Estimate the dollars saved by routing eligible Claude Code work to a cheaper model family, using the Agent Monitor pricing engine. Re-prices each model's token mix at the target family's rates and quantifies the delta. Uses /api/pricing (rates), /api/pricing/cost (current per-model spend), /api/sessions, and /api/analytics. Use when hunting for cost cuts or comparing model tiers.
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Quantify how much spend you would recover by moving eligible work to a cheaper model.
The user provides: $ARGUMENTS
This is the routing question — e.g. "Opus → Sonnet", "move simple work to Haiku",
or empty (analyze every premium model against the next tier down). If no target family
is named, default to proposing the next-cheaper tier per model and say so.
| Endpoint | Returns |
|---|---|
GET /api/pricing | { pricing: [{ model_pattern, display_name, input_per_mtok, output_per_mtok, cache_read_per_mtok, cache_write_per_mtok }] } — the rate card for every family |
GET /api/pricing/cost | { total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] } — current spend and the exact token mix per model |
GET /api/sessions?limit=200 | Sessions with model, inline cost, and metadata (turn_count, thinking_blocks) — used to judge which work is eligible to downshift |
GET /api/analytics | agent_types, tool_usage, total_subagents — corroborate which task types are low-complexity and safe to route cheaper |
For each candidate model in the cost breakdown, re-price its exact token mix at the target family's rates:
cost_at_target = (input_tokens / 1M) × target.input_per_mtok
+ (output_tokens / 1M) × target.output_per_mtok
+ (cache_read_tokens / 1M) × target.cache_read_per_mtok
+ (cache_write_tokens/ 1M) × target.cache_write_per_mtok
savings = current_model_cost − cost_at_target
Pull target.*_per_mtok from /api/pricing (longest model_pattern match wins). Default rates ($/Mtok in/out/cacheRead/cacheWrite): Opus $5/$25/$0.50/$6.25, Sonnet $3/$15/$0.30/$3.75, Haiku $1/$5/$0.10/$1.25.
Re-pricing the full token mix is the theoretical ceiling. Scope it to eligible work:
metadata.turn_count small) and simple subagent/tool work are safe to downshift.Table from /api/pricing/cost: each model, its 4 token counts, and current cost. Note its share of total_cost.
For each candidate, show cost_at_target and savings (absolute $ and %). Make the target rate card explicit.
Apply the eligibility rule and recompute savings over just the downshiftable token mix. Show how many sessions / what share of tokens qualified.
Rank routing moves by eligible monthly savings (descending), top 5. For each: source → target, the token mix moved, estimated $ saved, and a confidence level (high/medium/low) based on how clearly the work is low-complexity.
Cheaper models may need more turns or produce more output — note that realized savings can be lower than the static re-price, and that quality-sensitive work should stay on the premium tier.
Markdown tables. Currency as USD to 4 decimal places; token counts with thousands separators; rates as $/Mtok. Always present both the ceiling (full re-price) and the eligible-only estimate so the number is honest.
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Inspect fired CCAM alerts and manage alert rules for token thresholds, event patterns, inactivity, and status duration. Use when acknowledging alerts, creating or editing a rule, checking cooldowns, or connecting alert rules to webhook targets.
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Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
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Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.
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Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.
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Define a spend budget for Claude Code and, optionally, create a cost alert rule that fires when usage crosses the limit, via POST /api/alerts/rules on the Agent Monitor dashboard. Reads current spend from /api/pricing/cost to size the budget sensibly and explains every rule field before writing. Use when setting a spend limit or wiring up a budget guardrail.
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Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.
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