cost-optimizer

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Translated

Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.

3installs

Sourceerichowens/some_claude_skills

Added on2026-04-16

NPX Install

npx skill4agent add erichowens/some_claude_skills cost-optimizer

SKILL.md Content

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Cost Optimizer

Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget: downgrade models, skip optional nodes, or stop early.

When to Use

✅ Use for:

Setting and enforcing cost budgets for DAG executions
Real-time cost monitoring during execution
Deciding when to downgrade model tiers mid-execution
Identifying which nodes are most expensive and why
Post-execution cost analysis and optimization recommendations

❌ NOT for:

Choosing which model to use per node (use
```
llm-router
```
)
Provider pricing comparisons (static data, not a skill)
Billing or invoicing features

Budget Enforcement Process

mermaid

flowchart TD
  N[Node about to execute] --> C[Check: spent + estimated_node_cost vs budget]
  C --> S{Within budget?}
  S -->|Yes, &gt;20% remaining| E[Execute at planned model tier]
  S -->|Yes, &lt;20% remaining| W[Execute but downgrade to Tier 1 if possible]
  S -->|No| D{Node optional?}
  D -->|Yes| SK[Skip node]
  D -->|No| H{Human gate available?}
  H -->|Yes| A[Ask human: continue over budget?]
  H -->|No| ST[Stop execution, return partial results]

Budget Tiers

Budget Remaining	Action
>50%	Execute at planned model tier
20-50%	Log warning. Continue at planned tier.
10-20%	Downgrade remaining Tier 2 nodes to Tier 1 (Haiku)
5-10%	Downgrade ALL remaining nodes to Tier 1. Skip optional nodes.
<5%	Stop execution unless next node is critical path
0%	Stop. Return partial results with cost breakdown.

Cost Estimation Per Node

Before each node executes, estimate its cost:

estimated_cost = (avg_input_tokens × input_price + avg_output_tokens × output_price)

Use historical averages for this skill + model combination. If no history, use defaults:

Tier 1 (Haiku): ~800 input + 400 output = ~$0.001
Tier 2 (Sonnet): ~2000 input + 1000 output = ~$0.012
Tier 3 (Opus): ~3000 input + 1500 output = ~$0.16

Post-Execution Report

yaml

cost_report:
  total_budget: 0.50
  total_spent: 0.37
  budget_remaining: 0.13
  nodes_executed: 8
  nodes_skipped: 1
  nodes_downgraded: 2
  model_breakdown:
    haiku: { calls: 4, cost: 0.004 }
    sonnet: { calls: 3, cost: 0.036 }
    opus: { calls: 1, cost: 0.33 }
  savings_recommendations:
    - "Node 'deep-analysis' used Opus ($0.33) but downstream accepted on first try. Try Sonnet next time — potential saving: $0.32"
    - "Nodes 'validate-a' and 'validate-b' are sequential but independent. Parallelize to reduce wall-clock time."

Anti-Patterns

No Budget at All

Wrong: Running DAGs without any cost tracking until the API bill arrives. Right: Every DAG execution has a budget, even if generous. Track spend per node.

Aggressive Downgrading

Wrong: Downgrading Opus nodes to Haiku at 50% budget remaining, causing quality failures that trigger expensive retries. Right: Only downgrade when the alternative is stopping execution. Retries cost more than the original model tier.

Ignoring Retries in Cost

Wrong: Budgeting for one attempt per node. Right: Budget for avg_retries × cost_per_attempt. A node with 3 retries on Sonnet costs $0.036, not $0.012.