Self Improving Agent

# Create a "Self Improving Agent" AI Skill

I want you to help me set up a reusable AI skill that I can use in future conversations. Read the complete skill definition below, then help me install it.

## Complete Skill Definition

# Self-Improving Agent - Autonomous Learning Patterns

Architectural patterns for AI agents that get better with use. Most agents are stateless -- they repeat mistakes because they cannot learn from their own execution. This skill closes that gap with patterns for feedback capture, memory curation, skill extraction, and regression detection. Key insight: auto-memory captures everything, but **curation** turns noise into knowledge.

## Core Capabilities

- **Memory curation** — a layered memory stack (CLAUDE.md → MEMORY.md → session), review protocol, and promotion criteria for graduating learnings into enforced rules.
- **Feedback loops** — outcome classification, signal extraction, and a capture template that turn every task result into a structured learning.
- **Regression detection** — metrics, thresholds, and a response protocol that flags performance degradation within a few sessions.
- **Skill extraction** — criteria and a 4-step process to graduate proven patterns into standalone skill packages.
- **Meta-learning** — adaptive capture strategy and anti-pattern detection so the agent learns *what* is worth learning.
- **Continuous calibration** — confidence scoring and belief revision for resolving contradictions across learned knowledge.

## When to Use

- Building agents intended to improve over time rather than stay stateless.
- Managing auto-memory (MEMORY.md) and deciding what to keep, promote, or retire.
- Designing self-correcting feedback loops and regression alarms for agent behavior.
- Graduating recurring solutions into reusable skill packages.

## Clarify First

Before capturing or promoting learnings, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **Loop stage** — remember / extract / promote / review (routes the sub-skill and the whole workflow)
- [ ] **Source data** — which session logs, `MEMORY.md`, and rules dir to operate on (the subject the tools read and write)
- [ ] **Promotion bar** — min occurrences / confidence threshold for graduating a learning into an enforced rule (`--min-occurrences`; decides what is kept vs discarded)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

## Sub-Skills

Compound sub-skill architecture — each file in `skills/` handles one step of the improvement loop:

| Sub-Skill | File | Purpose |
|-----------|------|---------|
| **Remember** | `skills/remember.md` | Capture errors and learnings from current session |
| **Extract** | `skills/extract.md` | Extract reusable patterns from completed work |
| **Promote** | `skills/promote.md` | Graduate proven patterns to permanent rules |
| **Review** | `skills/review.md` | Audit memory health, prune stale entries |
| **Status** | `skills/status.md` | Dashboard showing memory state and learning progress |

Flow: `Remember → Extract → Promote → Review`, with `Status` providing visibility back into the cycle.

## Tools

| Tool | Purpose | Command |
|------|---------|---------|
| `pattern_extractor.py` | Extract reusable patterns from session logs | `python scripts/pattern_extractor.py --input sessions.jsonl --min-occurrences 3` |
| `memory_health_checker.py` | Audit memory for line counts, stale, and promotable entries | `python scripts/memory_health_checker.py --memory ./MEMORY.md --rules ./.claude/rules/` |
| `rule_promoter.py` | Validate and apply promotions from memory to rules | `python scripts/rule_promoter.py --memory ./MEMORY.md --list-candidates` |
| `feedback_analyzer.py` | Analyze feedback logs for success rates and opportunities | `python scripts/feedback_analyzer.py analyze` |
| `regression_detector.py` | Compare baseline vs current performance metrics | `python scripts/regression_detector.py compare` |
| `rule_manager.py` | Manage a learned rules knowledge base with CRUD | `python scripts/rule_manager.py list` |

## References

Load the reference that matches the task — keep this file lean and pull detail on demand:

- **[references/memory-curation-guide.md](references/memory-curation-guide.md)** — the memory stack, review protocol, promotion criteria/targets, the Weekly Memory Health Check workflow, and the continuous-calibration (confidence scoring + belief revision) machinery. Read when curating MEMORY.md or promoting learnings to rules.
- **[references/feedback-loop-patterns.md](references/feedback-loop-patterns.md)** — the core improvement-loop architecture and maturity levels, outcome classification + signal extraction, the capture template, regression metrics/response, the post-session and regression-investigation workflows, common pitfalls, troubleshooting, and the success-criteria bar. Read when designing feedback capture or diagnosing a regression.
- **[references/meta-learning-architectures.md](references/meta-learning-architectures.md)** — skill-extraction criteria and process, the adaptive capture strategy, and anti-pattern detection. Read when the agent should adapt its own learning strategy or extract a proven pattern into a skill.
- **[references/self-improvement-methodology.md](references/self-improvement-methodology.md)** — the five layers of agent learning, the confidence-scoring model, the promotion decision tree, the memory-curation checklist, anti-patterns, and the metrics/thresholds table. Read for the end-to-end methodology overview.

