docs: align model card with v0.3.0b9 (hook, acquire, native encoder, pure-MIND migration)

README.md

@@ -7,6 +7,8 @@ tags:
   - retrieval
   - routing
   - agent-runtime
+  - mcp
+  - skills
   - mind-nerve
 language:
   - en
@@ -15,13 +17,45 @@ pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 ---
 
-# mind-nerve — Phase 1 (v1.1-oss)
-
-**Intent-classification preselector for agent runtimes.**
-
-A small, fast classifier that sits between a user request and the host runtime. It reads the request, decides which subset of available tools/skills/agents is relevant, and hands the host a short list — so the downstream LLM never sees the full library in its system prompt.
-
-Result: library size decouples from token cost. Hosting 4,400 skills costs the same prompt budget as hosting 44, because only the top-K are ever loaded per turn.
+# mind-nerve — Intent-classification preselector for agent runtimes
+
+**Open the library, hide the cost.** A small, fast classifier sits between a
+user request and the host runtime (Claude Code, codex, grok, kimi, gemini,
+MCP hosts — 17 CLI runtimes supported by the installer). It reads the
+request, decides which subset of available skills/tools/agents/MCPs is
+relevant, and hands the host a short list — so the downstream LLM never sees
+the full library in its system prompt.
+
+Result: library size decouples from token cost. A 1,300+ skill hub is
+reachable for ~2k tokens of announce instead of a ~95k-token bulk listing —
+and only the top-K bodies are ever loaded per turn.
+
+**Current release: v0.3.0b8+ (public beta).** PyPI:
+[`mind-nerve`](https://pypi.org/project/mind-nerve/) · Code:
+[github.com/star-ga/mind-nerve](https://github.com/star-ga/mind-nerve).
+
+## What's new in the beta line
+
+- **Per-prompt routing hook** — a `UserPromptSubmit` hook queries the routing
+  daemon per prompt, projects the relevant skills into the CLI's skills dir
+  (atomic symlink flip), and injects a ranked route table with absolute
+  `SKILL.md` paths. This is what makes a large hub *reachable* without being
+  *announced*.
+- **`mind-nerve acquire`** — vetted external acquisition: search curated
+  sources (Anthropic's skills repo, the official MCP servers repo, the MCP
+  registry API, GitHub search), fetch into a capped quarantine, run a
+  deterministic fail-closed static security scan (shell-pipe installers,
+  reverse shells, exfiltration collectors, prompt injection incl. MCP tool
+  schemas, archive escapes, credential access, persistence hooks), and
+  install the clean packages into the hub with per-file SHA-256 manifests
+  and a live daemon reindex. Threat model: `docs/acquisition.md` in the repo.
+- **Native Q16.16 encoder bundled** — the wheel ships
+  `libmind_nerve_encoder.so` with a real `encoder_weights.q16.bin` blob;
+  `MIND_NERVE_BACKEND=native` is the default routing path with a PyTorch
+  fallback.
+- **Cross-CLI audited** — the change set was independently audited by two
+  external CLI agents; all critical/high findings fixed with regression
+  tests.
 
 ## Usage
 
@@ -40,6 +74,20 @@ The first call auto-downloads this checkpoint into
 `~/.local/share/mind-nerve/runtime/`. To pre-seed or use a custom location,
 set `MIND_NERVE_RUNTIME_DIR=/path/to/your/runtime/`.
 
+Wire the per-prompt hook into your CLIs (claude-code, codex, grok, kimi,
+gemini, +12 more):
+
+```bash
+mind-nerve-install install --cli all
+```
+
+Acquire a vetted external skill:
+
+```bash
+mind-nerve acquire search "pdf"
+mind-nerve acquire install <url>
+```
+
 ## Model
 
 - **Base model**: `BAAI/bge-small-en-v1.5` (fine-tuned)
@@ -71,20 +119,23 @@ set `MIND_NERVE_RUNTIME_DIR=/path/to/your/runtime/`.
 - `route_table.npy` — precomputed catalog embeddings (11,922 × 384, float32)
 - `route_table.jsonl` — catalog metadata (one JSON object per row of `route_table.npy`)
 
-## Status
-
-**Phase 1, public alpha.** Inference runs on PyTorch via the fine-tuned BGE encoder. Phase 2 (target Q3 2027) replaces the PyTorch path with a native MIND Q16.16 inference loop and adds:
-
-- Cross-architecture bit-identity gate (x86 CPU vs CUDA)
-- p95 ≤ 30 ms latency budget on 4-core CPU
+## Status & roadmap
 
-Phase 2 is gated on `mindc` 0.2.6 (`pub fn` → C symbol export) and 0.3.0 (cdylib emit).
+**Public beta (v0.3.0b8+).** The PyTorch reference path above drives the
+catalog; the bundled native Q16.16 encoder is the default routing path.
+The active workstream is the **pure-MIND migration**: the repo's end state
+is MIND-only (router core, CLI, daemon, MCP server, hook, installer as a
+compiled native binary), tracked in
+[ROADMAP.md](https://github.com/star-ga/mind-nerve/blob/main/ROADMAP.md).
+mindc is at 0.10.2; the kernel tree compiles under a fail-closed CI gate.
+Cross-arch bit-identity for CUDA (task #57) remains open — the emit path,
+not the hardware, is the blocker.
 
 ## License
 
 This model card and the weights it points at are released under **Apache-2.0**.
 
-The PyPI wheel `mind-nerve` bundles a FORTRESS-protected `libmindnerve.so` whose source remains private (STARGA Commercial). The wheel is Apache-2.0; the bundled binary is the protected runtime layer that activates in Phase 2. The Phase 1 inference path published here does not depend on the protected binary.
+The PyPI wheel `mind-nerve` bundles a FORTRESS-protected `libmindnerve.so` whose source remains private (STARGA Commercial). The wheel is Apache-2.0; the bundled binary is the protected runtime layer. The Phase 1 inference path published here does not depend on the protected binary.
 
 For commercial deployments needing per-customer FORTRESS-locked builds of the runtime layer, contact `license@star.ga`.
 
@@ -96,6 +147,6 @@ For commercial deployments needing per-customer FORTRESS-locked builds of the ru
   title   = {mind-nerve: Intent-classification preselector for agent runtimes},
   year    = {2026},
   url     = {https://github.com/star-ga/mind-nerve},
-  version = {0.1.0-alpha.3},
+  version = {0.3.0b9},
 }
 ```