Private-by-default, local-first memory/context/task orchestrator for MCP apps and agents.
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io.github.sheawinkler/contextlattice is an MCP server that private-by-default, local-first memory/context/task orchestrator for MCP apps and agents. Its tool list has not been published yet, requires no API key, and scores 91/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-sheawinkler-contextlattice-sheawinkler": {
"args": [
"-y",
"skills"
],
"command": "npx"
}
}
}Are you the author?
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Private-by-default memory and context orchestration for AI agents.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'skills' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked skills against OSV.dev.
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Open an agent. Already there.
The local-first intelligence layer that gives AI agents durable continuity, explainable retrieval, portable context, and verified learning across harnesses.
Quickstart · How it works · Agent setup · Architecture · Docs · Updates
Models can reason. Harnesses can act. Neither reliably retains the mission when a chat, model, tool, account, or computer changes.
ContextLattice gives that work a durable, inspectable context layer. It reconstructs the active objective, selects the evidence that matters, carries it safely, and records what actually worked—without turning every prompt into a transcript dump or making cloud storage mandatory.
| Capability | What changes |
|---|---|
| Durable continuity | Reopen the objective, decisions, repository state, risks, proof, and next move as one bounded packet. |
| Explainable retrieval | Rank evidence by impact per token and expose source coverage, omissions, opposition, degradation, and receipts. |
| Portable context | Move signed, least-privilege continuation across agents and machines while keeping execution and transport caller-owned. |
| Verified skill evolution | Discover skills without loading every file, evaluate repeated wins on holdouts, and require review before promotion. |
| Privacy-bounded Aggregate Signal | Learn from explicitly opted-in, clipped statistics while raw memory remains local; production activation stays hard-blocked pending independent privacy and utility review. |
The CLI is the primary interface. The dashboard makes behavior and proof visible. HTTP and MCP are companion integration surfaces for applications and harnesses.
| Stage | ContextLattice does |
|---|---|
| 01 · Reopen | Reconstructs the one active mission from durable checkpoints and current state. |
| 02 · Select | Retrieves high-signal evidence into a compact Context Pack with provenance. |
| 03 · Move | Carries signed, bounded context through Agent Packets, Passports, and encrypted continuation envelopes. |
| 04 · Earn | Records outcomes and promotes reusable behavior only after deterministic proof and human approval. |
| 05 · Compound | Improves future retrieval while preserving corrections, contradictions, freshness, and retirement semantics. |
ContextLattice does not replace your agent harness, choose goals from retrieved text, or execute imported context. Local tools remain execution surfaces; memory and remote content remain evidence.
Requirements: macOS, Linux, or Windows through WSL2; a Compose v2-compatible container runtime; and gmake, jq, rg, python3, and curl. The tested macOS baseline uses OrbStack through it