self-improvement

name	self-improvement
description	AI self-improvement and reflection skill. Run weekly or on-demand to audit capabilities, identify gaps, evolve identity documents, prune stale skills/memory, and propose new capabilities. Triggers on schedule, when the user asks for a self-audit, or when the AI notices recurring limitations.
compatibility	Created for Zo Computer
metadata	{"author":"skeletorjs","category":"Community"}

Self-Improvement

Structured reflection and evolution loop. Turns self-improvement from ad hoc to deliberate.

Setup

1. Install this skill to Skills/self-improvement/
2. Create a weekly scheduled agent (e.g., Sundays at 10 AM) to run the full loop
3. Ensure Records/Reflections/ directory exists for reports
4. Works best with the supermemory skill installed for memory hygiene checks

When to Run

• Scheduled: Weekly via agent (recommended: Sunday morning)
• Manual: When the user asks for a self-audit, reflection, or capability review
• Reactive: When the AI notices a pattern of limitation mid-conversation (run the relevant section, not the full loop)

The Loop

Run these phases in order. Each phase produces findings. At the end, synthesize into actions.

Phase 1: System Audit

Run the audit script to gather current state:

python3 /home/workspace/Skills/self-improvement/scripts/audit.py full

This returns: identity file stats (if any), skills inventory, workspace structure, records, and a memory sample.

Review the output for:

• Skills that look stale or redundant
• Missing directories or broken structure
• Workspace clutter

Phase 2: Skills Review

For each skill in the inventory, assess:

1. Still relevant? Does the user still use or need this? Check memory for recent mentions.
2. Working? Run a quick sanity check on scripts if they have CLI help (--help).
3. Up to date? Are there API changes, new capabilities, or better approaches?
4. Well-documented? Is the SKILL.md clear enough that a fresh AI instance could use it?

Flag skills as: healthy, needs-update, potentially-stale, or broken.

Phase 3: Capability Gap Analysis

Think about what the user has asked for recently that was hard, slow, or impossible. If Supermemory is available:

python3 /home/workspace/Skills/supermemory/scripts/memory.py search \
  --query "couldn't do, limitation, manual, workaround, slow" --limit 10

Also consider:

• Are there integrations the user uses that aren't connected or automated?
• Are there repetitive tasks that could be skills?
• Are there tools or APIs that would unlock new capabilities?

Produce a ranked list of capability gaps with effort estimates (small/medium/large).

Phase 4: Identity Reflection

If the user has persona/identity files for the AI, read them in full. Consider:

1. Accuracy: Does the voice description still match how the AI actually communicates?
2. Completeness: Are there aspects of the working relationship that have evolved but aren't captured?
3. Principles: Are any principles being violated regularly? Should new ones be added?
4. Boundaries: Are the guardrails still right? Too tight? Too loose?

Do NOT edit identity files directly. Propose changes and get the user's approval.

Phase 5: Memory Hygiene

If Supermemory is available, search for potential issues:

python3 /home/workspace/Skills/supermemory/scripts/memory.py search --query "decided" --limit 20
python3 /home/workspace/Skills/supermemory/scripts/memory.py search --query "prefers" --limit 20

Check for:

• Contradictory facts (the graph handles most of this, but flag obvious ones)
• Outdated decisions that should be revisited
• Important context that's missing
• Facts saved too vaguely

Phase 6: Action Plan

Synthesize findings into three categories:

1. Do now (internal improvements that don't need user approval): fix broken scripts, update stale skill docs, clean workspace clutter, save missing context to memory
2. Propose to user (changes that need approval): new skills to build, identity edits, capability investments, workflow changes
3. Watch (patterns to monitor, not yet actionable): emerging gaps, things that might matter later

Delivery

Scheduled Run (weekly)

1. Execute phases 1-6
2. Write the full report to Records/Reflections/YYYY-MM-DD-reflection.md
3. Execute all "do now" items immediately
4. Text the user a summary (2-4 lines max):

send_sms_to_user(
  message="Weekly self-audit done. [X] skills healthy, [Y] need attention. Top proposal: [brief]. Full report in Records/Reflections/. Want to discuss?"
)

5. If Supermemory is available, save the reflection summary:

python3 /home/workspace/Skills/supermemory/scripts/memory.py save \
  --content "Weekly self-improvement audit [DATE]: [key findings and actions taken]" \
  --tags "reflection,self-improvement"

Manual/Reactive Run

Skip the report file and SMS. Just run the relevant phases and discuss findings inline.

Report Format

# Self-Improvement Reflection -- YYYY-MM-DD

## System State
<audit summary>

## Skills Health
| Skill | Status | Notes |
|-------|--------|-------|
| ... | healthy/needs-update/stale/broken | ... |

## Capability Gaps
1. [Gap] -- effort: small/medium/large -- impact: high/medium/low
2. ...

## Identity Notes
<observations about drift, accuracy, proposed changes>

## Memory Hygiene
<issues found, corrections made>

## Actions Taken
- [x] <things done immediately>
- [ ] <proposals for user>

## Watch List
- <patterns to monitor>

Guardrails

• Never edit identity/persona files without the user's explicit approval
• Never delete skills without confirmation
• "Do now" items are limited to: documentation fixes, script bug fixes, workspace tidying, memory saves
• New skill creation is allowed (per autonomy clauses) but should be mentioned in the report