How does this skill differ from simply asking an AI to explain a repository?

This skill transforms static documentation and repositories into a structured, step-by-step learning path, solving the 'information overload' problem common when onboarding to new codebases or complex technical specs.

Which AI agents or models are compatible with this skill?

The 'project learning reader' is compatible with any LLM that supports long-context window processing and file uploads, such as ChatGPT Plus, Claude 3.5 Sonnet, or Gemini 1.5 Pro.

What is included in the purchase of this skill?

Upon purchase, you receive the skill configuration file and a dedicated Quick Start guide that details how to initialize your first 'learning loop' and save progress markers.

Can I use this skill with proprietary internal documentation?

Yes, this skill is designed to process everything from public GitHub repositories and READMEs to internal system design docs, RFCs, and API documentation.

How does the skill handle long-term learning across multiple sessions?

Unlike one-off prompts, this skill creates a persistent 'Learning Record' that stores your progress, allowing you to pick up exactly where you left off in a codebase across multiple sessions.

project learning reader

Example session with this skill installed

I'm a junior dev trying to understand the 'auth-service' repo. Can you give me a map of the project and start the first section? My goal is to understand the login flow.

Read your context and instructions
Compiled the project learning reader

I have analyzed the auth-service repository. I will pace this for a junior developer level, focusing on the login flow architecture.

Project Map: auth-service

The project is organized into three main layers:

API Layer (/src/api): Handles HTTP requests and input validation.
Logic Layer (/src/services): Manages JWT generation and credential verification.
Data Layer (/src/repository): Interfaces with the user database.

Section 1 of 4: Request Entry Point
The login flow begins in src/api/routes.ts, where the POST /login endpoint is defined. This route uses a middleware to validate the request body before passing it to the controller.

Key Idea: The API layer acts as a gatekeeper, ensuring data is well-formed before any logic executes.
Unfamiliar Terms: Middleware (A function that runs before the main logic to filter requests).
Evidence: Found in src/api/routes.ts lines 12-20.
Next Step: Shall we look at how the AuthController calls the LdapService?

Next steps

Ask "What is a Middleware?" to enter Tutor Mode.
Type "next" to proceed to Section 2: Credential Verification.
Type "record" to save this section to your learning log.

Connects securely to your tools. The creator never sees your data.

About this skill

Onboarding to a complex codebase or technical specification often feels like drinking from a firehose. This skill replaces the typical wall of text with a structured, interactive learning loop that adapts to your background.

What it does

Project mapping identifies core components and architectural relationships before diving into details.
Guided reading breaks long technical docs into numbered sections with progress tracking and key summaries.
Tutor mode provides isolated explanations for specific terms including analogies and architectural context.
Active recall tests your knowledge with source-based questions to ensure actual comprehension.
Learning records generates structured snapshots of technical concepts, examples, and open questions.

How it works

Source analysis starts by defining your learning goals, background, and the specific repository or document.
Interactive walkthrough delivers one section at a time, providing evidence from the source and defining unfamiliar terms.
Deep-dive tutoring triggers whenever you ask about a specific term, switching to a dedicated tutoring format.
Review cycles use your saved records to generate spaced repetition questions and grade your understanding.

Frameworks & tools

This tool works with any technical source including GitHub repositories, README files, system design documents, and API specifications. It utilizes structured Markdown for progress markers and learning records.

Why this beats prompting it yourself

Generic prompts often produce overwhelming, non-sequential explanations that lack pedagogical structure. This skill enforces a consistent learning loop, maintains section stability across long sessions, and separates evidence from inference.

Use cases

Codebase onboarding to understand a new repository without getting lost in the weeds.
Technical specification review to break down complex RFCs or architectural diagrams.
Internal tool education to help team members learn proprietary systems.
Skill building to master specific technical terminology through active recall.

Known limitations

This skill requires a provided source document or repository to function. It cannot invent implementation details not present in the source and requires user confirmation before updating records.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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project learning reader