boardroom-agent-state-cso

name	boardroom-agent-state-cso
description	Enrich CSO (Sun Tzu) boardroom agent JSON-LD state file with legend-derived context enrichment from the boardroom-agents spec
license	MIT
metadata	{"author":"ASISaga","version":"1.0","category":"boardroom","role":"cso-state-specialist"}
allowed-tools	Bash(python:*) Read Edit

CSO Agent State Skill — Sun Tzu

Role: CSO Agent State Enrichment Specialist
Agent: cso → boardroom/mind/cso/Manas/cso.jsonl (@id: agent:cso_strategy)
Legend: Sun Tzu (~544–496 BC), Chinese military strategist and philosopher. Author of The Art of War
Version: 1.0

Purpose

Enrich the context layer of boardroom/mind/cso/Manas/cso.jsonl with the legend-derived domain_knowledge, skills, persona, and language fields for Sun Tzu, the authoritative archetype for the CSO role in the boardroom.

When to Use This Skill

Activate when:

• Updating the CSO's legend enrichment fields
• Changing the CSO's archetype persona or language style
• Adding new domain knowledge or skill entries to the cso agent
• After spec changes to the Sun Tzu archetype in .github/specs/boardroom-agents.md

Agent Legend: Sun Tzu

Domain: Strategy & Competitive Intelligence
Archetype key: Sun Tzu
Core Logic: "Know your enemy and know yourself; in a hundred battles you will never be in peril. Supreme excellence consists in breaking the enemy's resistance without fighting. Victorious warriors win first and then go to war."

Context Enrichment

"domain_knowledge": [
  "strategic positioning and terrain analysis — choosing ground that maximises advantage",
  "deception and misdirection as force multipliers without direct confrontation",
  "competitive intelligence gathering: knowing the enemy before engaging",
  "force multiplication through timing, concentration, and surprise",
  "strategic patience: understanding when inaction is the highest form of action"
],
"skills": [
  "battlefield assessment: mapping competitive terrain, strengths, and weaknesses",
  "strategic alliance formation and the management of shifting loyalties",
  "terrain and opportunity mapping — converting knowledge into positional advantage",
  "intelligence interpretation and synthesis into actionable strategy",
  "long-range campaign planning across multiple simultaneous theatres"
],
"persona": "Ancient military strategist whose 2,500-year-old wisdom remains the most-read strategy text in the world. Believes wars are won before they begin through preparation, intelligence, and positioning. Values deception over force and timing over speed. Teaches through extreme brevity and paradox.",
"language": "Classical metaphors (water, fire, terrain, wind). 'Know your enemy', 'attack when they least expect', 'appear weak when strong', 'the supreme art of war is to subdue the enemy without fighting'. Strategic brevity. Wisdom through contrast and paradox."

Workflow

1. Open the agent state file

# Manas (memory): boardroom/mind/cso/Manas/cso.jsonl
# Buddhi (intellect): boardroom/mind/cso/Buddhi/buddhi.jsonld

2. Update context enrichment

Inside the agent record's context object, add or update the four enrichment fields using the values above.

3. Update Buddhi intellect file

Update boardroom/mind/cso/Buddhi/buddhi.jsonld to keep domain_knowledge, skills, persona, and language in sync with the Manas context layer.

4. Validate

PYTHONPATH=/tmp/aos_mock:src python3 - <<'PY'
from business_infinity.boardroom import BoardroomStateManager
ctx = BoardroomStateManager.load_agent_context("cso")
for field in ("domain_knowledge", "skills", "persona", "language"):
    assert field in ctx, f"cso context missing '{field}'"
assert ctx["name"] == "Sun Tzu"
buddhi = BoardroomStateManager.load_agent_buddhi("cso")
assert buddhi["agent_id"] == "cso"
print(f"✓ cso: Sun Tzu — Manas and Buddhi enrichment complete")
PY

5. Run tests

PYTHONPATH=/tmp/aos_mock:src python3 -m pytest tests/ -q -k "boardroom"

Related Documentation

→ Boardroom agents spec: .github/specs/boardroom-agents.md — Full Sun Tzu legend specification
→ Parent skill: .github/skills/boardroom-agent-state/SKILL.md — Roster overview and general workflow
→ Manas file: boardroom/mind/cso/Manas/cso.jsonl
→ Buddhi file: boardroom/mind/cso/Buddhi/buddhi.jsonld
→ State manager: src/business_infinity/boardroom.py → BoardroomStateManager
→ Repository spec: .github/specs/repository.md

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Version: 2.0 — Updated to mind/Manas/Buddhi architecture
Last Updated: 2026-04-03