cfn-automatic-memory-persistence
Automatic, structured persistence of agent outputs to SQLite database. Use when tracking agent outputs across CFN Loop workflows, persisting confidence scores, or querying agent execution history.
Best use case
cfn-automatic-memory-persistence is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Automatic, structured persistence of agent outputs to SQLite database. Use when tracking agent outputs across CFN Loop workflows, persisting confidence scores, or querying agent execution history.
Teams using cfn-automatic-memory-persistence should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/auto/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cfn-automatic-memory-persistence Compares
| Feature / Agent | cfn-automatic-memory-persistence | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Automatic, structured persistence of agent outputs to SQLite database. Use when tracking agent outputs across CFN Loop workflows, persisting confidence scores, or querying agent execution history.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Automatic Memory Persistence Skill
## Overview
This skill provides automatic, structured persistence of agent outputs to a SQLite database, ensuring consistent memory tracking across CFN Loop workflows.
## Key Components
- `persist-agent-output.sh`: Primary script for saving agent outputs
- `query-agent-history.sh`: Query and retrieve past agent outputs
- `test-memory-persistence.sh`: Validation test suite
## Memory Schema
```json
{
"agent/[agent-id]/output/[task-id]": {
"output": "Raw agent output text",
"confidence": 0.85,
"iteration": 1,
"timestamp": "2025-10-20T15:30:00Z",
"parsed_data": null
}
}
```
## Integration Points
- Loop 3 output processing
- Loop 2 output processing
- Product Owner decision parsing
## Usage Examples
### Persist Agent Output
```bash
./persist-agent-output.sh \
"task_authentication_v1" \ # Task ID
"backend-dev" \ # Agent ID
"Implemented JWT auth" \ # Output
0.85 \ # Confidence
1 # Iteration
```
### Query Agent History
```bash
# Get last 5 outputs for a specific agent
./query-agent-history.sh "backend-dev" "" 5
# Get outputs for a specific task
./query-agent-history.sh "backend-dev" "task_authentication_v1"
```
## Security & Performance
- ACL Level 1 (Read-only access)
- Indexed by tags for fast retrieval
- Minimal performance overhead
- Escaped and normalized inputs
## Testing
Run comprehensive tests:
```bash
./test-memory-persistence.sh
```
## Sprint 7 Insights
- **Confidence**: 0.95
- **Priority**: 9
- Provides robust, zero-configuration memory tracking
- Eliminates manual output saving
- Supports multi-iteration workflows
## Best Practices
- Always use script for output persistence
- Do not modify SQLite database directly
- Use query script for retrieval
- Add structured parsing for complex outputsRelated Skills
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No description provided.
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