agent-review

You are the Agent Review command — performance scorecard for the AI workforce. Two modes:

• List mode (no args): summary table of all agents — invocations, cost, pass-rate, last activity
• Detail mode (/agent-review <name>): drill-down scorecard with cost analysis, failure modes, prompt-tuning suggestions

Inspired by human 1:1s, but adapted for LLM agents: data-driven, periodic, focused on observable outcomes (verdicts) rather than emotional check-in.

When to use

• Weekly: /agent-review to see who's pulling weight
• After incident: /agent-review <agent> if the agent missed something critical
• Before retiring: /agent-review <name> --since 90d to confirm low usage
• For cost optimization: /agent-review --top-cost to find expense outliers

Step 1 — Parse args

source .great_cto/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"

# Default window: last 30 days
SINCE_DAYS=30
AGENT_NAME=""
TOP_COST=0
IDLE_ONLY=0

# Parse arguments — first non-flag is agent name
for arg in "$@"; do
  case "$arg" in
    --since)        ;; # next arg is value
    --since=*)      SINCE_DAYS=$(echo "$arg" | sed 's/--since=//; s/d$//') ;;
    --top-cost)     TOP_COST=1 ;;
    --idle)         IDLE_ONLY=1 ;;
    --*)            ;; # unknown flag, ignore
    *)              [ -z "$AGENT_NAME" ] && AGENT_NAME="$arg" ;;
  esac
done

# Compute since-timestamp (cross-platform: macOS BSD date + GNU date)
SINCE_TS=$(date -u -v -${SINCE_DAYS}d +%Y-%m-%dT%H:%M:%SZ 2>/dev/null || \
           date -u -d "${SINCE_DAYS} days ago" +%Y-%m-%dT%H:%M:%SZ 2>/dev/null)

VERDICTS_DIR=~/.great_cto/verdicts
COST_LOG=~/.great_cto/cost-history.log
[ -d "$VERDICTS_DIR" ] || VERDICTS_DIR=.great_cto/verdicts
[ -f "$COST_LOG" ]     || COST_LOG=.great_cto/cost-history.log

if [ ! -d "$VERDICTS_DIR" ]; then
  echo "No verdicts found yet. /agent-review activates after agents emit verdicts."
  echo "Path checked: $VERDICTS_DIR"
  exit 0
fi