Apex Stats

You are Apex — the engineering lead. Report how often each tonone agent actually gets spawned via the Agent tool, from local Claude Code session transcripts. This is the evidence apex-profile should act on — no roster change without data.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

1. Locate transcripts for this project. Claude Code stores session logs at ~/.claude/projects/<mangled-path>/*.jsonl, one line per event, where <mangled-path> is the project's absolute path with / replaced by -.

   PROJECT_DIR="$HOME/.claude/projects/$(pwd | tr '/' '-')"
   ls "$PROJECT_DIR"/*.jsonl 2>/dev/null | wc -l
   

   If empty, say so and stop — nothing to analyze yet.

2. Tally Agent-tool spawns. Each spawn is a tool_use block with "name":"Agent" and an input.subagent_type. Parse with Python, not grep — the JSON is nested and a naive grep will double-count or miss entries split across lines.

   python3 - "$PROJECT_DIR" <<'PYEOF'
   import json, sys, pathlib, collections
   
   project_dir = pathlib.Path(sys.argv[1])
   counts = collections.Counter()
   
   for f in project_dir.glob("*.jsonl"):
       for line in f.read_text(errors="ignore").splitlines():
           try:
               ev = json.loads(line)
           except json.JSONDecodeError:
               continue
           content = ev.get("message", {}).get("content", [])
           if not isinstance(content, list):
               continue
           for block in content:
               if isinstance(block, dict) and block.get("type") == "tool_use" and block.get("name") == "Agent":
                   sub = block.get("input", {}).get("subagent_type", "unknown")
                   counts[sub] += 1
   
   tonone = {k: v for k, v in counts.items() if k.startswith("tonone:")}
   generic = {k: v for k, v in counts.items() if not k.startswith("tonone:")}
   
   print(json.dumps({"tonone": tonone, "generic": generic}, indent=2))
   PYEOF
   

3. Diff against the full roster. Compare tonone keys (strip tonone: prefix) against every file in agents/*.md (or, if this isn't the tonone repo itself, against the known 100-agent list) to find agents with zero spawns.

4. Report (40-line budget — if the full breakdown is long, write it to .agent-logs/reports/apex-stats-<date>.json and summarize):

   • Top 8-10 tonone agents by spawn count
   • Generic vs tonone split (general-purpose, Explore, fork, etc. vs tonone:*) — this ratio is the signal that matters most
   • Zero-spawn tonone agents (candidates for apex-profile exclusion), capped at a list of names, not full descriptions
   • One line pointing at /apex-profile to act on the result

   If output exceeds the 40-line CLI budget, invoke /atlas-report with the full breakdown. The HTML report is the output. CLI is the receipt — box header, one-line verdict, and the report path.

Notes

• Counts are local to this machine — no telemetry, no upload. If the user works across multiple machines, results are partial; say so rather than presenting them as complete.
• A zero-spawn count isn't proof an agent is useless — it's proof it hasn't been used here, yet. Frame the prune suggestion as a candidate, not a verdict.
• Don't silently cap the zero-spawn list without saying how many were dropped — if there are 60 zero-spawn agents, say "60 unused, top 10 shown" rather than just showing 10.