Recursos
2Instal·la
npx skillscat add g-eskayo/marvin/route Instal·la des del registre de SkillsCat.
SKILL.md
Route Skill
Auto-routes tasks to the optimal profile + model combination based on bench-validated cost/capability data.
Routing table (bench-validated)
| Intent | Profile | Model | Savings | When to use |
|---|---|---|---|---|
| recall | marvin | claude-haiku-4-5-20251001 | ~60% | Memory/session history retrieval |
| research | marvin | claude-haiku-4-5-20251001 | ~60% | arXiv, papers, synthesis, ChromaDB queries |
| coding | lean | default (Sonnet) | ~9% on simple tasks; unreliable on hard tasks (Run 15) | Self-contained coding tasks with no memory dependency |
| architecture | marvin | default (Sonnet) | baseline | Design decisions, planning, trade-off analysis |
Triggers
Load this skill when the user asks:
- "which profile should I use for X"
- "should I use haiku for this"
- "route this task"
- "which model is best for"
- "how should I launch"
When triggered, run the classifier and print the routing recommendation:
~/.agents/venv/bin/python ~/.agents/skills/route/scripts/route.py "<task description>"For in-session auto-routing (session start only): if the user's first message clearly signals a specific task type (≥2 keyword matches from the routing table), surface the routing suggestion briefly:
"This looks like a recall task —
claude-recall(marvin + haiku) handles it at ~60% of this session's cost."
Only say this once, at the top of the session. Don't repeat it mid-session.
CLI usage
# After running install.sh:
route "what were the bench results?" # classify + print
route "fix the bug" --launch # classify + exec claude
route --recall --launch # explicit mode + launch
route --table # full routing table
route --aliases # print alias definitions
# Aliases (after install):
claude-recall # marvin profile + Haiku
claude-research # marvin profile + Haiku
claude-code # lean profile + Sonnet
claude-arch # marvin profile + Sonnet (default)Install
bash ~/.agents/skills/route/install.sh
source ~/.zshrcEvidence base
| Finding | Source |
|---|---|
| MARVIN wins recall (1.00 vs 0.00) at ~+600 token overhead | bench Run 7, task-002 + task-011 |
| MARVIN + Haiku wins recall identically, 60% cheaper | bench Run 8, Haiku cross-model |
| Lean saves 9–10% on SIMPLE coding tasks with zero quality gap | bench Runs 2–6, tasks 001/003/005/006 |
| On HARD coding tasks lean is NOT reliably cheaper than marvin — flips per task, high variance; clean is cheapest on both but lacks lean's TDD/grill quality benefit | bench Run 15 (N=3), tasks 012/013 |
| Haiku fails navigational recall without MARVIN | bench Run 8, task-007 |
| Haiku matches Sonnet on recall independently confirmed via a second method | bench Run 15, select_model.py sweep on task-002-recall |