literacy-improvements

name	literacy-improvements
description	Use when generating a prioritised improvement plan after an AI literacy assessment, or when a user knows their current level and wants to know what to do next — maps gaps to specific plugin commands and skills, grouped by target level, with accept/skip/defer for each item

Literacy Improvements

Generate a prioritised improvement plan that maps assessment gaps to specific plugin commands and skills. Each improvement is presented interactively — the user chooses to accept, skip, or defer.

This skill is invoked by /assess after Phase 5 (workflow recommendations), but can also be used standalone when the user knows their current level.

Input

The skill needs two things:

• Current level (L0–L5) — from an assessment or stated by the user
• Gaps (optional) — from the assessment document's Gaps section. If not provided, the skill infers gaps by checking which items in the improvement mapping are not yet present in the repository.

Process

Step 1: Confirm Current Level

If invoked from /assess, the level is already known — skip this step.

If invoked standalone, ask the user:

What is your current AI literacy level?

If you don't know, run /assess first — it takes fifteen minutes
and gives you an evidence-based answer.

1. Level 0 — Awareness
2. Level 1 — Prompting
3. Level 2 — Verification
4. Level 3 — Habitat Engineering
5. Level 4 — Specification Architecture

Level 5 teams do not need this skill — they are already at the top.

Step 2: Ask Target Level

Present the levels above the current one:

You're currently at Level N (Level Name).

How far would you like to improve?

1. Level N+1 — [name] (recommended next step)
2. Level N+2 — [name]
...up to Level 5

The default recommendation is always the next level. Higher targets include all intermediate levels — choosing L4 from L2 means doing L2→L3 improvements first, then L3→L4.

Step 3: Generate Prioritised Plan

Read the mapping from references/improvement-mapping.md. For each level transition between current and target:

1. Check existing state — for each gap in the mapping, verify whether it is already closed. Check the file system:

   • Does HARNESS.md exist? Does it have enforced constraints?
   • Does CLAUDE.md exist?
   • Are there CI workflows?
   • Does REFLECTION_LOG.md have recent entries?
   • Do specs/ directories exist? Use the observable evidence checks from the ai-literacy-assessment skill as guidance.

2. Filter to open gaps — remove items where the file or configuration already exists and is active.

3. Assign priority using this heuristic:

   • High — foundational. Nothing else at this level works without it. Examples: HARNESS.md for L3, CI pipeline for L2.
   • Medium — valuable and closes a real gap, but other items don't depend on it. Examples: secret scanning, GC rules.
   • Low — nice-to-have at this level, or the gap is partially closed. Examples: Docker scanning when no Docker is used, convention sync when only one AI tool is in use.

4. Group by level transition — "To reach Level 3" then "To reach Level 4".

5. Order within each group — high priority first, then medium, then low.

Step 4: Present Plan Item by Item

For each improvement, present one at a time:

Improvement 1/N (Level M — Level Name):
  Gap: [what is missing]
  Action: Run [command] or use [skill]
  Priority: High — [one-sentence rationale]

  Accept / Skip / Defer?

Handle each response:

• Accept — execute the command or invoke the skill immediately. Wait for it to complete before presenting the next item.
• Skip — remove from plan. Do not ask again this session.
• Defer — keep in plan but do not execute. Record as deferred for the next assessment to pick up.

If executing a command produces further interactive prompts (e.g., /harness-init asks about features), let them run naturally. Resume the improvement plan after the command completes.

Step 5: Record the Plan

If an assessment document exists for today (assessments/YYYY-MM-DD-assessment.md), append an Improvement Plan section:

## Improvement Plan

- Current level: LN
- Target level: LM
- Improvements accepted: N
- Improvements skipped: N
- Improvements deferred: N
- Commands executed: [list of commands/skills that ran]

### Accepted

| Gap | Action | Result |
| --- | --- | --- |
| No HARNESS.md | /harness-init | HARNESS.md created with 4 constraints |

### Skipped

| Gap | Reason |
| --- | --- |
| No Docker scanning | No Docker in this project |

### Deferred

| Gap | Action | Reason |
| --- | --- | --- |
| No fitness functions | fitness-functions skill | Team wants to stabilise L3 first |

If no assessment document exists for today, write the plan to assessments/YYYY-MM-DD-improvements.md as a standalone document.

Step 6: Summary

Print a summary:

Improvement plan complete.

  Current level: L2 (Verification)
  Target level: L3 (Habitat Engineering)
  Accepted: 4 improvements
  Skipped: 1
  Deferred: 1
  Commands executed: /harness-init, /harness-constrain, /reflect, /harness-health

  Run /assess again in 3 months to measure progress.

Standalone Usage

When used outside /assess, the skill follows the same process but starts from Step 1 (confirm level). The user can say:

• "I'm at L2, what should I do next?" → confirms L2, asks target, runs plan
• "Show me what L4 requires from L2" → confirms L2, sets target L4, runs plan
• "What's left to reach L3?" → infers current level by scanning, sets target L3

Priority Heuristic

Priority	Criteria	Examples
High	Foundational — other items at this level depend on it	HARNESS.md for L3, CI pipeline for L2, specs dir for L4
Medium	Valuable — closes a real gap independently	Secret scanning, GC rules, convention extraction
Low	Conditional — depends on project context or gap is partial	Docker scanning (no Docker), convention sync (one AI tool)

Items marked as "(manual — outside plugin scope)" in the mapping are presented as guidance rather than executable actions.