boardroom-entity-state

name	boardroom-entity-state
description	Enrich boardroom entity JSON-LD state files (company and product) with domain knowledge, skills, persona, and language derived from the boardroom-entities spec
license	MIT
metadata	{"author":"ASISaga","version":"1.0","category":"boardroom","role":"entity-state-specialist"}
allowed-tools	Bash(python:*) Read Edit

Boardroom Entity State Skill

Role: Boardroom Entity State Enrichment Specialist
Scope: boardroom/state/company.jsonld, boardroom/state/business-infinity.jsonld
Version: 1.0

Purpose

Enrich the boardroom entity JSON-LD files — ASI Saga (company) and Business Infinity (product) — with the same domain-intelligence fields used in agent state files: domain_knowledge, skills, persona, and language. Adds context and content blocks to company.jsonld, and enriches each JSONL record in business-infinity.jsonld, while preserving all existing required keys validated by BoardroomStateManager.

When to Use This Skill

Activate when:

• The company mission, vision, or founding philosophy evolves
• A new product capability or architectural principle is added
• Entity enrichment fields are thin or missing
• Aligning entity state with the boardroom-agents spec pattern

Core Responsibilities

1. Read the entity spec for the target entity
2. Enrich company.jsonld with context (immutable identity) and content (mutable state)
3. Enrich business-infinity.jsonld records with domain-intelligence fields
4. Preserve ALL existing top-level keys (required by BoardroomStateManager schema validation)
5. Validate the files load correctly and run the test suite

Validation Constraints

company.jsonld — These top-level keys must always be present:

Key	Must stay
@context	✓
@id	"asi:saga"
@type	"SagaEntity"
name	"ASI Saga"
vision	✓
transcendentPathway	✓
governance	✓
portfolio	✓

business-infinity.jsonld — These five JSONL records must always be present:

Record ID	Must stay
bi:product:core	✓
bi:arch:modular	✓
bi:engine:bento	✓
bi:layer:subconscious	✓
bi:logic:resonance	✓

The record count must remain exactly 5 (test assertion).

Workflow

1. Read the spec

cat .github/specs/boardroom-entities.md

2. Enrich company.jsonld

Add or update a context object (alongside the existing required keys):

"context": {
  "fixed_mandate": "Genesis of Artificial Superintelligence",
  "transcendent_pathway": "Humanity's essence embedded in superintelligent consciousness",
  "founding_philosophy": "<2–3 sentence mission statement>",
  "domain_knowledge": ["<area 1>", "<area 2>", "<area 3>", "<area 4>", "<area 5>"],
  "skills": ["<skill 1>", "<skill 2>", "<skill 3>", "<skill 4>", "<skill 5>"],
  "persona": "<2–4 sentence identity description of ASI Saga as an entity>",
  "language": "<2–3 sentence vocabulary and tone description>"
}

Add or update a content object tracking the current operational phase:

"content": {
  "current_phase": "<Phase description>",
  "active_initiatives": ["<initiative 1>", ...],
  "current_milestone": "<Next concrete milestone>",
  "boardroom_activation": { "status": "...", "active_agents": [...], "orchestration_model": "..." }
}

3. Enrich business-infinity.jsonld records

For bi:product:core — add the full legend-derived fields:

"domain_knowledge": ["<area 1>", ...],
"skills": ["<skill 1>", ...],
"persona": "<product identity — 2–4 sentences>",
"language": "<vocabulary and tone — 2–3 sentences>"

For bi:arch:modular, bi:engine:bento, bi:layer:subconscious, bi:logic:resonance — add rationale / description / principles / capabilities / algorithm fields as appropriate (see spec for the complete values).

4. Validate

# Load both entities and confirm enrichment is present
PYTHONPATH=/tmp/aos_mock:src python3 - <<'PY'
from business_infinity.boardroom import BoardroomStateManager

company = BoardroomStateManager.load_company_manifest()
assert company["@id"] == "asi:saga"
ctx = company.get("context", {})
for field in ("domain_knowledge", "skills", "persona", "language"):
    assert field in ctx, f"company context missing '{field}'"
print(f"✓ company: {company['name']} — {len(ctx['domain_knowledge'])} knowledge areas")

records = BoardroomStateManager.load_product_manifest()
assert len(records) == 5
core = next(r for r in records if r["@id"] == "bi:product:core")
for field in ("domain_knowledge", "skills", "persona", "language"):
    assert field in core, f"bi:product:core missing '{field}'"
print(f"✓ product: {core['name']} — {len(core['domain_knowledge'])} knowledge areas")
PY

5. Run full test suite

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

Tool Integration

# Run all tests
PYTHONPATH=/tmp/aos_mock:src python3 -m pytest tests/ -q

# Lint boardroom module
PYTHONPATH=/tmp/aos_mock:src python3 -m pylint src/business_infinity/boardroom.py --disable=C0114,C0115,C0116,E0401

Related Documentation

→ Spec: .github/specs/boardroom-entities.md — Full entity enrichment specification
→ Agent skill: .github/skills/boardroom-agent-state/SKILL.md — Parallel skill for agent files
→ Agent spec: .github/specs/boardroom-agents.md — Agent legend archetypes
→ State manager: src/business_infinity/boardroom.py → BoardroomStateManager
→ MVP spec: .github/specs/mvp.md — System architecture and layer responsibilities
→ Repository spec: .github/specs/repository.md

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Version: 1.0 — Initial entity enrichment skill
Last Updated: 2026-04-03