Skill: djc00p/agent-cost-strategy (version 1.3.6)

--- 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"],"version":"1.3.6"} --- # 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 ```text 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. ```text sessions_spawn → always include model: "claude-haiku-4-5-20251001" (or equivalent fast-cheap) ``` Default sub-agent tasks to Haiku for cost efficiency. Override with a stronger model when task complexity or accuracy requirements justify it. ## 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 **Keep sessions alive when possible — longer sessions build cache and reduce costs. Only end sessions when context is genuinely full or for privacy reasons.** 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 - Ending a session destroys the cache and forces a full cold reload next time **Rules:** - Let sessions run as long as possible for cost efficiency - Only start a new session (`/new`) when context is genuinely full (>80%) or when you need a fresh privacy boundary - Ending sessions should be intentional — for privacy/data-retention reasons, not routine cost management - The longer a session runs, the cheaper each message gets **Privacy & Cache Note:** Cached context may include workspace files and memory — avoid caching sessions containing secrets or sensitive PII. If a session will cache sensitive data, plan to end it when done. **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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)