mind-mem-development
Skill22 starsChanged 6 months agoApache-2.0View on GitHub
MIND-Mem Python development guide
- Installs packages
In this repository7 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- apply-proposalSkill
- integrity-scanSkill
- memory-recallSkill
- mind-memSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 46
No dynamic allocation in hot paths
- Distinctive: line 47
All SQL queries parameterized
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
MIND-Mem Development
Package
- PyPI:
pip install mind-mem - Source layout:
src/mind_mem/ - Tests: pytest, 3610 passing
- CI: GitHub Actions, 3 OS × 4 Python versions (16 matrix jobs)
Architecture
- BM25F retrieval with Porter stemming + RM3 query expansion
- Hybrid BM25+Vector search with RRF fusion (sqlite-vec)
- A-MEM block metadata evolution
- 9-type intent router with adaptive confidence weights
- ConnectionManager: thread-safe SQLite pool with WAL
- BlockStore protocol: decoupled block access
- Delta-based snapshot rollback
MCP Server (81 tools)
Grouped surfaces (full list in docs/api-reference.md and
src/mind_mem/mcp_server.py):
recall, hybrid_search, prefetch, propose_update, approve_apply,
rollback_proposal, scan, list_contradictions, reindex, index_stats,
create_snapshot, list_snapshots, restore_snapshot, briefing,
category_summary, cross_encoder_rerank, find_similar,
memory_evolution, delete_memory, export_memory, import_memory,
intent_classify, retrieval_diagnostics, get_mind_kernel,
list_mind_kernels, verify_chain, audit_replay, tier_decay_apply,
encrypt_status, alerts_subscribe, and more (57 total).
Key Files
src/mind_mem/mcp_server.py— MCP server (57 @mcp.tool entries)src/mind_mem/retrieval/— search engine (bm25, vector, hybrid)src/mind_mem/governance/— contradiction detection, drift analysissrc/mind_mem/blocks/— block store, parser, evolutiontests/— comprehensive test suite
Conventions
- Python 3.10+, type hints everywhere
- Docstrings: Google style
- No dynamic allocation in hot paths
- All SQL queries parameterized
- Config: mind-mem.json (not mem-os.json)
- Auth header: X-MindMem-Token
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

