| name | self-improve |
| description | Continuous self-improvement loop — AuthorAgent learns from mistakes, successes, and user feedback to get better over time |
| author | AuthorAgent |
| 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
AuthorAgent 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
-
User Revision (highest signal) — User edited or rewrote AI output
- Compare original vs. user version
- Extract the specific changes as preferences
- Confidence: HIGH
-
User Feedback — User explicitly says "I liked X" or "Don't do Y"
- Direct instruction → immediate high-confidence lesson
- Confidence: VERY HIGH
-
Acceptance Pattern — User accepted output without changes
- Reinforces that the approach worked
- Confidence: MEDIUM (absence of feedback isn't always approval)
-
Error Recovery — Something failed and was fixed
- The fix becomes a lesson for next time
- Confidence: HIGH
-
Self-Critique — Agent reviews its own output and spots issues
- Lower confidence but still valuable
- Confidence: LOW-MEDIUM
-
After-Action Review — Post-goal structured reflection
- Comprehensive lessons from completed goals
- Confidence: MEDIUM-HIGH
Applying Lessons
Before each task, AuthorAgent should:
- Load relevant lessons from the improvement log
- Filter by category matching the current task type
- Sort by confidence and recency
- 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 interactionsshow improvement log — View all active lessonswhat did you learn — Summary of recent learningsclear lesson [id] — Remove a specific lessonimprovement stats — Metrics on the learning systemapply lessons to [task] — Manually trigger lesson lookup for a task
