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BF Xrouter

Xrouter is an open-source inference router that sits between OpenClaw and your LLM providers. It uses a fast, hardware-aware classifier to route each request to the most cost-effective model that c...

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 2 385 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Xrouter is an open-source inference router that sits between OpenClaw and your LLM providers.

As a process F 35/100 · Will not run — References files that are not bundled: LICENSE, scripts/check_hw.js, scripts/configure_providers.js

ProcedureAzureGitHubDockerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
78/100
safety, quality, tests
Safety 60%
100
Quality 40%
44
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: LICENSE, scripts/check_hw.js, scripts/configure_providers.js
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: LICENSE
  • warning missing-ref reference to a missing file: scripts/check_hw.js
  • warning missing-ref reference to a missing file: scripts/configure_providers.js
  • warning missing-ref reference to a missing file: scripts/cloud_model_catalog.json

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: LICENSE, scripts/check_hw.js, scripts/configure_providers.js
  • 0Tools and files. 4 referenced file(s) missing: LICENSE, scripts/check_hw.js, scripts/configure_providers.js
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (Xrouter) differs from the folder (clawhub-skill-2)
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 118 steps
  • 100Execution cost. Instruction body is 2385 tokens
  • 100Progress reporting. Reports progress
  • low The response is described with custom markup (5 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +4Structure: 1 headings, hard to scan
  • +3Output format is not stated: the model decides each time
  • -5Long text without headings
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 200: enough signal without eating the budget
  • +3Step-by-step instructions: 118 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 44.