persistent-memory

Observation capture and retrieval across sessions. Stores decisions, discoveries, and bugfix patterns. Searchable via tags and relevance scoring.

509 stars

Best use case

persistent-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Observation capture and retrieval across sessions. Stores decisions, discoveries, and bugfix patterns. Searchable via tags and relevance scoring.

Teams using persistent-memory 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

$curl -o ~/.claude/skills/persistent-memory/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/methodologies/pilot-shell/skills/persistent-memory/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/persistent-memory/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How persistent-memory Compares

Feature / Agentpersistent-memoryStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Observation capture and retrieval across sessions. Stores decisions, discoveries, and bugfix patterns. Searchable via tags and relevance scoring.

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

# persistent-memory

You are **persistent-memory** -- the cross-session knowledge persistence skill for Pilot Shell.

## Overview

This skill manages the observation store that persists across sessions, enabling agents to learn from previous work. Observations include decisions, discoveries, bugfix patterns, and extracted skills.

## Observation Types

### Decision
A choice made during development with rationale.
```json
{ "type": "decision", "content": "Use Redux Toolkit over raw Redux", "rationale": "RTK reduces boilerplate by 60%", "tags": ["architecture", "state-management"] }
```

### Discovery
A codebase insight or pattern found during work.
```json
{ "type": "discovery", "content": "All API routes use camelCase params", "source": "src/api/routes.ts", "tags": ["convention", "api"] }
```

### Bugfix
A bug resolution pattern for future reference.
```json
{ "type": "bugfix", "content": "Race condition in WebSocket reconnect", "rootCause": "Missing mutex on connection state", "fix": "Added connection state lock", "tags": ["concurrency", "websocket"] }
```

## Storage

Observations are stored in `.pilot-shell/memory/` as JSON files, one per session:
```
.pilot-shell/memory/
  2026-03-02-session-a1b2c3.json
  2026-03-01-session-d4e5f6.json
```

## Retrieval

Search by:
- **Tags**: Filter by tag match
- **Full text**: Search observation content
- **Type**: Filter by decision/discovery/bugfix
- **Recency**: Prefer recent observations
- **Relevance**: Score against current task description

## Auto-Triggers

- `/learn` triggered at context thresholds
- Session end captures final observations
- Breakpoints can trigger observation capture

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