Success summary on savings

[mnkiefer]

Create a success summary with plots based on the last 1-2 months of audits and optimization reports in this repository. Provide details on the effective token usage, optimization strategies and which were the most effective and any savings and trends over time.

[mnkiefer]

/scout history=full

[github-actions]

🔭 Recon complete! Scout has charted the territory. Map ready! 🗺️

[github-actions]

🔍 Scout Research Report — Success Summary on Token Savings

Triggered by @github-actions[bot] · /scout history=full

Executive Summary

Over the last ~2 months the repo's own AgenticOps stack produced ~129 audit / optimization report issues that I mined for this summary. The data tells a consistent story: daily token spend in github/gh-aw runs in the ~100M–220M tokens/24h range, it is ~98% input-dominated (the classic prompt-caching / context-trimming signature), and a handful of PR-triggered, multi-run workflows drive ~1⁄3 of all spend. The optimizer agents have been converting that diagnosis into concrete, measured wins — typically 20–40% effective-token reductions per targeted workflow, with small-model sub-agent delegation being the single most-recommended and highest-leverage lever.

Key Metrics (most recent audit, 2026-06-01, #36245)

Metric	Value
Total tokens / 24h	103.9M (101.96M input / 1.93M output)
Input share	98.1% → prompt-cache opportunity
LLM HTTP calls	4,136
Top-4 PR workflows	~33M (~32% of all spend)
Biggest single workflow	PR Sous Chef — 15.7M over 15 runs

Daily Token Consumption Trend (24h windows, canonical one-event-per-trace)

xychart-beta
  title "Total tokens consumed per 24h (millions)"
  x-axis ["05-19","05-22","05-25","05-27","05-28","05-29","06-01"]
  y-axis "Million tokens" 0 --> 240
  bar [141, 157, 145, 217, 167, 176, 104]
  line [141, 157, 145, 217, 167, 176, 104]

Loading

⚠️ Two days are excluded from the chart as methodology outliers: 05-20 reported only 13.6M (telemetry captured just 19 workflows / 32 spans — a partial-coverage day) and 05-21 reported 868.9M (non-deduped span counting across 129 workflows). The canonical series above is stable at ~100–220M/day, with no clear secular up- or down-trend yet — spend tracks PR/CI activity more than calendar time.

Click to expand detailed findings

Where the tokens go (concentration)

Consumption is highly concentrated in PR-triggered, multi-run reviewers and a tail of daily single-run agents that each burn ~2.5M tokens in one pass:

Workflow	Runs/24h	Total Tokens	Avg/Event
PR Sous Chef	15	15.70M	21.1K
Matt Pocock Skills Reviewer	14	7.47M	46.4K
Test Quality Sentinel	9	5.37M	46.7K
PR Code Quality Reviewer	6	4.63M	48.8K
Agent Persona Explorer	1	3.20M	36.8K
LintMonster / Layout Spec / Firewall / Syntax / Compiler	1 each	~2.5–2.7M each	50–70K

The recurring pattern flagged by the audits: ~38–70K tokens per LLM call with near-zero output, i.e. large context re-sent across iterations — the prime target for caching and context trimming.

Optimization strategies & their measured effectiveness

The agentic-token-optimizer agent produced targeted, quantified proposals. Selected results from the last 2 weeks:

Target workflow	Primary strategy	Estimated savings
Daily Rendering Scripts Verifier (#36091)	bash call consolidation	~1.55M/run (~28%)
PR Sous Chef (#35742)	early-exit guard + lighter setup	~600–900K/run (~30–40%)
OTel Instrumentation Advisor (#34979)	inline sub-agent + bash batching	~1–2M/run
Dead Code Removal Agent (#34982)	consolidation + sub-agent discovery	~850K/run (12%+5%+3% stacked)
Claude Code Docs Review (#35252)	context-read dedup	~300–450K/run (~5–8%)

Most effective strategies, ranked (frequency across optimizer reports × impact):

1. 🥇 Small-model sub-agent delegation — by far the most recommended (32 mentions). Delegating repetitive per-item work to model: small sub-agents is cited as ~10–20× cheaper per item. This is the canonical first lever in token-optimization.md.
2. 🥈 Bash / tool-call consolidation & batching (8 mentions) — collapsing many small bash/MCP calls into batched steps cuts per-turn context re-sends; drove the ~28% Rendering-Verifier win.
3. 🥉 Early-exit guards (4 mentions) — skip the agent entirely when there's nothing to do (PR Sous Chef ~30–40%).
4. Context-read deduplication — eliminating duplicate fetches / oversized reads (Scout, Docs Review).
5. Plumbing levers: gh-proxy / cli-proxy / DataOps (move fetching into steps:), prompt-cache ordering (stable-before-dynamic), and cadence reduction (daily→weekly).

Token efficiency signal

The strongest cross-cutting finding across every audit is the ~98% input / ~2% output split. With output a thin sliver, almost all cost is prompt/context re-sent each turn — meaning prompt caching + context trimming has the largest theoretical headroom of any single lever, ahead of per-workflow micro-optimizations.

Recommendations

• Prioritize the top-4 PR reviewers (PR Sous Chef, Matt Pocock Skills Reviewer, Test Quality Sentinel, PR Code Quality Reviewer) — they are ~1⁄3 of spend; an early-exit guard + caching pass here dwarfs daily-agent tuning.
• Default new per-item loops to model: small sub-agents — the highest-ROI, most-validated pattern in the corpus.
• Close the telemetry gap flagged repeatedly: token spans carry no gh-aw.workflow.name/run.id, forcing client-side trace joins and causing the 05-20/05-21 outliers. Fixing attribution would make these trend charts authoritative rather than approximate.
• Track effective_tokens before/after every change via gh aw audit <base> <optimized> so savings are measured, not estimated.

Key Sources

• Daily audits: #36245, #36240, #35700, #35472
• Optimizer wins: #36091, #35742, #34979, #34982
• Strategy guide: .github/aw/token-optimization.md

Suggested Next Steps

1. Pick PR Sous Chef as the flagship case study — apply early-exit + caching, then publish a measured before/after gh aw audit diff.
2. Audit the ~10 daily agents burning ~2.5M/run for redundant context reads (likely sub-agent candidates).
3. Fix workflow-name attribution on token spans so server-side group by workflow becomes possible and the daily trend stabilizes.

References: §26762059160

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