AC hermes-smart-router
Use when switching models, saving costs, or routing queries. Automatically picks the cheapest model that can handle the job — "translate hello" routes to $0/M local, "design a database" routes to $3/M pro. 100% local classification, zero API calls for routing.
Automatically picks the cheapest model that can handle the job — "translate hello" routes to $0/M local, "design a database" routes to $3/M pro.
As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, progress reporting
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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
description-long-hermesdescription is 260 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - note
frontmatter-keyunknown frontmatter key "triggers"
Process rating: all ten parameters 58/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 60Consistency. The Hermes dialect needs category and tags
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 14 steps, 1 vague phrases
- 100Failures and branches. 3 branches, has a failure section
- 100Execution cost. Instruction body is 1163 tokens
- 100Running it twice. No mutating operations
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -2localhost URLs: will not work for another user
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 260: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (4 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.