gadaalabs/claude-code-on-steroids/vector
Claude Code and agent skill by gadaalabs/claude-code-on-steroids
- community
Checking that the SKILL.md file is still in the repository.
Select optimal AI model by task complexity — route to cheap/fast models for simple tasks, capable models for complex reasoning
Install
npx vskill@latest install gadaalabs/claude-code-on-steroids/vectorSKILL.md
Model Routing
Overview
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."
Single-Session vs Subagent Context
| 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 Summary
Model Selection
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
Tier Details
Tier 0: Direct (Zero-LLM)
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.
Tier 1: Haiku — Fast/Cheap
Single file edits, simple refactors, mechanical transformations, obvious test writing, documentation, format fixes.
Tier 2: Sonnet — Standard
Multi-file features, bug fixes requiring investigation, API integration, complex test design, module-level refactoring, code review.
Tier 3: Opus — Most Capable
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.
Dynamic Model Switching
Escalate when:
- Tier 1 gets stuck → Tier 2
- Tier 2 encounters novel problem → Tier 3
- Security implications discovered → Tier 3
- Task scope grows (1 file → 5 files) → Tier 2 or 3
De-escalate when:
- Tier 3 completes architecture → Tier 2 for implementation
- Tier 2 completes design → Tier 1 for mechanical edits
Model Capabilities Reference
| 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
Routing Feedback Loop
After every routed task, append to routing_log.md:
And add/update pointer in MEMORY.md:
| 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 |
Red Flags
Never:
- Use Tier 3 for mechanical tasks (waste of money)
- Use Tier 1 for security-critical code (false economy)
- Use Tier 1 for novel problems (will get stuck)
- Stay in Tier 2 after 3 failed attempts (escalate)
- Skip Tier 3 review for architecture decisions
Final Rule
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/vector/SKILL.md
Popularity
- Repository stars
- 67
- Repository forks
- 10
- 7-day trend score
- 21
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
Works with
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