An automated setup assistant for a research repository. It checks for a suitable Python version, installs needed packages, tests access to WRDS, and guides the repository's standard setup process.

Use it when starting the repository on a new machine, installing its dependencies, checking the local setup, or configuring WRDS access when you have an account.

It removes the manual work and guesswork involved in preparing a new computer or environment for the project. It also avoids relying on commands that may not work on every operating system.

How Alexander-M-Dickerson/ai-asset-pricing configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to Alexander-M-Dickerson/ai-asset-pricing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/Alexander-M-Dickerson/ai-asset-pricing/main/.claude/skills/onboard/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/Alexander-M-Dickerson/ai-asset-pricing

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/onboard/github.svg)](https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/onboard)

Your own site

<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/onboard"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/onboard/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/onboard"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/onboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,815 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00047	$0.01815
Opus 5	$0.00023	$0.00907
Sonnet 5	$0.00009	$0.00363
Haiku 4.5	$0.00005	$0.00181

Measured 12d ago against content hash 973f57bf268d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

onboard 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.

.claude/skills/onboard/SKILL.md · 211 lines

How it starts

The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.

User Onboarding

Use the shared repo-local onboarding flow:

tools/onboard.ps1 or tools/onboard.sh for the pre-Python cold-start step
tools/onboard_driver.py for agent orchestration once Python exists
tools/bootstrap.py as the shared Python-level audit/plan/apply engine

Do not duplicate bootstrap logic here when the repo scripts already handle it.

Hard Rules

Print Scanning your environment... before discovery.
Print Testing WRDS connectivity... before live psql checks.
Never assume bare python or bare pip are valid on Windows.
Prefer uv pip install --python "<PYTHON>" when uv is available; fall back to "<PYTHON>" -m pip install.
Prefer tools/bootstrap.py audit, the emitted bootstrap_plan, and tools/bootstrap.py apply over ad hoc local-file generation.
Treat canonical local state as external to the repo. Repo-root LOCAL_ENV.md, CLAUDE.local.md, and .claude/settings.local.json are compatibility shims only.
Let tools/bootstrap.py apply manage canonical local state. Use --write-compat-shims only for private single-user backward compatibility.
If an install path needs admin privileges or a missing package manager, stop and give exact instructions.
If a bootstrap-plan command needs approval, request it and continue with that exact command.
Ask once whether the user has a WRDS account and wants it configured now. If the answer is no, skip WRDS setup and still treat onboarding as complete once the base repo is ready.
Treat SSH key setup as optional for basic PostgreSQL access.
Never use conda install for system tools (psql, pdflatex, R, git). Use the OS package manager (winget/brew/apt). Conda is for Python packages only.

Workflow

1. Start With The Shell Entry Point

Use the repo-local shell entrypoint, not tools/bootstrap.py directly, when the machine may not have Python yet.

powershell -ExecutionPolicy Bypass -File tools/onboard.ps1

bash tools/onboard.sh

Read the full file on GitHub · 211 lines

Changes

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 · 211 lines · 47 tokens per session scan A 973f57bf268d

Subscribe to this mod's changes

onboard is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 4mo ago), licensed MIT. It adds 47 tokens to every session and 1,815 once invoked, about $0.0002 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.

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

ai-asset-pricing: Skill for Claude Code