| name | agent-cost-strategy |
| description | Tiered model selection and cost optimization for multi-agent AI workflows. Use this skill whenever you are choosing a model for a task, spinning up a sub-agent, setting up cron jobs or heartbeats, or trying to reduce API spend. Also use when the user says "save costs", "which model should I use", "optimize model usage", "this is getting expensive", or when delegating any task to a sub-agent. Works with any AI provider. |
| metadata | {"clawdbot":{"emoji":"💰","requires":{"bins":[]},"os":["linux","darwin","win32"]}} |
Agent Cost Strategy
Use the cheapest model that can reliably do the job. Most tasks don't need your most powerful model.
The Three Tiers
| Tier | When to Use | Examples |
|---|
| Fast/Cheap | Sub-agents, background tasks, automated fixes, simple lookups, short replies | Claude Haiku, GPT-4o-mini, Gemini Flash |
| Mid-tier | Main session dialogue, moderate reasoning, multi-step tasks | Claude Sonnet, GPT-4o, Gemini Pro |
| Powerful | Architecture decisions, deep reviews, hard problems, after cheaper models fail twice | Claude Opus, GPT-4.5, Gemini Ultra |
Task → Tier Routing
Fix failing tests → Fast/Cheap
Write boilerplate → Fast/Cheap
Research / search → Fast/Cheap
Cron / scheduled tasks → Fast/Cheap (always)
Short replies (hi, ok) → Fast/Cheap (always)
Background monitoring → Fast/Cheap (always)
Build new feature → Mid-tier
Review a PR → Mid-tier
Main assistant dialogue → Mid-tier (default)
Architecture decisions → Powerful
Deep code review → Powerful
Stuck after 2 attempts → Escalate one tier up
Heartbeat / Cron Model Rule
Always specify the cheapest model for scheduled and background tasks — they run frequently and costs add up fast. Check your platform's config for how to set a model per cron/heartbeat job.
For heartbeat intervals: set them just under your provider's cache TTL to keep the prompt cache warm and pay cache-read rates instead of full input rates. Check your provider's docs for the exact TTL.
Communication Pattern Rule
One-word and short conversational messages (hi, thanks, ok, sure, yes, no) should always route to Fast/Cheap. Never burn a mid-tier or powerful model on an acknowledgment.
Cache Optimization
Prompt caching cuts costs 50-90% on repeated context. Cache writes cost ~25% more but pay off after just 1-2 reuses. See references/cache-optimization.md for patterns and break-even math.
Batch API (Non-Urgent Tasks)
For cron jobs, scheduled analysis, or anything that doesn't need an immediate response — use the Batch API (Anthropic/OpenAI both offer it). 50% discount in exchange for async delivery (results within 24h). Never use real-time API for background work that can wait.
Sub-Agent Model Rule (Critical)
Always explicitly set the model when spawning sub-agents. Never rely on defaults — the default inherits the parent session model (expensive mid-tier). One month of sub-agents defaulting to Sonnet = 96% of costs going to Sonnet when it should be split ~80/20 Haiku/Sonnet.
sessions_spawn → always include model: "claude-haiku-4-5-20251001" (or equivalent fast-cheap)
Every sub-agent task that can be done by Haiku must use Haiku. No exceptions.
New Session / Machine Cold Start Cost
When starting a fresh session (new machine, new session after /new), the cache is empty. The first few messages will write the entire context (skills, workspace files, memory) to cache at 1.25x the normal input rate. This is unavoidable but temporary — it pays off within 2-3 messages once the cache warms up.
Don't panic at the first few messages being expensive on a new machine. The cache write cost is a one-time investment that makes every subsequent message ~90% cheaper.
Signs You're Over-Spending
- Running powerful models on tasks Fast/Cheap can handle
- No caching on repeated system prompts
- Heartbeat/cron jobs using the default (expensive) model
- Sub-agents spawned without explicit model = biggest cost leak
Session & Cache Management
Never delete or end sessions to "save money" — it backfires.
Anthropic's prompt cache builds from repeated context within a live session. When a session starts fresh, all context (system prompt, workspace files, skills) loads cold — typically 400-600k tokens at full cost. Once cached, subsequent messages cost ~10% of that.
The math:
- Cold session start: 600k tokens × full price = expensive
- After cache warms up: 600k tokens × 10% cache price = ~90% cheaper per message
- Deleting a session destroys the cache and forces a full cold reload next time
Rules:
- Let sessions run as long as possible
- Only start a new session (
/new) when context is genuinely full (>80%)
- Never tell a user to delete conversations — it spikes costs
- The longer a session runs, the cheaper each message gets
Delegation rule (keep main agent lean):
- Main agent (Sonnet/mid-tier) = conversational only: planning, coordination, reviewing results
- Sub-agents (Haiku/fast-cheap) = all actual doing: file edits, research, builds, data tasks
- Keeping the main agent conversational reduces its context growth and keeps cache hits high
