Question 1

Which local models should I run, and how does hardware affect SecureContext?

Accepted Answer

SecureContext degrades gracefully by hardware tier — every LLM-powered layer fails closed (the feature quietly contributes nothing) rather than breaking ingest or search. What changes with hardware is how much of the intelligence stack is active: | Tier | Hardware | Models that fit | What you get | |---|---|---|---| | Minimum | Any CPU, ~2 GB RAM free | nomic-embed-text (embeddings only) | Hybrid BM25+vector search, working memory, audit chain, skills gating — the core. LLM layers (event extraction, entity extraction, contradiction adjudication) stay dormant. | | Mid | 8–16 GB GPU (or Apple Silicon 16 GB+) | + qwen2.5-coder:14b or phi4:14b (one at a time) | + Event-fact extraction at ingest (temporal reasoning), LLM contradiction adjudication, entity extraction, L0/L1 semantic file summaries. | | Full | 24 GB+ GPU (e.g. RTX 4090/5090) | + phi4:14b and gpt-oss:20b resident together | Everything, concurrently, at interactive latency — plus a strong local generator for QA/benchmarks. This is the configuration our published benchmark deltas were measured on. | Model-choice guidance (all measured, see bench/): Embeddings: nomic-embed-text — required, tiny, runs anywhere. Event extraction (ZCEVENTEXTRACTMODEL, default phi4:14b): our bakeoff scored phi4:14b at 100% event recall / 100% date accuracy, tying gpt-oss:20b at 2× the speed. On smaller GPUs qwen2.5-coder:14b is close behind (84.6% recall). Coder models ≠ better: qwen2.5-coder:32b scored worst (69.2%) despite being the large

Question 2

我需要什么才能运行它?

Accepted Answer

Node 20+ 和 (推荐) Docker 以便使用配套 PostgreSQL + Ollama 仓库。如果你使用一个命令安装器，所有内容需要大约五分钟。SQLite的替代方案在没有基础设施的情况下工作

Question 3

Can multiple Claude Code sessions work on the same project without conflicts?

Accepted Answer

Yes — parallel sessions atomically claim tasks from a work-stealing queue (zero double-claims at 50 agents × 100 tasks in testing), coordinate through typed broadcasts (ASSIGN/STATUS/MERGE/REJECT), and keep private per-agent memory namespaces plus a shared pool. Department-style hierarchies (heads + workers, N-tier escalation) are supported for larger agent teams.

Question 4

Are Claude Code skills safe to install?

Accepted Answer

Filesystem skills bundle scripts that run with your permissions, and Claude Code's native loader does not scan them. SecureContext adds the missing gate: AST scan at admission, HMAC verification before every execution, automatic quarantine on failure or post-admission change, and a verifiable chained log of every admission decision.

Question 5

Does SecureContext send my code or data to the cloud?

Accepted Answer

No. Memory, embeddings (Ollama nomic-embed-text), search, summarization, and the audit chain all run locally. It works fully offline (search degrades gracefully to keyword-only if Ollama is down) and costs $0 when idle.

Question 6

Can it manage a long, multi-week project?

Accepted Answer

Yes — that's what the delivery layer is for. Define a program with phases (zc_program), and the open phase's acceptance checklist travels with the project state. Closing a phase requires an evidence table and generates a checkpoint document automatically. If the orchestrating agent dies mid-phase, a fresh one reconstructs the full delivery state from two calls — verified live.

Question 7

How much does SecureContext reduce Claude Code token costs?

Accepted Answer

Measured across a 10-session project: ~87% less context-overhead (session restore is ~1.5–4k tokens instead of 20–50k of re-pasted files). On long-lived projects with hundreds of accumulated memories, the recall budget keeps each recall at ~4k tokens where an unbounded dump would be ~47k. File questions are answered from 15–50× compressed semantic summaries instead of raw reads.

Question 8

How do I give Claude Code persistent memory across sessions?

Accepted Answer

Install SecureContext (one command — see Quick start), then agents call zcremember to persist decisions and gotchas and zcrecall_context({focus: "current task"}) at session start to restore them — ranked by relevance, with natural-language time queries answered directly from memory. Memory survives restarts, compactions, and reboots because it lives in PostgreSQL, not in the context window.

