A/B Test Setup
Use when designing an A/B or split test: define the hypothesis, control and variants, estimate sample size, verify tracking, and predeclare metrics and stopping rules.
5,224,609 AI resources indexedWorking toward the world’s largest AI resource index.
Analyze token usage patterns and recommend cost optimizations with estimated savings
---
name: cost-optimize-1eaa9f767609
description: Analyze token usage patterns and recommend cost optimizations with estimated savings
argument-hint: ""
allowed-tools: mcp__claude-flow__memory_search mcp__claude-flow__memory_list mcp__claude-flow__memory_store mcp__claude-flow__agentdb_pattern-search mcp__claude-flow__agentdb_pattern-store mcp__claude-flow__agentdb_semantic-route mcp__claude-flow__hooks_model-outcome Bash
---
# Cost Optimize
Analyze recent token usage across agents and models, identify waste, and recommend specific optimizations with estimated dollar savings.
## When to use
When costs are higher than expected or you want to proactively reduce spending. Analyzes model selection efficiency, cache utilization, agent redundancy, and prompt efficiency.
## Steps
1. **Load usage data** -- call `mcp__claude-flow__memory_search` on the `cost-tracking` namespace (last 7 days). The `memory_*` tools route by namespace; use them — not `agentdb_hierarchical-*` (which routes by tier).
2. **Analyze model fit** -- for each agent, assess whether the model tier matches task complexity:
- Agents doing simple tasks (formatting, linting) on Sonnet/Opus → suggest Haiku or Agent Booster
- Agents doing complex tasks (architecture, security) on Haiku → flag quality risk
3. **Check cache rates** -- compute cache hit rate per agent; if below 60%, recommend enabling or improving prompt caching (90% cost reduction on cache reads)
4. **Detect redundancy** -- look for multiple agents performing overlapping tasks, or agents being spawned for work that could be batched
5. **Estimate savings** -- for each recommendation, calculate: current cost, projected cost after optimization, dollar savings, percentage reduction
6. **Search prior optimization patterns** -- call `mcp__claude-flow__agentdb_pattern-search` (ReasoningBank-routed; **don't** pass a `namespace` argument — pattern-* tools ignore it).
7. **Store the optimization pattern** -- two paths:
- **Pattern store (typed, recommended)**: `mcp__claude-flow__agentdb_pattern-store` with `type: 'cost-optimization'`. Don't pass a `namespace` arg — ReasoningBank routes it; on bridge unavailability the fallback writes to the reserved `pattern` namespace with `controller: 'memory-store-fallback'` (see ruflo-agentdb ADR-0001).
- **Plain store (namespace-routable)**: `mcp__claude-flow__memory_store --namespace cost-patterns` — this DOES respect the `cost-patterns` namespace because `memory_*` is namespace-routed.
8. **Close the routing feedback loop — auto-emit `hooks_model-outcome`** -- for each downgrade recommendation, format the outcome-emit command as part of the recommendation table so it can be run directly:
```bash
# success path (downgrade worked)
node plugins/ruflo-cost-tracker/scripts/outcome.mjs "<task-description>" <model> success
# escalated path (had to upgrade after downgrade attempt)
node plugins/ruflo-cost-tracker/scripts/outcome.mjs "<task-description>" <model> escalated
```
The script wraps `npx @claude-flow/cli hooks model-outcome -t ... -m ... -o ...` with explicit-argv `spawnSync` so quoting is safe. Without this signal the router does not learn from cost-tracker's recommendations and the booster bypass rate (see `cost-booster-route` skill) does not improve over time. This is the typed equivalent of the legacy `routing-outcomes` namespace (see ruflo-intelligence ADR-0001 §"Neutral").
