New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Onboarding

by ginlix-ai in ginlix-ai/LangAlpha

First-time setup. An agent skill from ginlix-ai/LangAlpha.

Apache-2.0Auto-check passed

Install Onboarding

skills CLI
$ npx skills add ginlix-ai/LangAlpha --skill onboarding -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ginlix-ai/LangAlpha onboarding --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ginlix-ai/LangAlpha.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/langalpha_service/skills/onboarding .claude/skills/onboarding && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
onboarding
GitHub stars
1.8k
Token cost
~960 tokens
SKILL.md length
587 words
Files
1
Skills in repo
37
Repo updated
First seen
Licence
Apache-2.0

At a glance

First-time setup. An agent skill from ginlix-ai/LangAlpha.

  • Works in 5 steps: Start from what is there → Import from the brokerage → Learn the markets they follow → …
  • SKILL.md covers 1. Start from what is there, 2. Import from the brokerage, 3. Learn the markets they follow and 4. Who they are, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Onboarding is an agent skill from ginlix-ai/LangAlpha. First-time setup. Import holdings and watchlists from a connected brokerage, learn the markets the user follows, and save it all to their profile files.

Its SKILL.md is about 960 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Claude Code for Financial Market. The licence is Apache-2.0.

Example prompts

  • “/onboarding”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Start from what is there
  2. Import from the brokerage
  3. Learn the markets they follow
  4. Who they are
  5. Finish

What it can do on your machine

Read from SKILL.md and the folder at commit e05bd91. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Onboarding loads about 960 tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 587 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~960

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ginlix-ai/LangAlpha at commit e05bd91, republished under its Apache-2.0 licence (© ginlix-ai). 587 words, ~960 tokens.

Download SKILL.mdSave it as .claude/skills/onboarding/SKILL.md (or your agent's skills folder).
name
onboarding
description
First-time setup. Import holdings and watchlists from a connected brokerage, learn the markets the user follows, and save it all to their profile files.

Onboarding

Set up a new user's profile in one short conversation. Everything you learn goes into the files under .agents/user/profile/, which the app and every later conversation read. Read .agents/user/profile/README.md before your first write: it has each file's schema and the reasons a save is refused.

1. Start from what is there

Read user.json, portfolio.json, watchlist.json and preference.json. Skip any question they already answer, and keep the rows the user already has when you write a file back.

2. Import from the brokerage

A connected brokerage is listed in <mcp-servers>. Read its tool docs, then fetch every account's positions and every watchlist in one execute_code call, printing a compact summary rather than the raw answers.

  • Show the user what you found: the accounts, how many positions, the largest few, and the watchlists. Ask with AskUserQuestion whether to import all of it, pick, or skip.
  • Holdings go into portfolio.json. Set account_name to the brokerage's name followed by the account's last four digits (moomoo 1234), or the name alone when the brokerage reports no account number, so the name stays the same when a later import finds more accounts. Replace the rows with that account_name and keep every other row, so a later import updates the account instead of doubling it.
  • Map each position to an instrument_type (stock, etf, option, crypto, bond, fund) and carry quantity, average cost and currency as the brokerage reports them. Leave out a field the brokerage does not give rather than guessing it.
  • Each brokerage watchlist becomes its own list in watchlist.json, named after it with the brokerage in front (moomoo: Favorites), and is replaced the same way on a later import. The dashboard shows only the default list, so when the user's default list is empty, make the largest imported list the default.
  • If a call fails or there is nothing to import, say so in one line and move on.

With no brokerage connected, ask which stocks the user owns or follows. Add them to the default watchlist, or to the portfolio when they give a quantity.

Show full SKILL.md (245 more words)Show less

3. Learn the markets they follow

Ask with AskUserQuestion, a few related choices at a time, each leaving room for their own answer:

  • Markets: regions (US, Hong Kong, mainland China, Japan, Europe) and asset classes (stocks, ETFs, options, crypto, bonds).
  • Focus: the sectors and themes they watch, and any they avoid.
  • Style and horizon: growth, value, income or trading; holding for days, months or years.
  • Risk: how deep a drawdown they can sit through, and what shaped that.
  • Answers: brief or in depth, charts or text.

Save their words, not a label: "moderate, but sold everything in March 2020 and regretted it" tells a later conversation more than "moderate". Markets, focus and style go in investment_preference, risk in risk_preference, and how they want answers in agent_preference.

4. Who they are

If user.json has no name, ask what to call them. Confirm the timezone it holds, or ask for their city when it is empty: their turns and new automations run in that zone.

5. Finish

  1. Sum up in a few lines what you saved.
  2. Set onboarding_completed to true in user.json.
  3. Offer a first piece of work built on what they told you, tied to their largest holding or main market. If they accept, create a workspace for it with manage_workspaces(action="create") and hand the question to its analyst with delegate_to_analyst.

Keep it a conversation, not a form: combine related questions, follow up when an answer is vague, and let the user skip anything.

© ginlix-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/langalpha_service/skills/onboarding of ginlix-ai/LangAlpha.

Open the folder on GitHubat commit e05bd91

Compare with similar skills

Onboarding next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Onboarding compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Onboarding this skillginlix-ai/LangAlpha1.8k—~960Automated safety check: PassApache-2.0
ConnectComposioHQ/awesome-claude-skills77k3 repos~987Automated safety check: PassNone
Importasgeirtj/system_prompts_leaks69k—~3.5kAutomated safety check: PassCC0-1.0
Onboardalirezarezvani/claude-skills28k—~1.3kAutomated safety check: PassMIT
Codebase Onboardingaffaan-m/ECC276k3 repos~2kAutomated safety check: PassMIT
Connection Import WasmfeigeCode/navop1.8k—~2.7kAutomated safety check: PassCustom licence

Similar skills

  • Connect

    ComposioHQ/awesome-claude-skills

    Connect Claude to any app. An agent skill from ComposioHQ/awesome-claude-skills.

