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List all past Agent Council sessions for the current project. Shows session ID, mode, agent count, and question for each.
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Based on SOC occupation classification
| name | council-list |
| description | List all past Agent Council sessions for the current project. Shows session ID, mode, agent count, and question for each. |
| allowed-tools | ["Bash"] |
Show all past council deliberation sessions for this project.
COUNCIL_BIN=""; for _d in "$HOME/.claude/skills/agent-council" "$HOME/.agents/skills/agent-council" "$HOME/.gemini/skills/agent-council" "$(git rev-parse --show-toplevel 2>/dev/null)"; do [ -x "$_d/bin/council" ] && COUNCIL_BIN="$_d/bin/council" && break; [ -x "$_d/council" ] && COUNCIL_BIN="$_d/council" && break; done; [ -z "$COUNCIL_BIN" ] && COUNCIL_BIN="$(which council 2>/dev/null || echo "bin/council")"
SLUG=$(basename "$(git rev-parse --show-toplevel 2>/dev/null || pwd)")
$COUNCIL_BIN list --project "$SLUG"
After showing the list, tell the user:
Next steps:
- Replay in terminal:
/council-replay <session-id>- Re-run with current context:
/council-revisit <session-id>- Record outcome:
/council-outcome <session-id> "what happened"- Open viewer:
open ~/.council/{project}/{session-id}/viewer.html
Nudge a specific agent to reconsider its opinion based on a corrected assumption. Sends the original question + response + correction to one agent and saves the updated opinion alongside the original for comparison.
Convene a panel of CLI-based AI agents (Codex, Gemini) to deliberate on a question. Each agent answers independently, then you synthesize the council's verdict as chairman. Use for architecture decisions, code review, debugging hypotheses, or any question where diverse perspectives add value.
Replay a past Agent Council session in the terminal. Shows the full deliberation: question, each agent's opinion, and the chairman's synthesis.
Revisit a past Agent Council decision with current codebase context. Re-runs the same question through the council and shows a side-by-side comparison of what changed. Use for living decisions.
Record the outcome of a past Agent Council decision. Was the council right? Builds calibration data over time to learn which models are best at what.
Ambient awareness for Agent Council. Detects moments where convening a multi-model council would genuinely help and suggests the right command. Never interrupts. Never nags. Just a quiet tip at the right moment.