# Intent

## Project Purpose

This kit turns Codex or a compatible IDE agent into a structured onboarding and recruiting-training assistant for TTC recruiting interns.

The package must support two connected jobs:

- guide a new learner through Day 1 landing and recruiting intern training one step at a time;
- let maintainers update modules, source materials, and search indexes without losing structure or inventing unsupported process.

## Target Users

- New recruiting interns who need guided onboarding, account setup, learning tasks, and first-day completion checks.
- Mentors and Recruiting Ops owners who need a repeatable training and acceptance flow.
- Future AI agents that need enough local context to answer questions, modify modules, ingest source materials, and verify changes safely.

## Success Signals

- A learner can start with `start.prompt.md`, complete one confirmed step at a time, and receive a Day 1 Landing Summary at completion.
- Random questions are answered from indexed local evidence with citations, not from memory.
- PDFs, images, tables, linked docs, and external links are represented in `sources/source-manifest.yaml` and searchable text files.
- Maintainers can add, inspect, update, disable, and re-index modules by following `MAINTAINERS.md`.
- Future agents can understand the purpose, rules, workspace, current state, verification, and systemization backlog without re-reading the entire source corpus.

## Non-Goals

- This is not a web product, browser extension, or desktop app yet.
- This package must not store real passwords, verification codes, private keys, full identity documents, bank card numbers, or raw signed Feishu asset URLs.
- This package should not scrape or expose private material beyond what the authorized local Feishu/Lark tools can access.
- This package should not hand-edit generated search indexes.

## Strategic Notes

The intended architecture is local-first and portable: a zip can be handed to another user, who opens the folder in Codex and starts the skill or prompt.

The deeper product direction is an AI-native onboarding/RAG operating kit: modules define guided workflows, sources preserve evidence, scripts make maintenance deterministic, and `.ai/context/` preserves long-horizon project understanding.
