StarNet is a local-first desktop harness for creating AI agents and arranging them in a pixel-art space station whose layout represents their capabilities, permissions, and workflow. It lets people run real model calls and tools through multiple bounded agents, using their own provider keys or accounts and optionally messaging the station through chat services. The catalogue entries are skills that extend or support work performed by StarNet agents.
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 androoAGI/starnet --skill songwritinggit clone --depth 1 https://github.com/androoAGI/starnetWrote 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/androoagi/starnet/songwriting)<a href="https://agentmods.dev/skills/androoagi/starnet/songwriting"><img src="https://agentmods.dev/badge/skills/androoagi/starnet/songwriting/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/androoagi/starnet/songwriting"><img src="https://agentmods.dev/badge/skills/androoagi/starnet/songwriting.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 | $0.00045 | $0.02953 |
| Opus 5.5 | $0.00018 | $0.01181 |
| Sonnet 5.5 | $0.00009 | $0.00591 |
| Haiku 4.5 | $0.00005 | $0.00295 |
Grade A, and why
songwriting 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 12d 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 — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Songwriting and AI Music Prompts
Everything here is a GUIDELINE, not a rule. Art breaks rules on purpose. Use what serves the song. Ignore what does not.
When to use
- Writing a song or song lyrics.
- Writing a prompt for an AI music tool (Suno and similar).
- A parody, or new words to an existing tune.
- Fixing lyrics that read well but sing badly.
The station has no music-generation tool. You deliver text: the lyrics and the prompts. The Commander pastes them into their music tool and tells you what came out. If the Commander has not said, ask for the subject, the feeling, the genre, who is singing, and the language.
1. Song structure (pick one or invent your own)
Common skeletons: mix, modify, or throw out as needed.
ABABCB Verse/Chorus/Verse/Chorus/Bridge/Chorus (most pop and rock)
AABA Verse/Verse/Bridge/Verse (refrain-based) (jazz standards, ballads)
ABAB Verse/Chorus alternating (simple, direct)
AAA Verse/Verse/Verse (strophic, no chorus) (folk, storytelling)
The six building blocks:
- Intro: set the mood, pull the listener in
- Verse: the story, the details, the world-building
- Pre-Chorus: optional tension ramp before the payoff
- Chorus: the emotional core, the part people remember
- Bridge: a detour, a shift in perspective or key
- Outro: the farewell; can echo or subvert the rest
You do not need all of these. Some great songs are one section that evolves. Structure serves the emotion, not the other way around.
2. Rhyme, meter and sound
Rhyme types, from tight to loose:
- Perfect: lean/mean
- Family: crate/braid
- Assonance: had/glass (same vowels, different endings)
- Consonance: scene/when (different vowels, similar endings)
- Near or slant: enough to suggest a connection without locking it down
Mix them. All perfect rhymes can sound like a nursery rhyme. All slant rhymes can sound lazy. The blend is where it lives.
Internal rhyme is rhyme inside a line, not only at the ends: in "the late train rolls through the rain again", train and rain chime before the line is over.
What ships with it
2 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.
- 12d ago First seen · 296 lines · 45 tokens per session scan A c36c045e055d
songwriting is a skill published in the GitHub repository androoAGI/starnet (1,468 stars, last pushed 3d ago), licensed MIT. It adds 45 tokens to every session and 2,953 once invoked, about $0.0002 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 2026-10-04.
Other skills, from other repositories
Review a GitHub PR (via gh)
Review a specific GitHub pull request with gh — fetch the diff, fan out reviewers, consolidate, and optionally post the review. Requires the gh CLI or the GitHub MCP server.
Implement (multi-agent loop)
Orchestrate an implement -> review -> fix loop with subagents until reviewers sign off.
Best of N (parallel attempts)
Delegate N parallel subagents on the same task, then pick the best result.
Check work (verify against criteria)
Verify an implementation against acceptance criteria with a reviewer and a tester.
Commit (clean, conventional)
Stage the right changes and write a clear, conventional commit message.
Design doc (write -> review loop)
Draft a design document and iterate writer/reviewer subagents until consensus.