Model Intel

Live LLM model intelligence from OpenRouter. Compare pricing, search models by name, find the best model for any task — code, reasoning, creative, fast, chea...

Security Scan

The skill's behavior mostly matches its description (it queries OpenRouter for model data), but the declared requirement for OPENROUTER_API_KEY is inconsistent with the code (the key is never used) and the script will try to read ~/.openclaw/workspace/.env — these mismatches merit caution.

Purpose & Capability

Name/description match the code's functionality (fetch models, price, compare). However the registry and SKILL.md declare OPENROUTER_API_KEY as the primary credential even though the included script performs requests to https://openrouter.ai/api/v1/models without supplying any Authorization header or using the key. Requiring a key in metadata that the code does not use is an inconsistency.

Instruction Scope

SKILL.md instructs running the included Python script which calls OpenRouter. The script also attempts to read ~/.openclaw/workspace/.env to extract OPENROUTER_API_KEY if the env var is unset. The README/metadata did not call out that the script will read that local config file; reading a user's dotfile is scope creep and could expose unrelated secrets if the logic were extended.

Install Mechanism

No install spec or remote downloads — the skill is shipped as a small Python script and usage docs. No archives or third-party installers are fetched at runtime.

Credentials

The only declared required environment variable is OPENROUTER_API_KEY, which is reasonable for an OpenRouter integration — except the code does not actually use that key for its API calls. Additionally, the script will parse ~/.openclaw/workspace/.env to obtain that key if absent; that means the skill will read a local credential file even though the declared env var is supposedly sufficient.

Persistence & Privilege

The skill does not request persistent/always-on privileges, does not modify other skill configs, and does not write files or install services. Autonomous invocation is allowed but is the platform default.

What to consider before installing

This skill appears to do what it says — it queries OpenRouter for model info — but there are two mismatches you should be aware of: (1) the registry/README require OPENROUTER_API_KEY, yet the script does not use that key when calling the API, and (2) the script will try to read ~/.openclaw/workspace/.env to find the key if the environment variable is missing. Before installing or supplying credentials, consider: inspect the script yourself (it's short) to confirm no hidden network endpoints; avoid providing unrelated secrets in the .env file; run the script in a sandbox or isolated environment if you must test it; or ask the author to either remove the misleading metadata or to implement proper authenticated requests so the declared requirement matches actual behavior.

Live LLM model intelligence — pricing, capabilities, and comparisons from OpenRouter.

When to Use

• Finding the best model for a specific task (coding, reasoning, creative, fast, cheap)
• Comparing model pricing and capabilities
• Checking current model availability and context lengths
• Answering "what's the cheapest model that can do X?"

Usage

# List top models by provider
python3 {baseDir}/scripts/model_intel.py list

# Search by name
python3 {baseDir}/scripts/model_intel.py search "claude"

# Side-by-side comparison
python3 {baseDir}/scripts/model_intel.py compare "claude-opus" "gpt-4o"

# Best model for a use case
python3 {baseDir}/scripts/model_intel.py best fast
python3 {baseDir}/scripts/model_intel.py best code
python3 {baseDir}/scripts/model_intel.py best reasoning
python3 {baseDir}/scripts/model_intel.py best cheap
python3 {baseDir}/scripts/model_intel.py best vision

# Pricing details
python3 {baseDir}/scripts/model_intel.py price "gemini-flash"

Use Cases

Command	When
best fast	Need lowest latency
best cheap	Budget-constrained
best code	Programming tasks
best reasoning	Complex logic/math
best vision	Image understanding
best long-context	Large document processing

Credits

Built by M. Abidi | agxntsix.ai YouTube | GitHub Part of the AgxntSix Skill Suite for OpenClaw agents.

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