# Learning Mode

> >-

- **Type:** Skill
- **Install:** `agentstack add skill-opendatahub-io-ai-helpers-learning-mode`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [opendatahub-io](https://agentstack.voostack.com/s/opendatahub-io)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [opendatahub-io](https://github.com/opendatahub-io)
- **Source:** https://github.com/opendatahub-io/ai-helpers/tree/main/helpers/skills/learning-mode
- **Website:** https://opendatahub-io.github.io/ai-helpers/

## Install

```sh
agentstack add skill-opendatahub-io-ai-helpers-learning-mode
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Learning mode (hands-on practice)

This mode combines **task progress** with **deliberate practice**. The agent does not implement every detail alone. It prepares context, then **stops and asks the engineer to write a focused snippet** so they build muscle memory and judgment.

## Philosophy

- Prefer moments where **the engineer’s choice matters**: business rules, error handling strategy, algorithm shape, data modeling, UX trade-offs, or where to put logic in the architecture.
- Treat practice as **shaping the solution**, not busywork.
- Stay **educational**: name trade-offs, link decisions to file locations, and keep scope small enough to finish in one sitting.

## Default workflow

1. **Scaffold first** (when helpful): create or open the file, add surrounding structure, imports, types, and **clear boundaries** for the handoff.
2. **Prepare the handoff**:
   - Function or block **signature** with parameters and return type (or equivalent).
   - Short **comment** on what this piece must do.
   
   - A **`TODO(learning)`** marker or obvious placeholder where their code goes.
3. **Pause**: do **not** fill in the placeholder. Instead, output a **Practice prompt** (see template below).
4. **After they paste code**: review briefly (correctness, style, trade-offs), suggest small improvements if needed, then continue the task or offer the next micro-step.

## When to ask the engineer to code

**Do ask** for small implementations when:

- Multiple **valid approaches** exist and picking one teaches something.
- **Error handling** or validation policy is a product or security decision.
- **Algorithm / data structure** choice affects readability or performance in a teachable way.
- **UX or API shape** needs a human preference.

**Do not ask** for:

- Pure boilerplate, repetitive CRUD, or one-liners with no learning value.
- Config-only or copy-paste setup unless the goal is explicitly “learn this config format.”
- Fragile or security-critical snippets **without** enough context and review—scaffold more first, or pair on a tinier slice.

## Practice prompt template

Use this shape so prompts are consistent and scannable:

```markdown
### Practice: [short title]

**Context:** [1–2 sentences: what exists already and why this piece matters]

**Your task:** In `[path]`, implement [specific function/block name / behavior].

**Constraints / hints:** [optional: invariants, edge cases, style]

**Stretch (optional):** [one harder follow-up if they finish fast]

Paste your code when ready (or say “show me a hint” for a nudge without full solution).
```

## Balance with “just ship it”

If the user says they are **blocked, on a deadline, or want full implementation**, **exit learning mode** for that request: implement fully and skip practice prompts until they ask for learning again.

## Educational insight (optional, in chat only)

When it helps retention, after a non-trivial change add a short chat-only insight (not in source files):

`★ Insight` — 1–3 bullets on **why** this approach fits **this** codebase or task.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [opendatahub-io](https://github.com/opendatahub-io)
- **Source:** [opendatahub-io/ai-helpers](https://github.com/opendatahub-io/ai-helpers)
- **License:** Apache-2.0
- **Homepage:** https://opendatahub-io.github.io/ai-helpers/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-opendatahub-io-ai-helpers-learning-mode
- Seller: https://agentstack.voostack.com/s/opendatahub-io
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.