Agent Skills: Learning mode (hands-on practice)

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UncategorizedID: opendatahub-io/ai-helpers/learning-mode

Install this agent skill to your local

pnpm dlx add-skill https://github.com/opendatahub-io/ai-helpers/tree/HEAD/helpers/skills/learning-mode

Skill Files

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helpers/skills/learning-mode/SKILL.md

Skill Metadata

Name
learning-mode
Description
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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.
    <!-- skillsaw-disable content-placeholder-text -->
    • 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:

### 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.