AC input-classification-system
Deterministic rule-based system for classifying clarified input into a single primary task category and assigning execution complexity. Use when the Main Agent needs to categorize user requests before task decomposition, route tasks to appropriate handlers, assess complexity and risk levels, or determine if clarification is needed. Triggers after clarification is complete and before decomposition begins.
As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, consistency
How to improve
- 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: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 64/100
- 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
- 30Running it twice. 10 mutating operations with no state check
- 40Consistency. Frontmatter name (input-classification-system) differs from the folder (input-classification-v1)
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 118 steps
- 100Failures and branches. 4 branches, has a failure section
- 100Execution cost. Instruction body is 2594 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 11 top-level sections: this looks like several domains in one skill
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 407: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 118 items
- +4Has examples (0 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.