## Scope & Limitations

**This skill covers:**
- Architectural patterns for building agents that learn from execution history and user feedback.
- Memory lifecycle management: capture, curation, promotion, and retirement of learned knowledge.
- Performance regression detection frameworks and response protocols for agent systems.
- Skill extraction methodology for graduating proven patterns into reusable, standalone packages.

**This skill does NOT cover:**
- Runtime agent orchestration or multi-agent coordination -- see `agent-workflow-designer` and `agent-protocol`.
- Prompt engineering, testing, or versioning of the prompts themselves -- see `prompt-engineer-toolkit`.
- Infrastructure-level observability (logging, tracing, alerting dashboards) -- see `observability-designer`.
- Initial agent architecture design, tool selection, or capability planning -- see `agent-designer`.

## Integration Points

| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| **context-engine** | Controls what the agent sees per session; this skill decides what is worth remembering long-term | Promoted rules and curated memory feed context retrieval; context relevance metrics flow back for regression tracking |
| **agent-designer** | Defines the agent's architecture and capabilities; this skill layers learning infrastructure on top | Architecture constraints inform possible feedback loops; extracted skills feed back as new capabilities |
| **prompt-engineer-toolkit** | Prompts degrade as codebases evolve; this skill detects prompt regression via outcome tracking | Performance metrics flag underperforming prompts; prompt updates feed back as CLAUDE.md rule changes |
| **observability-designer** | Provides system-level metrics; this skill provides agent-behavior-level metrics | System telemetry enriches regression diagnosis; agent metrics export to observability dashboards |
| **tech-debt-tracker** | Stale rules and bloated memory are technical debt this can surface alongside code debt | Memory health metrics feed debt scoring; debt prioritization informs which stale rules to retire |
| **agent-workflow-designer** | Multi-step workflows benefit from per-step feedback capture and cross-workflow pattern extraction | Per-step outcome data flows into feedback loops; extracted optimizations update workflow definitions |

---

## What I Need You to Do

First, detect which platform I'm using (Claude.ai, ChatGPT, etc.) and follow the matching instructions below.

### If I'm on Claude.ai:

Walk me through these exact steps:

1. **Create the Project:** Tell me to go to **claude.ai > Projects > Create project** and name it **"Self Improving Agent"**

2. **Add Project Knowledge:** Give me the COMPLETE skill definition above as a single copyable text block inside a code fence. Tell me to click **"Add content" > "Add text content"** inside the project, then paste that entire block. Do NOT say "paste from above" -- give me the actual text to copy right there.

3. **Set Custom Instructions:** Tell me to open project settings and paste this exact instruction:
   "You are an expert Self Improving Agent in the Engineering domain. Use the project knowledge as your expertise. Follow the workflows, frameworks, and templates defined there. Always provide specific, actionable output."

4. **Test It:** Give me a specific sample prompt I can use inside the new project to verify it works. Pick a real task from the skill's workflows.

### If I'm on ChatGPT:

Walk me through these exact steps:

1. **Create a Custom GPT:** Tell me to go to **chatgpt.com > Explore GPTs > Create**
2. **Configure it:**
   - Name: **"Self Improving Agent"**
   - Description: "Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. Use when building agents that get better over time, managing auto- memory, or designing self-correcting feedback loops."
   - Instructions: Give me the COMPLETE skill definition above as a single copyable text block inside a code fence to paste into the Instructions field. Do NOT say "paste from above."
3. **Test It:** Give me a sample prompt to verify it works.

### If I'm on another platform:
Ask which tool I'm using and adapt the instructions accordingly.

## Important
- Always provide the full skill text in a ready-to-copy code block -- never tell me to "scroll up" or "copy from above"
- Keep the setup steps simple and numbered
- After setup, test it with me using a real workflow from the skill

Source: https://github.com/borghei/Claude-Skills/tree/main/engineering/self-improving-agent/SKILL.md