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Oss Learn Stack

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chiruu12
oss-learn-stack

Learn unfamiliar technologies used in a repo by studying how the repo actually uses them. Finds patterns, explains concepts in context, and points to examples in the codebase. Use when a repo uses frameworks, languages, or patterns you haven't worked with before. Not for mapping a repo's architecture or domain language. Use oss-explore-repo for that.

Overview

Publisherchiruu12
RepositoryOSS-Skills
Skill nameoss-learn-stack
Stars
62
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by chiruu12 on GitHub. Read the source before you install it.

Installation

Install the Oss Learn Stack AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chiruu12/OSS-Skills/tree/main/skills/oss-learn-stack
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chiruu12/OSS-Skills.git /tmp/OSS-Skills
mkdir -p .claude/skills
cp -r /tmp/OSS-Skills/skills/oss-learn-stack .claude/skills/oss-learn-stack
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oss Learn Stack in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Oss Learn Stack on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Oss Learn Stack is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Learn Stack

Don't learn a framework from docs alone. learn it from how THIS repo uses it. This skill finds the patterns, explains the concepts, and points to real examples in the codebase you're about to contribute to.

Purpose

oss-prep-to-contribute does a knowledge check for one issue. This skill is broader: when a repo uses tech you don't know (unfamiliar language, framework, testing tool, build system, architecture pattern), this skill teaches those concepts using the repo itself as the textbook. Faster than reading generic docs because every example is from the actual codebase you'll contribute to.

Prerequisites

  • A repo cloned locally
  • At least a basic understanding of the project (from oss-explore-repo or your own exploration)
  • An honest assessment of what you don't know

Process

1. Identify the stack

Inventory the technologies used:

bash
# Dependencies
cat package.json 2>/dev/null | jq '.dependencies, .devDependencies' 2>/dev/null
(cat requirements.txt 2>/dev/null || cat pyproject.toml 2>/dev/null) | head -40
cat go.mod 2>/dev/null | head -20
cat Cargo.toml 2>/dev/null | head -30

# Build and CI tools
ls .github/workflows/ 2>/dev/null
cat Makefile 2>/dev/null | head -20

# Testing
ls test/ tests/ __tests__/ spec/ 2>/dev/null
grep -l "jest\|pytest\|mocha\|vitest\|cargo test\|go test" * .* 2>/dev/null

# Linting and formatting
cat .eslintrc* .prettierrc* pyproject.toml tox.ini setup.cfg 2>/dev/null | head -30

Present the full stack categorized:

## Stack: {repo}

| Category | Technology |
|----------|-----------|
| Language | {e.g., TypeScript 5.x} |
| Framework | {e.g., Express.js} |
| Testing | {e.g., Vitest + Testing Library} |
| Build | {e.g., Vite} |
| CI | {e.g., GitHub Actions} |
| Linting | {e.g., ESLint + Prettier} |
| Database | {e.g., PostgreSQL via Prisma} |
| Other | {anything else notable} |

2. Find the user's gaps

Ask targeted questions:

"Here's the stack this repo uses. For each technology, tell me your comfort level:

  • Comfortable: used it in a project
  • Familiar: read about it, haven't used it
  • Unknown: never used it or only vaguely know what it is

Be honest. I'll focus teaching on the gaps. No judgment."

Process one gap at a time, starting with the most critical for the contribution they plan to make.

3. Teach from the codebase

For each identified gap, find real examples in this repo. Do NOT teach from generic docs.

bash
# Find usage of the technology in the codebase
grep -rn "{framework_import_or_pattern}" src/ lib/ \
  --include="*.ts" --include="*.py" --include="*.go" --include="*.rs" | head -20

# Find the simplest, clearest example
# (pick files that are short, well-named, and demonstrate the concept cleanly)

For each concept, present:

## {Technology}: {Concept Name}

**What it is**: {2-3 sentence explanation. just enough to read the code}

**How this repo uses it**:
- `src/handlers/auth.ts:15-30`: {what this example demonstrates}
- `src/middleware/validate.ts:8-22`: {what this example demonstrates}

**Walk-through** of `src/handlers/auth.ts:15-30`:
{line-by-line explanation of the key lines. not every line, just the ones that matter}

**Learn more**: {one link to official docs for this specific concept. not the homepage, the specific page}

Rules:

  • Every example comes from THIS repo, not generic tutorials
  • Explain in 2-3 sentences, not paragraphs
  • One external link per concept (the specific doc page, not the homepage)
  • Walk through the clearest example, skip the complex ones

4. Thinking gate: user reads the code

After explaining a concept:

"Now find one MORE example of {concept} in the codebase that I didn't show you. Read it and tell me what it does.

(This checks whether you can recognize the pattern on your own, not just understand my explanation. Hint: search for {relevant import or keyword} in the codebase.)"

