Learning Mode

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.

1. Pause: do not fill in the placeholder. Instead, output a Practice prompt (see template below).
2. 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:

### 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
• Source: 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.