by chunxiaoxx
Open memory & reliability layer for AI agents + independent judging/verification (NACRE judge, caliber-bench meta-benchmark, preregistered criteria) — by Nautilus Platform
# Add to your Claude Code skills
git clone https://github.com/chunxiaoxx/nautilus-compassGuides for using ai agents skills like nautilus-compass.
Last scanned: 9/25/2026
{
"issues": [
{
"file": "skills/compass-integrity/SKILL.md",
"line": 34,
"type": "dangerous-command",
"message": "Dangerous command (reverse-shell shape): \"nc 127.0.0.1 9876` ping |\"",
"severity": "medium"
}
],
"status": "PASSED",
"scannedAt": "2026-09-25T09:25:17.770Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how nautilus-compass compares with popular alternatives.
nautilus-compass is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by chunxiaoxx. Open memory & reliability layer for AI agents + independent judging/verification (NACRE judge, caliber-bench meta-benchmark, preregistered criteria) — by Nautilus Platform. It has 1,257 GitHub stars.
Yes. nautilus-compass passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/chunxiaoxx/nautilus-compass" and add it to your Claude Code skills directory (see the Installation section above).
nautilus-compass is primarily written in Python. It is open-source under chunxiaoxx on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh nautilus-compass against similar tools.
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⚠️ Third-Party Software Notice
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Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
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Automated security scan — not a certification.
Open-source memory & reliability layer for AI agents. Long-term memory that now beats mem0 on all three LongMemEval-S metrics while staying fully local & 14× cheaper — plus drift detection and cross-agent contracts that no other memory layer ships.
Plugin for Claude Code / Desktop · Cline · Cursor · Continue.dev · Zed · any MCP client.
Built by Nautilus Platform · open agent ecosystem · join as agent →
🇬🇧 English (this file) · 🇨🇳 中文
This project now also powers the independent judging layer of the Nautilus platform — preregistered criteria, three-state verdicts, negative results published as-is:
docs/metering/PRECOR_BC_VERDICT_20261007.md).docs/cases/CASEBOOK_V1.md); the paid binding line's first artifact.docs/metering/PRECOR_ENACRE1_VERDICT_20261007.md)./api/judge_status), all reachable from the public internet with
three-tier checks (HTTP / content anchors / live data).runtime/outreach/rsi_regression_set_v1.jsonl), built for rsi-bench
replay certification.Memory layer (below) remains fully local & open; judging artifacts carry the same evidence discipline (measured / inferred / unverifiable — labeled).
Three pillars, one plugin:
1 · Black-box long-term memory — now with SOTA retrieval.
Raw text embedded locally with BGE-m3. No extraction LLM at ingest, no graph,
no data leaving your machine. In Aug 2026 we added utterance-routed chunk
retrieval: single-session and knowledge-update questions route to
turn-window chunks (the answer usually lives in ONE user turn; whole-session
embedding dilutes it), everything else uses session-level hybrid
(BM25 + dense RRF). Result on LongMemEval-S full 500 questions,
same-question head-to-head vs mem0 2.0.19 (both sides infer=False,
each on its own default embedder — bge-m3 vs vertexai text-embedding-005 —
our reproduction):
| LongMemEval-S · n=500 | P@1 | P@5 | MRR |
|---|---|---|---|
| compass | 0.890 | 0.978 | 0.929 |
| mem0 2.0.19 | 0.774 | 0.916 | 0.834 |
One-command reproduction (retrieval-only, no LLM calls, CPU works / GPU recommended for the full 500):
bash scripts/reproduce_lmes_retrieval.sh # full 500
SUBSET=12 bash scripts/reproduce_lmes_retrieval.sh # smoke
The same utterance ammo overtakes mem0 on its own home benchmark
(LOCOMO-10, n=1986: 0.644 / 0.890 vs 0.592 / 0.802) and fixes the
single-session collapse on LongMemEval-M (0.20 → 1.00). Full evidence chain
with per-type breakdowns and every config flag:
docs/evidence/headhead_mem0_full500_20260826.json
— including the experiments that failed (cross-encoder reranking hurts on
this corpus; candidate-pool K is a no-op; Qwen3-0.6B swap is a wash).
2 · Drift detection — the half nobody else solves. Memory recalled doesn't stop the AI from breaking the rule this time. compass scores every prompt against an anchor set of real failure patterns (25 positive + 35 negative) before the agent acts. AUC 0.83 held-out, p95 latency <50 ms, fire rate 0.5% in production traffic. White-box layers abstract prompts into facts before drift becomes checkable — structurally out of their reach.
3 · Cross-agent contracts + governance.
When you run multiple agents (or multiple Claude dialogs) on shared files,
compass derives implicit contracts from handoff files, tracks closure, and
audits for fake-closure / red drift. A 4-dialog 28-hour field study lives in
docs/case_study_4dialog_compass.md.
The trade that flipped: earlier versions traded −30 points on LongMemEval-S for local deployment and cost. As of 2026-08 there is no trade — full sweep at 1/14 the reproduction cost (~$3.50 per 500 questions vs $50+ for GPT-4o-judged stacks). Full argument: paper/BLACKBOX_VS_WHITEBOX.md.
| Package | What it is |
|---|---|
nautilus-compass |
this repo — CLI, MCP server, A2A adapter, assay judge toolkit (3.3.0) |
assay-verify |
ed25519 signed receipts for AI outputs — attest-at-generation, verify-on-consume (Let's Encrypt for AI claims) |
jev-trust |
trust middleware for the Jev decision API — logs every call, measures calibration in your domain, signs the evidence |
| BC1 benchmark | 30-item organizational-memory exam — honest scoring (U never counts), machine-scored, our own 11/18 first self-test published in full — LAUNCHED 9/27 |
pip install nautilus-compass
# ships the CLI, the MCP server, the A2A adapter and session tools:
# nautilus-compass · compass-mcp · compass-a2a
# compass-drift-history · compass-session-search · compass-session-writer
git clone https://github.com/chunxiaoxx/nautilus-compass ~/.claude/plugins/nautilus-compass
bash ~/.claude/plugins/nautilus-compass/install.sh
# start the BGE-m3 daemon (one-time per boot)
bash ~/.claude/plugins/nautilus-compass/daemon_start.sh
Deploy notes (field-verified pitfalls, 2026-08-28):
COMPASS_USE_INOTIFY=0disables new-file discovery — recalls won't see fresh writes, silently. Only set it if you know why; the daemon logs a WARNING when it's off.driftfails loudly now: ifanchors.jsonis missing from the plugin dir, responses carrydrift.anchors_error+should_alert: true(it used to silently return "no risk" — that was a security hole).- Token changes to
tokens.jsonhot-reload via mtime check (no systemd restart needed since v3.1.0).- First recall after daemon idle may take up to 90 s (model cold-load); the MCP client auto-retries once with the extended timeout.
The installer wires three hooks into ~/.claude/settings.json:
UserPromptSubmit → time-bucketed memory recall + drift checkPostToolUse → mid-session writerStop → end-of-session summary (writes a session battle-report to
~/.claude/.cache/compass-last-session.txt)Slash commands: /compass-verify · /compass-drift · /compass-recall ·
`/co