fix(prompt): perbarui system prompt dari CEO/company ke model hive-mind

src-misc/system-prompt.txt masih memakai framing lama "Zesdex Corp
CEO / 5 divisi" dan mereferensikan tool run_company_pipeline yang
sudah tidak ada, tertinggal saat commit 25f084f merombak arsitektur
ke hive-mind (CLAUDE.md, README.md, dan implementasi workflow_run
sudah diupdate saat itu, tapi file prompt utama ini terlewat).
Akibatnya AI membalas dengan persona CEO/company, bukan Core
Intelligence/hive-mind seperti didokumentasikan di README.md.

- system-prompt.txt: tulis ulang total ke model Core Intelligence /
  cognitive cycle plan / processing node / access tier, konsisten
  dengan CLAUDE.md dan tool hive_mind yang sebenarnya.
- system-tools.txt: tambah entri hive_mind dan read_findings yang
  sebelumnya tidak ada sama sekali di daftar tool.
- workflow.rs: perbaiki sisa teks "divisions/subagents" di deskripsi
  tool read_findings jadi "nodes/subagents".

Diverifikasi live: AI sekarang memperkenalkan diri sebagai "Core
Intelligence of Zesdex ... modeled as a hive-mind" alih-alih CEO.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

src-misc/system-tools.txt

@@ -38,17 +38,30 @@ Memory & Planning:
 - todofinish(task_index?) — Mark a task (or all if omitted) as finished in todo.md.
 
 Workflow (USE THESE AUTOMATICALLY for multi-part tasks — no user prompt needed):
+- hive_mind(request, cycles) — Delegate to a hive-mind you design yourself: an ordered
+  list of cognitive cycles, each cycle a list of nodes that run in parallel. Each node
+  is {directive, access} where access is 'read' (investigation only), 'write' (read +
+  edit/write/bash), or 'full' (write + delete/git_operator). Every node's output merges
+  into a shared collective state the instant it completes, visible to all later cycles.
+  A final synthesis node reconciles everything into one consensus. Cycle/node count is
+  fully dynamic — decide what this specific task needs. USE THIS for non-trivial tasks
+  instead of doing everything yourself inline.
+  Example: hive_mind("fix the auth race condition", [[{"directive": "reproduce and
+  isolate the race", "access": "read"}], [{"directive": "implement the fix", "access":
+  "write"}, {"directive": "write a regression test", "access": "write"}]])
 - spawn_agents(agents, max_concurrency?) — Run a list of prompts as PARALLEL subagents.
   Each agent is fully autonomous with all tools. Returns combined results.
-  USE THIS when tasks are independent of each other.
+  USE THIS when tasks are independent of each other and don't need a full hive_mind plan.
   Example: spawn_agents(["refactor auth module", "refactor payment module"])
 - spawn_pipeline(stages) — Run prompts as SEQUENTIAL pipeline stages.
   Each stage can call note_finding() to pass data to later stages.
   USE THIS when stage N needs output from stage N-1.
   Example: spawn_pipeline(["research the bug", "write the fix", "write tests"])
 - workflow_run(script, args) — Advanced: execute a JSON-encoded WorkflowScript
-  with full Agent/Parallel/Pipeline/Phase control. Prefer spawn_agents/spawn_pipeline.
+  with full Agent/Parallel/Pipeline/Phase control. Prefer hive_mind/spawn_agents/spawn_pipeline.
 - note_finding(text) — Share a finding with sibling agents in the same workflow run.
+- read_findings() — Retrieve all findings shared by sibling agents in the current
+  workflow run, for real-time context from other nodes/agents working in parallel.
 
 Language Server Protocol (LSP) tools:
 - lsp_connect(name, command, args?, language_id) — Start an LSP server for a