Question 9

How it's different from competitors

Accepted Answer

Most "memory for Claude Code" projects do one of three things: Wrap the conversation in JSON. Saves the messages, replays them. No semantic compression, no audit trail, no security model. Sync to a cloud service. Better UX but now your agent's tool history (potentially including secrets it touched) lives on someone else's server. Drop in vector search. Adds RAG over your docs but doesn't help with the actual problem — orchestrating what the agent does, verifying what ran, surviving session compaction. SecureContext is none of those: | | SecureContext | Cloud memory services | Vector RAG plugins | Native Claude Code | |---|---|---|---|---| | Persistent across restarts | ✅ Yes, local Postgres | ✅ But on their server | ⚠️ Only docs, not state | ❌ Lossy auto-compact | | Delivery programs + evidence-gated checkpoints | ✅ zcprogram | ❌ | ❌ | ❌ | | HMAC audit trail | ✅ Per-agent subkey | ❌ | ❌ | ❌ | | Per-agent identity isolation | ✅ HKDF + RLS | ❌ | ❌ | ❌ | | Skill admission gate (HMAC + AST) | ✅ | ❌ | ❌ | ❌ | | Tamper-evident chain + session replay | ✅ | ❌ | ❌ | ❌ | | Work-stealing queue for parallel sessions | ✅ FOR UPDATE SKIP LOCKED | ❌ | ❌ | ❌ | | Runs offline | ✅ Local Postgres + Ollama | ❌ | ⚠️ Depends | ✅ | | Cost when idle | $0 | Monthly subscription | $0 | $0 | Versus the well-known memory products: Mem0 and Zep are excellent hosted memory APIs for LLM apps you build; Letta (MemGPT) is an agent framework with self-editing memory. SecureContext targets a different job — me

Question 10

What's new (v0.46.1)

Accepted Answer

Temporal retrieval round 2 + the delivery-tool kit. Compound temporal questions decompose into event clauses with rank fusion; temporal searches render a chronological Timeline block and stale results carry age warnings. zcprogram adds multi-phase delivery programs with evidence-gated, auto-checkpointing phase closure — live-verified with an orchestrator handoff test (kill the orchestrator mid-phase; a cold replacement resumes from two calls). The embed lane now has a watchdog with budgeted self-healing. Recent releases: v0.46.0 — team memory (per-user keys, shared workspaces, write attribution), session replay with cryptographic provenance, OTel Gen-AI spans, CI/headless memory CLI, chunked embeddings, compliance report export. v0.44.0 — temporal fact supersession, durable PG task graph, first public LongMemEval numbers. v0.43.0 — token-budgeted recall digest (fixes the 47k-token recall firehose on mature projects). v0.41–0.42** — memory-quality program: focused recall, consolidation, bi-temporal windows, multi-hop graph retrieval, TTL memories, multimodal ingestion. Full details in CHANGELOG.md; architecture notes in ARCHITECTURE.md.

Question 11

What is SecureContext?

Accepted Answer

如果您在实际项目中使用Claude Code，很可能会遇到这些问题： 1. 每次会话都需要重新开始。您需要重新粘贴同样的文件，重新解释同样的决策，重新发现同样的问题——并且每次都支付相同的方向指引费用。 2. 长期项目没有交付状态。 当协调执行器死亡（崩溃，关闭窗口，耗尽上下文）时，它的替代品从零开始。 人们以手写的检查点文件的形式弥补。 3. 没有记录 agent 实际执行过的内容。 当发生问题时，Claude Code 没有tamper-evident历史，知道什么已经执行了。 4.Skill 脚本以您的权限运行，无需扫描。 有权访问~/.claude/skills/的任何人（或任何 agent）都可以植入在下次调用时运行的代码。 5.并行会话互相干扰。 两个窗口会争夺同一个任务；一个会覆盖另一个窗口的编辑。  SecureContext 是 MCP 插件，它关闭了上述所有五个问题。 安装一次，agent 将获得：重启后记忆，决策，问题和项目状态在一键中恢复; 根据文件内容生成的语义摘录，能以 15-50 倍的压缩比回答“这个文件是什么作用”的问题，而不是原始读入数据; 知识的时间维度搜索，标记过时的知识. 对于长期项目，SecureContext 提供了交付层 -- zcprogram 跟踪多阶段程序; 关闭一个阶段需要接受性证据，而是自动搜索检查点; 然后一个新协调器可以在两个调用中从另一个崩溃的工作中恢复. 加密审计追踪 - 每个agent 的行为都有历史记录。

designer-cognitive-instincts (Grade A)经过安全测试的 data-ai 技能，适用于 Claude AI。等级 A。COGNITIVE INSTINCTS —— 从 roles.json deepPrompt 中提取的设计师专用技能

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