If the user can find and explain an example, they understand the concept. Move to the next gap.

If the user can't find one or explains it wrong:

  • Show one more example with a different angle
  • Repeat the thinking gate

Don't move on until the user can recognize the pattern independently.

5. Connect to the contribution

Once gaps are filled, tie the learning to what the user needs to do:

"The issue you're working on touches {area} which uses {concept you just learned}. Specifically, look at {file}:{line}: that's where your change will interact with {concept}. Based on what you just learned, what do you think needs to change there?"

If the user doesn't have a specific issue yet, point to areas where the newly learned technology is most visible and suggest they explore those files on their own.

6. Build a reference sheet

The user creates their own quick-reference for while they code.

Thinking gate:

"Write a cheat sheet for yourself. 3-5 bullet points covering the key concepts you just learned, with file references for each. This is your reference while you code. Write it in YOUR words, not mine.

Example format:

  • {Concept}: {what it does}. See {file}:{line} for an example"

Review their cheat sheet. Flag anything incorrect but don't rewrite it.

7. Verify readiness

Quick comprehension check:

"Without looking at your notes:

  1. What does {key concept} do in this repo?
  2. If you saw {pattern} in a file you haven't read yet, what would you expect it to do?
  3. Where would you look in the codebase for more examples of {concept}?"

If the user can answer all three, they're ready to contribute. If not, revisit the relevant concept.

Related Skills

  • Triggered from: ← oss-prep-to-contribute: when knowledge check reveals unfamiliar technology
  • Triggered from: ← oss-explore-repo: when exploration reveals the user doesn't understand the stack
  • Next step: → oss-contribute: ready to work on the issue
  • Pairs with: oss-explore-repo (architecture understanding) + this skill (technology understanding) = full preparation

Common Rationalizations

ShortcutWhy It Fails
"I'll read the official docs instead"Docs teach the framework's happy path. This repo uses a subset of it, with local conventions layered on top. Code that is idiomatic for the docs and wrong for the repo is worse than code that is merely wrong, because it looks deliberate.
"I already know this framework"Knowing the framework is not knowing how this repo uses it. The gap that costs you a review round is always the local convention, never the language.
"I'll learn it when I hit it"You hit it halfway through the implementation, and the patch that comes out reads like it was written by someone guessing. Reviewers can tell.
"I found one example, that is the pattern"One example might be the outlier nobody has cleaned up yet. Find a second before copying it.
"I'll just ask the LLM to explain the concept"A general explanation is what the docs already gave you. The point of this skill is the explanation grounded in this codebase's own files.

Red Flags

  • User cannot find a second example of a concept unaided. the first one was not understood, only read
  • The stack list has run past ten items. that is a repo tour, not a gap list, and none of it will stick
  • User is reading framework tutorials in another tab instead of the repo's code
  • The cheat sheet is copied from documentation rather than written from the repo's usage
  • Every explanation ends with the user saying "makes sense" and asking nothing

Verification Checklist

  • Stack identified from the repo's own manifests and configs, not assumed (step 1)
  • Gaps named by the user, not guessed at on their behalf (step 2)
  • Every concept taught points at a real file:line in this repo (step 3)
  • User found an additional example of the concept without help (step 4 gate)
  • Concepts connected to the area the user's issue actually touches (step 5)
  • Cheat sheet written by the user, in their words, from this repo's code (step 6)
  • User can say what they still do not know (step 7)

Anti-patterns

  • DO NOT teach from generic docs. every example must come from THIS repo's codebase
  • DO NOT dump all concepts at once. teach one gap at a time, verify understanding, then move on
  • DO NOT skip the "find another example" gate. recognition matters more than explanation
  • DO NOT teach more than what's needed for the contribution. the goal is sufficiency, not mastery
  • DO NOT replace official documentation. point to it for depth, don't reproduce it
  • DO NOT assume the user knows prerequisite concepts. if they're unknown on React, don't assume they know JSX

Frequently asked questions

What does the Oss Learn Stack AI skill do?

Learn unfamiliar technologies used in a repo by studying how the repo actually uses them. Finds patterns, explains concepts in context, and points to examples in the codebase. Use when a repo uses frameworks, languages, or patterns you haven't worked with before. Not for mapping a repo's architecture or domain language. Use oss-explore-repo for that.

Why use Oss Learn Stack on TypingMind?

Because you install it once and use it with any model. Oss Learn Stack is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Oss Learn Stack in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chiruu12/OSS-Skills/tree/main/skills/oss-learn-stack. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Oss Learn Stack?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Oss Learn Stack?

As many as you like. As long as a model supports skills, you can use Oss Learn Stack with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Oss Learn Stack AI skill free?

Yes. It is published on GitHub by chiruu12 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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