agentleFS
mikemorgensternmikemorgenstern/model-router/skills/recommend/SKILL.md

recommend

Skill0 starsChanged 10 months agoMITView on GitHub

Get a model recommendation for a specific task type. User runs /model-router:recommend [task_type] or asks "what model should I use for X?" If not provided, ask the user what they're trying to do. Read ~/.model-router/config.yaml and ~/.model-router/models.yaml If config doesn't exist: If task_type argument provided, use it. If not, ask: Map their response to a task_type. From models.yaml, get the recommendations for this task type: - best: Highest quality option - runner_up: Good alternative - budget: Cheapest option Check which…

7 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 6 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If not provided, ask the user what they're trying to do.

    line 13
  2. Very specific:

    Read ~/.model-router/config.yaml and ~/.model-router/models.yaml

    line 19
  3. Distinctive:

    If config doesn't exist:

    line 21
  4. Distinctive:

    If task_type argument provided, use it.

    line 28
  5. Distinctive:

    Check which models the user actually has access to:

    line 55

“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Model Router Recommend

Get a model recommendation for a specific task type.

Trigger

User runs /model-router:recommend [task_type] or asks "what model should I use for X?"

Arguments

  • task_type (optional): One of critical_code, code_review, reasoning, math_logic, simple_edits, bulk_operations, writing, analysis

If not provided, ask the user what they're trying to do.

Instructions

1. Load Configuration

Read ~/.model-router/config.yaml and ~/.model-router/models.yaml

If config doesn't exist:

No configuration found. Run /model-router:setup first.

2. Determine Task Type

If task_type argument provided, use it.

If not, ask:

What kind of task are you working on?

○ Code review / refactoring
○ Critical production code
○ Reasoning / math / logic
○ Simple edits / formatting
○ Bulk operations / batch processing
○ Writing / documentation
○ Analysis / summarization
○ Something else (describe it)

Map their response to a task_type.

3. Get Recommendations

From models.yaml, get the recommendations for this task type:

  • best: Highest quality option
  • runner_up: Good alternative
  • budget: Cheapest option

4. Filter by Availability

Check which models the user actually has access to:

  • Claude models → Always available (they're using Claude Code)
  • GPT models → Requires chatgpt_plus: true AND codex CLI installed
  • Gemini models → Requires google_ai_pro: true AND gemini CLI installed
  • Ollama models → Requires ollama.enabled: true AND model in ollama.models list

5. Apply Routing Preference

Based on preferences.routing:

preserve_claude: Order by non-Claude first

1. Ollama models (free)
2. ChatGPT models (via Codex)
3. Gemini models (via Gemini CLI)
4. Claude models (last resort)

quality_first: Order by quality tier

1. best model for task
2. runner_up
3. budget option

budget_first: Order by cost

1. Free (Ollama)
2. Cheap (Gemini Flash, Haiku)
3. Moderate (Sonnet, GPT-5.2)
4. Expensive (Opus)

6. Present Recommendation

Task: [task_type_description]

Recommended models (based on your setup):

1. [model_name] [RECOMMENDED]
   Provider: [provider]
   Why: [reason based on task + user preference]
   How: [CLI command or "Use Claude directly"]

2. [model_name]
   Provider: [provider]
   Why: [alternative reason]
   How: [CLI command]

3. [model_name]
   Provider: [provider]
   Why: [budget/fallback reason]
   How: [CLI command]

---
To use: Tell me "use [model_name]" or run the CLI command directly.

Example Output

Task: Code Review

Recommended models (based on your setup):

1. GPT-5.2 via Codex [RECOMMENDED]
   Provider: OpenAI (ChatGPT Plus)
   Why: Preserves Claude usage, HumanEval 91%, included in your subscription
   How: codex exec -m gpt-5.2 "Review this code: [paste code]"

2. Gemini 3 Pro
   Provider: Google (AI Pro)
   Why: Strong coding benchmark (76% SWE-bench), good alternative
   How: gemini -y -m gemini-3-pro-preview "[prompt]"

3. Claude Sonnet 4.5
   Provider: Anthropic (Claude Pro)
   Why: Best quality (92% HumanEval), uses Claude subscription
   How: I'll handle this directly

---
To use: Tell me "use GPT-5.2" or run the codex command.

If No Models Available

No models available for [task_type].

You may need to:
- Set up CLI tools: /model-router:configure
- Add subscriptions: /model-router:setup
- Install Ollama for free local models

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.