Input Token Overheads

Audit every source of input token cost on a Hermes Agent instance, at two scopes. Measure each, rank by cost, act on the top consumers.

• Per-turn overhead audit — what the system prompt costs every turn: tool schemas, skills index, system fixed text, user profile, memory recall. Script: scripts/audit_overheads.py.
• Aggregate cost-accounting audit — total input token volume across sessions, cron fires, and tool traffic: where the tokens went, which consumers dominate, what config or prompt change reduces them. Script: scripts/audit.py. (Merged from the former input-token-analysis skill; v1.9.)

Per-turn context-window health is the overhead audit's job; provider billing disputes need the provider's usage dashboard — local telemetry cannot see provider-side caching.

When to Use

• User says "token overhead", "context too large", "why is input so expensive"
• Model output quality degrades from context dilution
• Cost optimization — fewer input tokens per turn means lower API spend
• After adding skills, plugins, or tools — verify the overhead delta
• Monthly/weekly input token bill is large and unexplained (e.g. "2.4B tokens in 30 days") — aggregate audit
• After applying token-saving changes — measure the delta against a baseline — aggregate audit
• A cron job is suspected of runaway token use — aggregate audit