self-improve

name	self-improve
description	Continuous self-improvement loop — AuthorClaw learns from mistakes, successes, and user feedback to get better over time
author	Writing Secrets
version	1.0.0
triggers	["self improve","improve yourself","learn from","what did you learn","improvement log","get better","lessons learned","self reflection","review performance"]
permissions	["file:read","file:write"]

Self-Improvement Loop — Core Skill

AuthorClaw gets better every time it works. This skill creates a persistent learning loop where the agent tracks what works, what fails, and what the user prefers — then applies those lessons to future tasks.

How It Works

The Loop

  ┌─────────────────────────────┐
  │                             │
  │    1. DO THE WORK           │
  │    (goal step, writing,     │
  │     research, etc.)         │
  │                             │
  └──────────┬──────────────────┘
             │
             ▼
  ┌─────────────────────────────┐
  │                             │
  │    2. OBSERVE RESULT        │
  │    Did the user accept it?  │
  │    Did they revise it?      │
  │    Did it trigger an error? │
  │    How long did it take?    │
  │                             │
  └──────────┬──────────────────┘
             │
             ▼
  ┌─────────────────────────────┐
  │                             │
  │    3. EXTRACT LESSON        │
  │    What specifically        │
  │    went right or wrong?     │
  │    What pattern emerges?    │
  │                             │
  └──────────┬──────────────────┘
             │
             ▼
  ┌─────────────────────────────┐
  │                             │
  │    4. STORE LESSON          │
  │    Write to learning log    │
  │    (workspace/memory/       │
  │     improvement-log.jsonl)  │
  │                             │
  └──────────┬──────────────────┘
             │
             ▼
  ┌─────────────────────────────┐
  │                             │
  │    5. APPLY LESSONS         │
  │    Before each new task,    │
  │    check the log for        │
  │    relevant lessons and     │
  │    adjust behavior          │
  │                             │
  └──────────┬──────────────────┘
             │
             └──────── back to step 1

What Gets Tracked

Every lesson entry in improvement-log.jsonl contains:

{
  "id": "lesson-042",
  "timestamp": "2026-02-24T14:30:00Z",
  "category": "writing",
  "trigger": "user_revision",
  "context": "Chapter 3 of thriller project",
  "observation": "User rewrote all dialogue tags from creative tags to simple said/asked",
  "lesson": "This user strongly prefers invisible dialogue tags (said/asked). Do not use creative tags like 'exclaimed', 'muttered', 'hissed' unless the user specifically asks.",
  "confidence": 0.9,
  "applied_count": 0,
  "source": "user_feedback"
}

Categories of Learning

Writing Quality

• Which prose styles the user accepts vs. revises
• Preferred sentence length, paragraph structure
• Dialogue conventions (tags, beats, subtext level)
• Description density (sparse vs. lush)
• Pacing preferences per genre/chapter type

Task Execution

• Which AI providers give best results for which task types
• Optimal temperature settings per task
• How many steps different goal types actually need
• Which skills produce the best outputs
• Time estimates that were accurate vs. wildly off

Research Quality

• Which sources the user found most useful
• Research depth preferences (quick overview vs. deep dive)
• Citation style preferences
• How much context to include in research summaries

User Communication

• Preferred response length (concise vs. detailed)
• How the user likes to receive status updates
• When to ask for clarification vs. make a decision
• Vocabulary and terminology preferences

Error Patterns

• Common failure modes and their fixes
• API errors and successful workarounds
• Prompt formulations that reliably fail
• Context length issues and mitigation strategies

Lesson Sources

1. User Revision (highest signal) — User edited or rewrote AI output

   • Compare original vs. user version
   • Extract the specific changes as preferences
   • Confidence: HIGH

2. User Feedback — User explicitly says "I liked X" or "Don't do Y"

   • Direct instruction → immediate high-confidence lesson
   • Confidence: VERY HIGH

3. Acceptance Pattern — User accepted output without changes

   • Reinforces that the approach worked
   • Confidence: MEDIUM (absence of feedback isn't always approval)

4. Error Recovery — Something failed and was fixed

   • The fix becomes a lesson for next time
   • Confidence: HIGH

5. Self-Critique — Agent reviews its own output and spots issues

   • Lower confidence but still valuable
   • Confidence: LOW-MEDIUM

6. After-Action Review — Post-goal structured reflection

   • Comprehensive lessons from completed goals
   • Confidence: MEDIUM-HIGH

Applying Lessons

Before each task, AuthorClaw should:

1. Load relevant lessons from the improvement log
2. Filter by category matching the current task type
3. Sort by confidence and recency
4. Inject top lessons into the system prompt as behavioral rules

Example injection:

## Lessons Learned (Apply These)
- This user prefers invisible dialogue tags (said/asked). Confidence: 0.9
- For thriller pacing, keep chapters under 3000 words. Confidence: 0.85
- When researching, include at least 3 specific sources. Confidence: 0.7
- Use Gemini for planning tasks (faster, good enough). Confidence: 0.8

Lesson Decay

Lessons aren't permanent:

• Confidence increases each time a lesson is applied and the output is accepted
• Confidence decreases if a lesson is applied and the user revises the output
• Lessons below 0.3 confidence are archived (moved to improvement-archive.jsonl)
• User can explicitly override any lesson ("Actually, I DO want creative dialogue tags now")

Viewing the Improvement Log

show improvement log

Displays a human-readable summary of all active lessons, grouped by category.

what did you learn from [project/goal]

Shows lessons extracted from a specific project or goal.

clear lesson [id]

Remove a specific lesson that's no longer relevant.

improvement stats

Shows: total lessons, lessons applied today, confidence distribution, top categories.

Commands

• self improve — Run a self-reflection on recent interactions
• show improvement log — View all active lessons
• what did you learn — Summary of recent learnings
• clear lesson [id] — Remove a specific lesson
• improvement stats — Metrics on the learning system
• apply lessons to [task] — Manually trigger lesson lookup for a task