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 dinhtungdu/tangerine --skill platform-setupgit clone --depth 1 https://github.com/dinhtungdu/tangerineWrote 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/dinhtungdu/tangerine/platform-setup)<a href="https://agentmods.dev/skills/dinhtungdu/tangerine/platform-setup"><img src="https://agentmods.dev/badge/skills/dinhtungdu/tangerine/platform-setup/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/dinhtungdu/tangerine/platform-setup"><img src="https://agentmods.dev/badge/skills/dinhtungdu/tangerine/platform-setup.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 | $0.00031 | $0.04858 |
| Opus 5.5 | $0.00012 | $0.01943 |
| Sonnet 5.5 | $0.00006 | $0.00972 |
| Haiku 4.5 | $0.00003 | $0.00486 |
Grade D, and why
platform-setup scanned grade D with 3 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 2mo 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt-get update && sudo apt-get install -y git curl jq dtach Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://bun.sh/install | bash Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://bun.sh/install | bash How it starts
The opening of the file, as written. The whole thing — 439 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tangerine Init Skill
Set up the Tangerine coding agent platform. Tangerine can run directly on the host machine OR inside a Lima VM. Either way: one machine runs the server, dashboard, agents, and all project repos.
Architecture (v1)
Host (laptop) — or VM (Lima)
├── tangerine server + dashboard (:3456)
├── {workspace}/project-a (main clone)
│ /project-a--wt-1 (task worktree)
│ /project-a--wt-2 (task worktree)
├── {workspace}/project-b, project-b--wt-1, ...
├── ACP agents — local stdio processes
└── Apache, MariaDB, tools — shared
{workspace} defaults to ~/tangerine-workspace but is configurable via config.workspace in ~/tangerine/config.json.
No SSH tunnels, no per-project VMs. One machine, all projects.
Routing
Before doing anything, figure out which mode applies:
- Check if Tangerine is already running (
tangerine --versionor~/tangerine/config.jsonexists) → Mode 3 (add a project). - Check if we're inside a Lima VM (
limactl infofails oruname -ncontains "lima") → Mode 1 (set up Tangerine here). - Check if the user is on macOS (
uname -s=Darwin) andlimactlis available → ask: "Do you want to run Tangerine directly on this machine or inside a Lima VM?"- Direct → Mode 1
- VM → Mode 2
- Otherwise (Linux host, no VM) → Mode 1.
Setup Modes
Two modes: Tangerine Setup (install and run Tangerine on the current machine) and VM Creation (create a Lima VM on macOS, then run Tangerine Setup inside it — the setup steps are identical once inside).
Mode 1: Tangerine Setup (host or inside VM)
User is on the machine where Tangerine will run — either their host or inside a Lima VM. You help them:
-
Install prerequisites
Detect OS:
uname -s→Darwin= macOS,Linux= Linux.macOS (use Homebrew; install brew first if missing):
brew install git gh jq dtach node # Bun: curl -fsSL https://bun.sh/install | bash
What ships with it
4 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.
- 2mo ago First seen · 439 lines · 31 tokens per session scan D 6089a93618b1
platform-setup is a skill published in the GitHub repository dinhtungdu/tangerine (5 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 4,858 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it D with 3 findings (asks for root, downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
platform-setup compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| platform-setupdinhtungdu/tangerine | Skill | 5 | 5mo ago | MIT | D | 31 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |