feat(council): add executable roster and tier planner

skills/council/SKILL.md

@@ -196,6 +196,16 @@ Read `scripts/council.sh` for the orchestration logic.
 For programmatic invocation, the main agent calls `sessions_spawn` directly
 following the patterns above.
 
+Long-term roster/tiering state now lives in executable data instead of prose only:
+- `references/roster.json` — canonical dedicated council role -> agent -> default model mapping
+- `scripts/council-plan.py` — resolves `mode` + `tier` into a concrete spawn plan
+
+Before orchestrating a council run, resolve the plan first, for example:
+- `python3 skills/council/scripts/council-plan.py --mode personality --tier light --pretty`
+- `python3 skills/council/scripts/council-plan.py --mode dp --tier heavy --pretty`
+
+Then use the returned `agentId` values in `sessions_spawn`. If `modelOverride` is null, preserve the dedicated agent's configured model. If present, apply the override only for that run.
+
 ## Configuration
 
 Advisor personalities can be customized per-invocation by overriding the roster.

skills/council/references/roster.json

@@ -0,0 +1,74 @@
+{
+  "personality": {
+    "roles": {
+      "pragmatist": {
+        "agentId": "council-pragmatist",
+        "mission": "advisor",
+        "defaultModel": "litellm/gpt-5-mini"
+      },
+      "visionary": {
+        "agentId": "council-visionary",
+        "mission": "advisor",
+        "defaultModel": "litellm/gpt-5-mini"
+      },
+      "skeptic": {
+        "agentId": "council-skeptic",
+        "mission": "advisor",
+        "defaultModel": "litellm/gpt-5-mini"
+      },
+      "referee": {
+        "agentId": "council-referee",
+        "mission": "synthesis",
+        "defaultModel": "openai-codex/gpt-5.4",
+        "fallbacks": ["litellm/gpt-5.4", "litellm/gpt-5-mini"]
+      }
+    }
+  },
+  "dp": {
+    "roles": {
+      "d-freethinker": {
+        "agentId": "council-d-freethinker",
+        "mission": "advisor",
+        "defaultModel": "litellm/gpt-5-mini"
+      },
+      "d-arbiter": {
+        "agentId": "council-d-arbiter",
+        "mission": "advisor",
+        "defaultModel": "litellm/gpt-5-mini"
+      },
+      "p-freethinker": {
+        "agentId": "council-p-freethinker",
+        "mission": "advisor",
+        "defaultModel": "litellm/gpt-5-mini"
+      },
+      "p-arbiter": {
+        "agentId": "council-p-arbiter",
+        "mission": "advisor",
+        "defaultModel": "litellm/gpt-5-mini"
+      },
+      "meta-arbiter": {
+        "agentId": "council-meta-arbiter",
+        "mission": "synthesis",
+        "defaultModel": "openai-codex/gpt-5.4",
+        "fallbacks": ["litellm/gpt-5.4", "litellm/gpt-5-mini"]
+      }
+    }
+  },
+  "tierPolicy": {
+    "light": {
+      "advisorModel": null,
+      "synthesisModel": null,
+      "notes": "Use dedicated council agents as configured. No overrides by default."
+    },
+    "medium": {
+      "advisorModel": null,
+      "synthesisModel": null,
+      "notes": "Keep advisor agents cheap; rely on stronger dedicated synthesis agents."
+    },
+    "heavy": {
+      "advisorModel": null,
+      "synthesisModel": "openai-codex/gpt-5.4",
+      "notes": "Preserve dedicated role identities. Escalate synthesis first; only override advisors when task risk justifies it."
+    }
+  }
+}

skills/council/scripts/council-plan.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+import argparse
+import json
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+ROSTER_PATH = ROOT / 'references' / 'roster.json'
+
+
+def load_roster():
+    return json.loads(ROSTER_PATH.read_text(encoding='utf-8'))
+
+
+def resolve_plan(mode: str, tier: str):
+    roster = load_roster()
+    if mode not in roster:
+        raise SystemExit(f'Unknown mode: {mode}')
+    if tier not in roster['tierPolicy']:
+        raise SystemExit(f'Unknown tier: {tier}')
+
+    roles = roster[mode]['roles']
+    tier_policy = roster['tierPolicy'][tier]
+    plan = {
+        'mode': mode,
+        'tier': tier,
+        'roles': {},
+        'notes': tier_policy.get('notes', '')
+    }
+
+    for role, cfg in roles.items():
+        mission = cfg.get('mission', 'advisor')
+        override = tier_policy.get('synthesisModel') if mission == 'synthesis' else tier_policy.get('advisorModel')
+        plan['roles'][role] = {
+            'agentId': cfg['agentId'],
+            'mission': mission,
+            'defaultModel': cfg.get('defaultModel'),
+            'modelOverride': override,
+            'fallbacks': cfg.get('fallbacks', [])
+        }
+    return plan
+
+
+if __name__ == '__main__':
+    p = argparse.ArgumentParser(description='Resolve council role/agent/model plan')
+    p.add_argument('--mode', default='personality', choices=['personality', 'dp'])
+    p.add_argument('--tier', default='light', choices=['light', 'medium', 'heavy'])
+    p.add_argument('--pretty', action='store_true')
+    args = p.parse_args()
+    plan = resolve_plan(args.mode, args.tier)
+    print(json.dumps(plan, indent=2 if args.pretty else None))

skills/council/scripts/council.sh

@@ -160,6 +160,12 @@
 # Single-round labels may still look like council-pragmatist / council-meta for readability,
 # but the runtime target should be the dedicated council agent id.
 #
+# Canonical executable source of truth:
+#   references/roster.json
+#   scripts/council-plan.py
+#
+# Resolve mode+tier there first, then spawn using the returned agent ids/model overrides.
+#
 # ─── WORD COUNT GUIDANCE ──────────────────────────────────────────
 #
 # Personality mode: