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 grill-megit 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/grill-me)<a href="https://agentmods.dev/skills/androoagi/starnet/grill-me"><img src="https://agentmods.dev/badge/skills/androoagi/starnet/grill-me/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/grill-me"><img src="https://agentmods.dev/badge/skills/androoagi/starnet/grill-me.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 | $0.00041 | $0.01090 |
| Opus 5.5 | $0.00016 | $0.00436 |
| Sonnet 5.5 | $0.00008 | $0.00218 |
| Haiku 4.5 | $0.00004 | $0.00109 |
Grade A, and why
grill-me 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grill Me
Stress-test a plan through structured adversarial questioning before any code is written. Model the plan as a design tree: every decision branches into the decisions that hang off it. Interview the Commander in rounds until every branch is resolved and nothing is silently assumed.
When to use
- The Commander says "grill me", "interview my plan", or "stress test this idea".
- Before complex work: auth flows, schema changes, migrations, payments.
- A plan has unresolved decisions or seems vague.
- Before splitting work across the crew.
Do NOT use it for reviewing existing code (use a code-review recipe) or for simple one-off tasks.
Prerequisites
None. The skill works on any plan or raw idea.
Core mechanic: frontier rounds
Map the plan as a design tree. The frontier is every decision whose prerequisites are already settled: the questions you can ask NOW without guessing at answers you have not heard yet.
Work in rounds: ask the whole current frontier in one message, numbered, each question carrying your recommended answer. Then wait. A question whose answer depends on another question still open in this round belongs to a LATER round, not this one.
Format each round like this:
Q1 — <question title>: <question body, options if relevant>
Recommendation: <your recommended answer + one-line why>
Q2 — <question title>: <question body>
Recommendation: <...>
Each answer reshapes the tree: settled decisions push the frontier outward and unblock dependent questions. Recompute the frontier and ask the next round.
Facts are your job; decisions are the Commander's. When a frontier question needs a fact from the environment (codebase, files, config, docs), find it yourself with fs.search, fs.read, and shell.exec (for example git log in the project), or hand a heavy exploration to a sub-agent with team.spawn. Never ask the Commander for anything you could look up. Do not block on an exploration: only the questions downstream of it wait; ask the rest of the frontier now.
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 · 94 lines · 41 tokens per session scan A 6249754d66af
grill-me is a skill published in the GitHub repository androoAGI/starnet (1,468 stars, last pushed 3d ago), licensed MIT. It adds 41 tokens to every session and 1,090 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.