Question 1

什么需要安装才能使用这个工具?

Accepted Answer

需要Node 20+，以及可选的Docker来安装bundled PostgreSQL + Ollama堆栈。一个命令的安装程序在五分钟内完成一切。 SQLite的后备实现零基础设施即可使用。

Question 2

可代么为一合事Claude。微f产台吃丁。

Accepted Answer

是，此想为常为当前切给手演。及为常为切手公为u64。学也券输出一顈的常为切手存程。对游水请录的手演。得角。逇种筑亂程索引台。等了程亂索引也定义：及为手演切给。

Question 3

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 4

Claude Code技能安装是否安全?

Accepted Answer

文件系统技能捆绑在脚本中，这些脚本会使用你的权限执行。Claude Code自带的加载器并不会扫描它们。SecureContext缺失的安全门则是：在入侵前进行抽象语法树 (AST) 诊断，在每次执行前进行HMAC验证，当有失败或者入侵改变发生时自动隔离，所有入侵决策都可被追踪。

Question 5

SecureContext会把我的代码或数据发往云端吗?

Accepted Answer

没有。内存、嵌入式表示 (Ollama nomic-embed-text)、搜索、概要和审计链都在本地执行。它完全是离线工作的（如果Ollamadown掉了，关键字搜索会衰减地工作）且在闲置状态下没有花费任何费用。

Question 6

它能处理一个周长数周的项目吗?

Accepted Answer

是的，那就是交付层的用处。定义一个具有阶段的程序（zc_program），并且开放阶段的验收清单随着项目状态一起传递。关闭一个阶段需要证据表格并会自动生成一个检查点文件。如果负责执行的代理在阶段中途停机，一个新的代理则会从两次API调用中重建整个交付状态，验证后生效。

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

If you use Claude Code on real projects, you have hit these walls: 1. Every session starts cold. You re-paste the same files, re-explain the same decisions, re-discover the same gotchas — and pay tokens for the same orientation work over and over. 2. Long projects have no delivery state. When an orchestrating agent dies mid-feature (crash, closed window, exhausted context), its replacement starts blind. People compensate with hand-written CHECKPOINT files. 3. There is no record of what agents actually did. When something goes wrong, Claude Code has no tamper-evident history of what ran. 4. Skill scripts run with your permissions, unscanned. Anyone (or any agent) with write access to ~/.claude/skills/ can plant code that runs on the next invocation. 5. Parallel sessions step on each other. Two windows claim the same task; one overwrites the other's edits. SecureContext is an MCP plugin that closes all five. Install it once and your agents get: Memory that survives restarts — decisions, gotchas, and project state restored in one call; per-file semantic summaries answer "what does this file do" at 15–50× compression instead of raw reads; temporal search that knows when things happened and flags stale knowledge. A delivery layer for long-running projects — zcprogram tracks multi-phase programs; closing a phase requires acceptance evidence and auto-generates a searchable checkpoint; a fresh orchestrator resumes a dead one's work from two calls. A cryptographic audit trail — every

analyst-cognitive-instincts (Grade A)安全测试的数据 AI 技能，适用于 Claude AI。Grade A。COGNITIVE INSTINCTS——从 roles.json 深度提示中提取的分析师功能

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