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 agentmods add skills/first-tree-ai/first-tree/first-tree-welcomenpx skills add first-tree-ai/first-tree --skill first-tree-welcomegit clone --depth 1 https://github.com/first-tree-ai/first-treeWrote 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/first-tree-ai/first-tree/first-tree-welcome)<a href="https://agentmods.dev/skills/first-tree-ai/first-tree/first-tree-welcome"><img src="https://agentmods.dev/badge/skills/first-tree-ai/first-tree/first-tree-welcome.svg" alt="Measured on agentmods" 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 | $0.00104 | $0.10159 |
| Opus 5 | $0.00052 | $0.05079 |
| Sonnet 5 | $0.00021 | $0.02032 |
| Haiku 4.5 | $0.00010 | $0.01016 |
Grade A, and why
first-tree-welcome 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 6d 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 — 728 lines — stays where its author put it; the contents beside it link to each section on GitHub.
First Tree Welcome
Scope
Use this skill only when the chat is clearly the onboarding first chat created by First Tree, including natural messages such as "welcome aboard", "Please help me get started with First Tree", or "Please help me get settled into this team on First Tree." Do not use it for ordinary chats, PR/MR reviews, repo scans, tree writes, or maintenance work.
Messages attributed as type=integration or arriving from an external channel
such as Feishu, GitHub, or GitLab are ordinary channel work, never this
onboarding launcher. A new external-channel chat and a greeting such as "hello"
or "你好" do not establish First Tree onboarding intent.
Two look-alikes that are NOT this launcher, and one that routes by shape:
- A dedicated tree-build / single-task chat (you were placed in it, or it IS
one) — run that task's own skill (
first-tree-seedto build/seed a tree,first-tree-read/first-tree-writeas appropriate), not this launcher flow. - A repo-scan chat — it can open with the same "welcome aboard" line but then asks for a repository scan or readiness report; run its own bound scan skill.
- A production-scan FIX chat — the opening message references an
already-completed scan ("fix the launch blockers found by my production
readiness scan") with a
Repository:line, plus aMachine-readable findings: https://report.first-tree.ai/<key>.jsonline when the report key survived the handoff. Nothing needs re-scanning — never look for a scan skill. This is the launcher for a pre-selected fix: once a readable findings source exists, route by blocker count — several eligible blockers become their own fix chats, a single one is just fixed in place (see "Production-scan fix handoff" below). The onboarding greeting ("welcome aboard") only tells you the human's role for later setup gating; it does NOT change how you handle the fix. No readable findings source → ask for the report or a re-run, then stop.
What This Is
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.
- 6d ago First seen · 728 lines · 104 tokens per session scan A 18378c20108a
first-tree-welcome is a skill published in the GitHub repository first-tree-ai/first-tree (141 stars, last pushed 2d ago), licensed Apache-2.0. It adds 104 tokens to every session and 10,159 once invoked, about $0.0005 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.
Other skills, from other repositories
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
pinchtab-mcp
Use this skill when a task requires browser automation through PinchTab's MCP server connected to a remote browser instance. Covers navigation, element interaction, data extraction, form filling, multi-step flows, and session management via MCP tools.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
peekaboo
Capture and automate macOS UI with the Peekaboo CLI.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.