- name
- modelsense
- description
- >
- user-invocable
- true
ModelSense Skill
Purpose
ModelSense helps users pick the optimal model and effort level for their task. It does NOT route automatically on every request (use a provider plugin for that). It's an on-demand advisor: ask it a question, get a clear recommendation with reasoning.
When to trigger
- User asks: "which model for X?", "should I use Opus or Sonnet?", "what effort level?"
- User wants to understand what a benchmark means
- User wants ModelSense to auto-switch the session model
Inputs to collect (infer from context, ask only if truly unclear)
- Task description — what is the user trying to do?
- Effort preference (optional):
quick/balanced/deep/research
- If not specified, infer from task urgency/complexity
- Auto-switch? — does the user want ModelSense to apply the recommendation automatically?
Recommendation Process
Step 1 — Task Analysis
Classify the task across these dimensions:
- Domain: code, math, reasoning, writing, dialogue, document analysis, multimodal, research
- Complexity: simple / moderate / complex / research-grade
- Output type: text, code, JSON, long-form, structured data
- Context length needed: short (<8K), medium (8–32K), long (32K+), very long (100K+)
- Special requirements: function calling, thinking/CoT, multimodal, speed-sensitive
Step 2 — Benchmark Matching
Cross-reference task domain with relevant benchmarks from data/benchmarks.yaml.
| Benchmark | Best for |
|---|---|
| HumanEval / SWE-bench | Code generation, debugging, engineering |
| GPQA | Graduate-level science & research |
| MATH / AIME | Mathematical reasoning |
| MMLU | General knowledge, multidomain QA |
| Needle-in-Haystack | Long-context retrieval |
| MT-Bench / Arena Elo | Dialogue, writing quality |
| BBH (Big-Bench Hard) | Complex reasoning, multi-step logic |
Step 3 — Effort × Model Matrix
| Effort | Target quality | Typical model tier |
|---|---|---|
quick | Good enough, fast | Haiku / Flash / GLM |
balanced | High quality, reasonable cost | Sonnet / GPT-4o |
deep | Best available, thinking on | Opus / o3 |
research | No cost limit, maximum quality | Opus + thinking=high |
Step 4 — Provider Filter
Check the user's available providers:
- Run:
openclaw models listvia exec tool (or read from context) - Only recommend models the user can actually use
- Flag when a top pick requires a provider they haven't configured
Step 5 — Output the Recommendation
Format:
🎯 Recommended: <model>
⚡ Effort: <level>
📊 Why: <1-2 sentence benchmark-grounded rationale>
🔧 Special: <thinking on? function calling? etc.>
💰 Cost estimate: <rough $/M or relative>
Alternatives:
- <model B> — if you want faster/cheaper
- <model C> — if you want higher qualityAuto-Switch Behaviors
Option A: Advisory only (default)
Just output the recommendation. Tell user: "Run /model <name> to switch."
Option B: Switch current session
If user confirms or says "yes switch" / "apply it":
session_status(model="<provider/model>")Notify user: "✅ Switched to X for this session. Run /model default to reset."
Option C: Delegate task to best model
If user says "just do it with the best model":
sessions_spawn(
task="<original task>",
model="<recommended model>",
thinking="<level>"
)Data Files
data/benchmarks.yaml— benchmark definitions, score leaders, task mappingsdata/models.yaml— model catalog (updated via GitHub Actions weekly)
Examples
User: "I need to write a Solidity audit report" → Domain: code + security + long-form → Benchmarks: SWE-bench, HumanEval → Recommendation: claude-opus-4-6 with thinking=high, effort=deep
User: "Quick summary of this Slack thread" → Domain: dialogue, short → Recommendation: claude-haiku-4-5 or gemini-flash, effort=quick
User: "Prove this mathematical conjecture" → Domain: math, research-grade → Benchmarks: MATH, AIME, GPQA → Recommendation: o3 or claude-opus-4-6 with thinking=high, effort=research