ppm · diff
git:20260815.078218a to git:20260817.0a119cc
39 added, 10 removed. Audit B to C.
+ cat > /home/workdir/artifacts/ppm-SKILL.md.txt << 'EOF'
---
- title: PMLL
- description: Persistent spatial memory for AI agents (Stellar commitment anchoring planned)
+ name: ppm
+ description: Python Package Manager with persistent memory MCP tools for Context+, supermodeltools, and agent workflows. Use when working with PPM, pmll-memory-mcp via PPM, Context+ pipelines, context stitching, or packaging/agent memory tooling under drQedwards/ppm.
---
- # PMLL
+ # PPM
- Gives AI agents persistent spatial memory so they can retain long-term context, form symbiotic memory layers, and maintain durable state across sessions.
+ Python Package Manager oriented around **persistent memory MCP tools** for agentic workflows — Context+ pipelines, supermodeltools/cli analysis, and structured memory ingestion/retrieval.
- PMLL provides durable, structured memory primitives useful for agentic workflows. It supports the PPM project, Context+ pipelines, and supermodeltools/cli for analysis and visualization. **On-chain commitment anchoring on Stellar (storing 32-byte hashes of off-chain memory via a Soroban contract) is planned.**
+ PPM is the packaging and tool surface that sits alongside PMLL-style spatial memory. Memory payloads stay **off-chain** by default; any on-chain commitment anchoring is handled by the separate **PMLL / `pmll-anchor`** path, not by this skill.
## Highlights
- - Persistent, addressable spatial memory (off-chain today).
- - PPM-based context stitching and MCP tools for memory ingestion and retrieval.
- - Integration with forloopcodes/contextplus for hierarchical indexing and supermodeltools/cli for graphing and analysis.
- - Planned: atomic Soroban `pmll-anchor` contract that stores only a 32-byte commitment + emits events (full payload stays off-chain).
+ - PPM-based context stitching for agent memory and tool graphs.
+ - MCP tools for memory ingestion and retrieval (`init`, `peek`, `set`, `resolve`, `flush`, graph ops).
+ - Works with forloopcodes/contextplus hierarchical indexing and supermodeltools/cli for graphing and analysis.
+ - Complements [drQedwards/pmll](https://github.com/drQedwards/pmll) spatial memory; does not claim Stellar storage of full memory payloads.
## Quick start
- 1. Install the memory MCP package:
+ 1. Install the memory MCP package (shared with the PMLL memory stack):
```bash
pip install pmll-memory-mcp
# or via npm: npx pmll-memory-mcp
+ # or: npm install -g pmll-memory-mcp
+ ```
+
+ 2. Register it with your MCP client (Claude Desktop example — `claude_desktop_config.json`):
+
+ ```json
+ {
+ "mcpServers": {
+ "pmll-memory-mcp": {
+ "command": "npx",
+ "args": ["pmll-memory-mcp"]
+ }
+ }
+ }
+ ```
+
+ (If installed via pip you can use `"command": "pmll-memory-mcp"` instead.)
+
+ 3. Restart the client / start a fresh session. Call `init` once at the start of a task, then `peek` before expensive operations.
+
+ ## Related
+
+ - PMLL spatial memory skill: https://github.com/drQedwards/pmll/blob/main/SKILL.md
+ - This repo: https://github.com/drQedwards/ppm
+ - Planned on-chain commitments (32-byte hashes only): `pmll-anchor` under drQedwards/pmll — not part of the PPM package surface.
+ EOF
+ wc -l /home/workdir/artifacts/ppm-SKILL.md.txt
+ ls -la /home/workdir/artifacts/ppm-SKILL.md.txt