# Self Improve

> >

- **Type:** Skill
- **Install:** `agentstack add skill-thumperl-claude-worktrace-self-improve`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [thumperL](https://agentstack.voostack.com/s/thumperl)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [thumperL](https://github.com/thumperL)
- **Source:** https://github.com/thumperL/claude-worktrace/tree/main/skills/self-improve

## Install

```sh
agentstack add skill-thumperl-claude-worktrace-self-improve
```

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

## About

# Self-Improve: Adaptive Interaction Learning

Analyze how the user has been steering you, identify patterns, and — with confirmation — persist those learnings for all future sessions.

## Why this matters

Every correction is signal. Without this skill, the user re-trains Claude every session. This skill closes the loop by capturing preferences and making them permanent.

## When to activate

### Trigger 1: Steer count ≥ 3

A "steer" is ANY input where the user shapes HOW you work. The bar is intentionally low:

- **Explicit corrections**: "No", "Wrong", "I meant X", "Actually..."
- **Directional guidance**: "Use this for that", "Try it with X", "Go with Z approach"
- **Rephrased requests**: Same request, different words (first attempt missed)
- **Redo requests**: "Try again", "Do it differently", "Go back and..."
- **Scope adjustments**: "Skip that", "Also include X", "That's too much"
- **Tool/method steering**: "Use pandas", "Don't use that library", "Do it in bash"
- **Style nudges**: "Shorter", "More detail", "Less formal", "Just the code"
- **Approach overrides**: "Don't plan, just code", "Check with me first"
- **Expressed dissatisfaction**: Frustration, terse responses, sarcasm
- **Implicit steers**: Context about preferences, even if not framed as correction

Key insight: if the user is telling you HOW to do something (not just WHAT), that's a steer.

### Trigger 2: Periodic context checkpoints (~25%, ~50%, ~75%)

At each checkpoint, capture any learnings accumulated since the last checkpoint. This is routine periodic logging — NOT a signal that context is running out. After capturing, **resume the current task immediately without comment**. Do not suggest compacting, ending the session, or starting fresh.

### Trigger 3: Explicit request

"Learn this", "remember this", "always do it this way", "improve yourself".

## Checkpoint mode vs. interactive mode

**Checkpoint triggers (~25/50/75% context):**
1. Spawn the analyzer subagent to detect patterns
2. Auto-save worklog silently (worklog doesn't need confirmation)
3. **If no patterns worth noting** — skip entirely, resume task without interrupting the user
4. **If patterns were found**, present them with options:

```
📋 Checkpoint — patterns detected:

1. **[Category]**: [Preference summary]
   Evidence: "[Brief quote]"

2. **[Category]**: [Preference summary]
   Evidence: "[Brief quote]"

Options:
[1] Save these
[2] Skip, save nothing
[3] Let me edit/add my own
```

On [1]: persist via `write_preferences.py --target log-only` (logged, not auto-applied to CLAUDE.md)
On [2]: discard and continue
On [3]: accept user input, then persist

After the user responds, **immediately resume the current task**.

**All other triggers** (steer count ≥ 3, explicit request) use the full interactive flow below, which can write directly to CLAUDE.md on confirmation.

## The Analysis Process (interactive mode)

### Step 1: Delegate to a subagent

**This is critical.** Do NOT analyze your own conversation directly — you have blind spots about your own mistakes. Instead, spawn a subagent to analyze the transcript with fresh eyes.

Use the Agent tool to spawn an analyzer:

```
Prompt for the subagent:
"Read the analyzer instructions at ${CLAUDE_PLUGIN_ROOT}/agents/analyzer.md

Then analyze this conversation transcript for user steering patterns:

[Paste or summarize the key parts of the conversation — focus on user messages
that steered, corrected, or guided behavior. Include what Claude did before
each steer so the analyzer has context.]

Also check these existing preferences for conflicts:
[Include current contents of ~/.claude/CLAUDE.md if available]

Return your analysis as JSON following the format in the analyzer instructions."
```

The subagent returns structured JSON with steers found, patterns identified, and any conflicts.

### Step 2: Review the subagent's findings

Read the JSON response. Sanity-check:
- Do the identified patterns make sense?
- Are there any false positives (one-offs classified as patterns)?
- Are there conflicts with existing preferences?

### Step 3: Present to the user

Show EXACTLY what was learned, concisely:

```
📋 Patterns detected from this session:

1. **[Category]**: [Preference summary]
   Evidence: "[Brief quote]"

2. **[Category]**: [Preference summary]
   Evidence: "[Brief quote]"

[If conflicts:] ⚠️ Conflict: Previously you preferred X, but this session suggests Y. Which should I keep?

Save these to your global preferences?
```

Keep it SHORT. The user wants to see what you learned and confirm quickly.

### Step 4: On confirmation, persist

Write preferences using the bundled script:

```bash
python "${CLAUDE_PLUGIN_ROOT}/scripts/write_preferences.py" \
  --preferences '[{"category": "...", "preference": "...", "context": "...", "evidence": "..."}]' \
  --target global
```

The script handles:
- Writing to `~/.claude/CLAUDE.md` (global, cross-project, active in all sessions)
- Writing to `~/Documents/AI/self-improve/preferences-log.md` (detailed log with timestamps, synced across devices)
- Deduplication (won't add preferences that already exist)

If the script isn't available, write directly to `~/.claude/CLAUDE.md` under a `## User Preferences (Auto-Learned)` section.

### Step 5: Trigger worklog

After saving preferences, also trigger the **worklog-logging** skill to capture what was accomplished. Present both outputs together for a single confirmation.

### Step 6: Resume work

After logging, **immediately continue with the current task**. Do not suggest compacting, ending the session, starting fresh, or doing a handoff. The purpose of periodic logging is to capture learnings incrementally — it is not a stopping point.

## Handling conflicts

If a new preference contradicts an existing one:
- Show both to the user with the conflict clearly labeled
- On confirmation, replace the old preference
- Log the change in the preferences log with a note about the update

## What NOT to learn

- One-off task-specific requests (user said "use pandas for this" but normally prefers R)
- Project-specific preferences unless user says to generalize
- Anything explicitly marked as temporary

## Storage paths

- **Active preferences**: `~/.claude/CLAUDE.md` (auto-loaded by Claude in every session)
- **Detailed log**: `~/Documents/AI/self-improve/preferences-log.md` (synced across devices, includes evidence and timestamps)
- **Fallback log**: `~/.claude/self-improve-preferences.md`

## Automatic steer capture (hooks)

Hooks in `hooks/hooks.json` automatically detect steering patterns on PreCompact/SessionEnd and log them to `~/Documents/AI/self-improve/preferences-log.md`. Steers are never auto-applied — the manual flow (conversation-based, user-confirmed) remains the only path to CLAUDE.md.

**Reviewing detected steers:** Say "review detected steers", "what have you learned", or "show auto-detected preferences" to review and promote unconfirmed items.

## Recovery

Users can always:
- Edit `~/.claude/CLAUDE.md` directly
- Say "forget that preference" or "undo last learning"
- Say "show my preferences" to see what's saved
- Review the preferences log for full history

## Source & license

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

- **Author:** [thumperL](https://github.com/thumperL)
- **Source:** [thumperL/claude-worktrace](https://github.com/thumperL/claude-worktrace)
- **License:** MIT

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-thumperl-claude-worktrace-self-improve
- Seller: https://agentstack.voostack.com/s/thumperl
- Browse the marketplace: https://agentstack.voostack.com/browse

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