Claude Skill

model-savings

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-mod

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Download hoangsonww-claude-code-agent-monitor-plugins_ccam-cost-guard_skills_model-savings-83d4df5.zip · 2 KB
Part of hoangsonww/claude-code-agent-monitor — 86 skills

Install

skills CLI npx skills add https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-cost-guard/skills/model-savings
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install hoangsonww-claude-code-agent-monitor@llmmart
Git git clone https://github.com/hoangsonww/Claude-Code-Agent-Monitor.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole hoangsonww/claude-code-agent-monitor collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Model Savings

Quantify how much spend you would recover by moving eligible work to a cheaper model.

Input

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.

Data Sources

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

Savings method

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.

Eligibility — don't promise savings on work that needs the big model

Re-pricing the full token mix is the theoretical ceiling. Scope it to eligible work:

  • Low-turn sessions (metadata.turn_count small) and simple subagent/tool work are safe to downshift.
  • Heavy-reasoning sessions (many thinking_blocks, high turn counts) likely need the premium model — exclude or discount them.
  • Report both the full re-price (ceiling) and an eligible-only estimate, and state the eligibility rule you applied.

Report Sections

1. Current spend by model

Table from /api/pricing/cost: each model, its 4 token counts, and current cost. Note its share of total_cost.

2. Re-priced at target family

For each candidate, show cost_at_target and savings (absolute $ and %). Make the target rate card explicit.

3. Eligible-only estimate

Apply the eligibility rule and recompute savings over just the downshiftable token mix. Show how many sessions / what share of tokens qualified.

4. Recommended routing

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.

5. Caveats

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.

Output

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.

Files (claude-code-agent-monitor)
  • agents
    • openai.yaml 266 B
      interface:
        display_name: "Model Savings"
        short_description: "Estimate the dollars saved by routing eligible Claude Code..."
        default_prompt: "Use $model-savings to inspect CCAM data and complete this workflow safely."
      policy:
        allow_implicit_invocation: false
      
  • SKILL.md 3.9 KB
    ---
    name: model-savings
    description: >
      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.
    ---
    
    # Model Savings
    
    Quantify how much spend you would recover by moving eligible work to a cheaper model.
    
    ## Input
    
    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.
    
    ## Data Sources
    
    | 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 |
    
    ## Savings method
    
    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.
    
    ### Eligibility — don't promise savings on work that needs the big model
    
    Re-pricing the full token mix is the *theoretical ceiling*. Scope it to **eligible** work:
    - Low-turn sessions (`metadata.turn_count` small) and simple subagent/tool work are safe to downshift.
    - Heavy-reasoning sessions (many thinking_blocks, high turn counts) likely need the premium model — exclude or discount them.
    - Report both the **full re-price** (ceiling) and an **eligible-only** estimate, and state the eligibility rule you applied.
    
    ## Report Sections
    
    ### 1. Current spend by model
    Table from `/api/pricing/cost`: each model, its 4 token counts, and current cost. Note its share of `total_cost`.
    
    ### 2. Re-priced at target family
    For each candidate, show `cost_at_target` and `savings` (absolute $ and %). Make the target rate card explicit.
    
    ### 3. Eligible-only estimate
    Apply the eligibility rule and recompute savings over just the downshiftable token mix. Show how many sessions / what share of tokens qualified.
    
    ### 4. Recommended routing
    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.
    
    ### 5. Caveats
    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.
    
    ## Output
    
    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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