Routes coding tasks to optimal AI model tier by complexity: no LLM for mechanical edits, Haiku for simple refactors, Sonnet for multi-file bugs, Opus for architecture/security. Saves 50-65% API costs.
How this skill is triggered — by the user, by Claude, or both
Slash command
/claude-code-superpowers:vectorThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
**VECTOR** — *A vector has both direction and magnitude — it points precisely at the right target.*
VECTOR — A vector has both direction and magnitude — it points precisely at the right target. When invoked: evaluates task complexity and routes to the least-capable model that can succeed — Tier 0 (no LLM) through Tier 3 (Opus). Systematic routing cuts API costs 50–65% vs treating everything as Opus.
Core principle: Use the least capable model that can handle the task — save cost and latency without sacrificing quality.
Announce at start: "Running VECTOR to assign the correct model tier."
| Context | How VECTOR applies |
|---|---|
| Main session | Tier selection is advisory — informs reasoning depth, not model invoked |
| Spawning subagents (COMMANDER or PHANTOM) | Tier selection is literal — specify model when dispatching |
| Multi-agent swarms (LEGION) | Assign each agent a tier based on its sub-task complexity |
Full cost savings come from subagent dispatch: Haiku for mechanical tasks, Opus only for critical reasoning.
TIER 3: Opus 4.6 — Architecture, security-critical, cross-system, novel problems, review of Tier 2 work
TIER 2: Sonnet 4.6 — Multi-file features, bug investigation, API integration, complex test design
TIER 1: Haiku 4.5 — Single file edits, simple refactors, obvious test writing, docs, format fixes
TIER 0: No LLM — Mechanical single-pattern transforms (sed/awk/prettier/eslint)
Security-critical OR novel problem OR cross-system integration → Tier 3 Touches 3+ files OR requires investigation → Tier 2 Single file, mechanical, obvious → Tier 1 No judgment needed, pure pattern → Tier 0
Latency: <1ms | Cost: $0 | Accuracy: 100% for exact patterns
Gate — ALL must be true: mechanical transform, unambiguous pattern, single file, reversible.
Examples: var → const (sed), sort imports (eslint --fix), format (prettier --write), rename single identifier (sed).
If Tier 0 fails → escalate to Tier 1.
Single file edits, simple refactors, mechanical transformations, obvious test writing, documentation, format fixes.
Multi-file features, bug fixes requiring investigation, API integration, complex test design, module-level refactoring, code review.
Architecture decisions, security-critical code, cross-system integration, novel problem-solving, elusive bugs (survived 3+ fix attempts), review of Tier 2/3 work before merge.
Escalate when:
De-escalate when:
| Capability | Haiku | Sonnet | Opus |
|---|---|---|---|
| Simple edits | ✓ | ✓ | ✓ |
| Test writing (basic) | ✓ | ✓ | ✓ |
| Multi-file features | ✗ | ✓ | ✓ |
| Bug investigation | ✗ | ✓ | ✓ |
| Architecture design | ✗ | △ | ✓ |
| Security analysis | ✗ | △ | ✓ |
| Novel problem-solving | ✗ | △ | ✓ |
| Code review | ✗ | △ | ✓ |
✓ = Strong | △ = Adequate | ✗ = Not recommended
After every routed task, append to routing_log.md:
| <date> | <task-1-line> | Tier N | Success/Escalated | <why> |
And add/update pointer in MEMORY.md:
- [Routing: <task-type>](routing_log.md) — Tier N → [Success|Escalated|Failed]
| Pattern | Adjustment |
|---|---|
| Tier 1 escalated to Tier 2 ≥ 3x | Start that task type at Tier 2 |
| Tier 2 escalated to Tier 3 ≥ 2x | That pattern = Tier 3 default |
| Tier 3 completed trivially | Over-routed — note for next time |
Never:
Tier 0: mechanical single-pattern transforms — $0
Tier 1: single file, obvious behavior — cheap/fast
Tier 2: multi-file, investigation — standard
Tier 3: architecture, security, novel — capable
Default to lowest tier that can handle it
Escalate when stuck, de-escalate after hard parts resolved
npx claudepluginhub gadaalabs/claude-code-on-steroidsRoutes tasks to Haiku, Sonnet, or Opus based on complexity to optimize cost and quality. Use for intelligent model selection and tiered routing.
Routes Claude Code tasks to optimal models (Haiku, Sonnet, Opus) using decision matrices, cost tables, complexity signals, and subagent assignments for cost/quality tradeoffs.
Recommends optimal Claude model (Haiku, Sonnet, Opus) for tasks using decision matrix for technical, business, strategy, creative, and command complexity.