9. **Report** -- display: ranked recommendations with savings estimate, total potential savings, implementation priority (quick wins first), and any model-outcome events emitted in step 8
## CLI alternative
```bash
npx @claude-flow/cli@latest memory search --query "cost optimization strategies" --namespace cost-patterns
npx @claude-flow/cli@latest memory store --key "opt-2026-05-04" --value '{...}' --namespace cost-patterns
```
---
name: cost-optimize-1eaa9f767609
description: Analyze token usage patterns and recommend cost optimizations with estimated savings
argument-hint: ""
allowed-tools: mcp__claude-flow__memory_search mcp__claude-flow__memory_list mcp__claude-flow__memory_store mcp__claude-flow__agentdb_pattern-search mcp__claude-flow__agentdb_pattern-store mcp__claude-flow__agentdb_semantic-route mcp__claude-flow__hooks_model-outcome Bash
---
# Cost Optimize
Analyze recent token usage across agents and models, identify waste, and recommend specific optimizations with estimated dollar savings.
## When to use
When costs are higher than expected or you want to proactively reduce spending. Analyzes model selection efficiency, cache utilization, agent redundancy, and prompt efficiency.
## Steps
1. **Load usage data** -- call `mcp__claude-flow__memory_search` on the `cost-tracking` namespace (last 7 days). The `memory_*` tools route by namespace; use them — not `agentdb_hierarchical-*` (which routes by tier).
2. **Analyze model fit** -- for each agent, assess whether the model tier matches task complexity:
- Agents doing simple tasks (formatting, linting) on Sonnet/Opus → suggest Haiku or Agent Booster
- Agents doing complex tasks (architecture, security) on Haiku → flag quality risk
3. **Check cache rates** -- compute cache hit rate per agent; if below 60%, recommend enabling or improving prompt caching (90% cost reduction on cache reads)
4. **Detect redundancy** -- look for multiple agents performing overlapping tasks, or agents being spawned for work that could be batched
5. **Estimate savings** -- for each recommendation, calculate: current cost, projected cost after optimization, dollar savings, percentage reduction
6. **Search prior optimization patterns** -- call `mcp__claude-flow__agentdb_pattern-search` (ReasoningBank-routed; **don't** pass a `namespace` argument — pattern-* tools ignore it).
7. **Store the optimization pattern** -- two paths:
- **Pattern store (typed, recommended)**: `mcp__claude-flow__agentdb_pattern-store` with `type: 'cost-optimization'`. Don't pass a `namespace` arg — ReasoningBank routes it; on bridge unavailability the fallback writes to the reserved `pattern` namespace with `controller: 'memory-store-fallback'` (see ruflo-agentdb ADR-0001).
- **Plain store (namespace-routable)**: `mcp__claude-flow__memory_store --namespace cost-patterns` — this DOES respect the `cost-patterns` namespace because `memory_*` is namespace-routed.
8. **Close the routing feedback loop — auto-emit `hooks_model-outcome`** -- for each downgrade recommendation, format the outcome-emit command as part of the recommendation table so it can be run directly:
```bash
# success path (downgrade worked)
node plugins/ruflo-cost-tracker/scripts/outcome.mjs "<task-description>" <model> success
# escalated path (had to upgrade after downgrade attempt)
node plugins/ruflo-cost-tracker/scripts/outcome.mjs "<task-description>" <model> escalated
```
The script wraps `npx @claude-flow/cli hooks model-outcome -t ... -m ... -o ...` with explicit-argv `spawnSync` so quoting is safe. Without this signal the router does not learn from cost-tracker's recommendations and the booster bypass rate (see `cost-booster-route` skill) does not improve over time. This is the typed equivalent of the legacy `routing-outcomes` namespace (see ruflo-intelligence ADR-0001 §"Neutral").
9. **Report** -- display: ranked recommendations with savings estimate, total potential savings, implementation priority (quick wins first), and any model-outcome events emitted in step 8
## CLI alternative
```bash
npx @claude-flow/cli@latest memory search --query "cost optimization strategies" --namespace cost-patterns
npx @claude-flow/cli@latest memory store --key "opt-2026-05-04" --value '{...}' --namespace cost-patterns
```
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.