parse formats: PDF, PNG, JPG, TIFF, BMP, GIF, WEBP.
Workflow
For web URLs
- Decide: single URL load? Batch? Or search query?
- Default to
--format hqcc(compressed). Userawonly when exact wording, code, or tables are needed. - Run
cloud load/cloud searchand capture: source URLs, titles, cache status, cost. - Base downstream reasoning only on the returned content. If a load failed, say so; don't pretend you read 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.
- 8d ago First seen · 106 lines · 79 tokens per session scan A 17bfe67faa32
ingest is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,410 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 1,230 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.
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