Next phase — trace export, language scrub, hops vs autonomous turns

By Yad Konrad — @0bserver07

This artifact captures the map of what was orchestrated. The next phase is the analysis.

1. Export traces / sessions

Pull the 59 sessions (orchestrator + 58 workers) into a portable form for teaching:

• One markdown file per session, with: full prompt history (user turns + assistant turns + tool calls), redacted of any private content.
• A trimmed-down “highlights” version with only the interesting decision points (first prompt, key strategy turns, last 2 hops).
• The data/sessions.jsonl file already has the per-session metadata. The trace export needs to walk the original JSONL files line-by-line and render.

2. Language scrub

Remove AI-slop / overused vocabulary / em-dashes / business jargon from the worker prompts and assistant outputs before publishing the traces. The anti-slop-guide skill is the canonical list. Specifically watch for:

• “delve”, “tapestry”, “landscape”, “robust”, “leverage”
• Em-dashes used as throat-clearing
• Throat-clearing openers (“Great question!”, “Let me think about this carefully”)
• Rule-of-three structures used reflexively
• Hedge phrases without information value (“It’s worth noting that”)

3. Hops vs autonomous turns

The current data/sessions.tsv has hops, turns, and autonomy_ratio. The next step is to compute:

• Per-session autonomy index: (turns − hops) / turns. Closer to 1.0 = more autonomous.
• Per-wave autonomy: averaged across workers + orchestrator’s wave-slice.
• Hop classification: of Yad’s 40 actual orchestrator prompts (out of 192 type=user records, the rest being worker replies + slash + skill loaders), how many were:
  • Initial setup (one-shot context)
  • Strategy nudges (one-line course corrections)
  • Approval gates (yes/no on a plan)
  • Recovery prompts (something broke, fix it)
  • Closing prompts (wrap up, write docs)
• Turn classification: of the 1026 orchestrator turns, how many were:
  • Tool calls only (autonomous execution)
  • Tool call + reasoning (autonomous decision)
  • Plain text (responding to a hop)

A target autonomy ratio for the next build to beat: this build was 1026 orchestrator turns / 40 Yad-typed prompts ≈ 25.7 turns per Yad prompt in the orchestrator. (The raw total_turns/total_hops = 7265/353 = 20.6:1 ratio is misleading — total_hops includes worker→orchestrator routing and templated worker first-prompts, not just Yad-typed input.) Can the next build hit 50 turns per Yad prompt without quality regressing?

4. Open questions for the teaching session

• Were the 12 per-wave audits worth the cost? Each wave’s Explore audit dispatch added ~3–8% overhead. (Plus 1 initial repo survey + 2 final BUILD_NOTES extracts = 15 Explore dispatches total in the orchestrator.)
• The workers’ first-hop teammate-message has duplicated context. Could a shorter handoff cut worker cost?
• Could waves run in parallel (e.g. wave 6 and wave 7 simultaneously) instead of serially? They have no dependency.
• Could the audit step be merged into the worker prompt (self-audit), eliminating one dispatch per wave?