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 prismer-songseegit 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/prismer-songsee)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/prismer-songsee"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/prismer-songsee/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/prismer-songsee"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/prismer-songsee.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 | $0.00022 | $0.00766 |
| Opus 5.5 | $0.00009 | $0.00306 |
| Sonnet 5.5 | $0.00004 | $0.00153 |
| Haiku 4.5 | $0.00002 | $0.00077 |
Grade A, and why
prismer-songsee scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 29d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
songsee
Generate spectrograms and multi-panel audio feature visualizations from audio files.
Prerequisites
Requires Go:
go install "github.com/steipete/songsee/cmd/songsee@${SONGSEE_VERSION:?Set an exact reviewed release or commit}"
Optional: ffmpeg for formats beyond WAV/MP3.
Quick Start
# Basic spectrogram
songsee track.mp3
# Save to specific file
songsee track.mp3 -o spectrogram.png
# Multi-panel visualization grid
songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux
# Time slice (start at 12.5s, 8s duration)
songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg
# From stdin
cat track.mp3 | songsee - --format png -o out.png
Visualization Types
Use --viz with comma-separated values:
| Type | Description |
|---|---|
spectrogram |
Standard frequency spectrogram |
mel |
Mel-scaled spectrogram |
chroma |
Pitch class distribution |
hpss |
Harmonic/percussive separation |
selfsim |
Self-similarity matrix |
loudness |
Loudness over time |
tempogram |
Tempo estimation |
mfcc |
Mel-frequency cepstral coefficients |
flux |
Spectral flux (onset detection) |
Multiple --viz types render as a grid in a single image.
Common Flags
| Flag | Description |
|---|---|
--viz |
Visualization types (comma-separated) |
--style |
Color palette: classic, magma, inferno, viridis, gray |
--width / --height |
Output image dimensions |
--window / --hop |
FFT window and hop size |
--min-freq / --max-freq |
Frequency range filter |
--start / --duration |
Time slice of the audio |
--format |
Output format: jpg or png |
-o |
Output file path |
Notes
- WAV and MP3 are decoded natively; other formats require
ffmpeg - Output images can be inspected with
vision_analyzefor automated audio analysis - Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 29d ago First seen · 92 lines · 22 tokens per session scan A 552242af35ee
prismer-songsee is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,363 stars, last pushed 6d ago), licensed MIT. It adds 22 tokens to every session and 766 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 2026-09-29.
Other skills, from other repositories
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.
float-optimizer
A document-layout helper for LaTeX, the system commonly used to typeset research papers. It adjusts how figures and tables are placed near the text that refers to them.
Automate ML research loops with ARIS skills
Use ARIS to run Markdown-based agent skills for literature review, idea discovery, cross-model critique, experiment planning, and paper-writing support.
reference-verification
Deterministic anti-hallucination gate for cited references. Resolves each entry in a manuscript .bib file against Crossref, OpenAlex, and arXiv; flags fabricated (identifier resolves to nothing), mismatched (title/author/year disagree), unverifiable (no identifier), unchecked (offline cache miss — never silently OK)…
template-literature-synthesis
LLM prompt blocks for per-paper and corpus literature synthesis after a search pipeline. USE WHEN synthesizing arXiv/Crossref results, writing per-paper notes, thematic clusters, gap analysis from a literature corpus, or wiring templatesearchproject synthesis — even without naming infrastructure.llm templates.
infrastructure-documentation
Figure metadata, deterministic generated-figure registries, and source-derived API documentation. Use when publishing figure inventories, validating generated image files, or maintaining API glossaries.