    77k GitHub starsUsed in 3 repos~987 tokens
    Productivity & AutomationAuto-check passed

  • Import

    asgeirtj/system_prompts_leaks

    Handle explicit /import requests for read-only transcript recovery and a resume checkpoint, or continue work from other coding agents and unnamed artifacts.

    69k GitHub stars~3.5k tokensUpdated yesterday
    Agent WorkflowsAuto-check passed

  • Onboard

    alirezarezvani/claude-skills

    /cs:onboard — Founder interview that populates ~/.claude/company-context.md using the canonical 7-dimension cs-onboard schema.

    28k GitHub stars~1.3k tokensUpdated 1 mo ago
    Auto-check passed

  • Codebase Onboarding

    affaan-m/ECC

    Analyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md.

    276k GitHub starsUsed in 3 repos~2k tokens
    DevelopmentAuto-check passed

  • Connection Import Wasm

    feigeCode/navop

    A skill your agent uses when implementing, debugging, packaging, or host-enabling onetcli WASM connection importers such as DBeaver, Navicat, Navicat Lite, Termius, connection-import.wit components…

    1.8k GitHub stars~2.7k tokensUpdated today
    DevelopmentAuto-check passed

  • Onboarding

    sickn33/agentic-awesome-skills

    When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value.

    47k GitHub starsUsed in 1 repo~1.8k tokens
    Auto-check passed

More from ginlix-ai/LangAlpha

All 37 skills in this repo

  • Investment Deck Check

    ginlix-ai/LangAlpha

    Quality-checks an investment deck in .pptx form before it goes out: number consistency, chart and narrative alignment, source coverage, language and a circulation verdict.

    1.8k GitHub stars~3.7k tokensUpdated today
    Auto-check passed

  • Equity Initiation Report

    ginlix-ai/LangAlpha

    Produces a first-time equity research initiation report in five tasks: company research, financial model, valuation, charts and a DOCX report.

    1.8k GitHub stars~3.8k tokensUpdated today
    Auto-check passed

  • Three-Statement Model Builder

    ginlix-ai/LangAlpha

    Builds or repairs an integrated income statement, balance sheet and cash flow model in Excel with live formulas, supporting schedules, scenarios and a Checks sheet.

    1.8k GitHub stars~5.4k tokensUpdated today
    Auto-check passed

  • Financial Model Checker

    ginlix-ai/LangAlpha

    Audits an existing Excel financial model without editing it, checking structure, formulas, integrity identities and source tie-out, and ends in a prioritized issue log.

    1.8k GitHub stars~4.2k tokensUpdated today
    Auto-check passed

  • DCF Model Builder

    ginlix-ai/LangAlpha

    Builds a live Excel DCF valuation workbook with free cash flow projections, WACC, terminal value, three scenarios, sensitivity grids and a reverse DCF.

    1.8k GitHub stars~7.7k tokensUpdated today
    Auto-check passed

  • Word Documents for Human Review

    ginlix-ai/LangAlpha

    Builds Word files with python-docx, edits existing ones in place with tracked changes and comments, then renders and validates the result.

    1.8k GitHub stars~4.8k tokensUpdated today
    Auto-check passed

Questions about Onboarding

What does Onboarding do?

First-time setup. An agent skill from ginlix-ai/LangAlpha. Onboarding is an agent skill from ginlix-ai/LangAlpha. First-time setup.

How do I install Onboarding in Claude Code?

Run `npx skills add ginlix-ai/LangAlpha --skill onboarding -a claude-code`. Or copy the skill folder (plugins/langalpha_service/skills/onboarding in ginlix-ai/LangAlpha) into .claude/skills/onboarding in your project. Claude Code loads it when a task matches its description.

How do I install Onboarding in Codex?

Run `npx skills add ginlix-ai/LangAlpha --skill onboarding -a codex`. Or copy the skill folder (plugins/langalpha_service/skills/onboarding in ginlix-ai/LangAlpha) into .agents/skills/onboarding in your project. Codex loads it when a task matches its description.

Can I use Onboarding in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ginlix-ai/LangAlpha --skill onboarding -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/onboarding, .gemini/skills/onboarding, .github/skills/onboarding and .opencode/skills/onboarding in your project.

What does Onboarding need to run?

SKILL.md names no scripts, command-line tools or credentials: Onboarding is instructions for the agent only.

Does Onboarding access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Onboarding safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Onboarding use?

Onboarding is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Onboarding use?

About 960 tokens (SKILL.md is roughly 3.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Onboarding?

Skills that share tags, products or a category with Onboarding: Connect (ComposioHQ/awesome-claude-skills, 77k stars), Import (asgeirtj/system_prompts_leaks, 69k stars), Onboard (alirezarezvani/claude-skills, 28k stars) and Codebase Onboarding (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Onboarding?

ginlix-ai (a GitHub organization) maintains it in ginlix-ai/LangAlpha, which has 1,811 GitHub stars. The repository holds 37 skills in this directory. The repository was last updated on October 9, 2026.

Source: ginlix-ai/LangAlpha on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.