AB siyuan-note-cli
Connect to and operate SiYuan Note through the siyuan-note-cli command-line tool, using notes as context for AI tasks. Use when the user asks to query, create, or modify SiYuan notes; read guides or knowledge bases from notes; manage notebooks, documents, blocks, or databases; write task results to SiYuan; or reference SiYuan notes during other work. After this skill is triggered, first read the note named "AI Assistant Guide" before performing the specific operation. Requires the local SiYuan Note client to be running and an API token to be configured.
Connect to and operate SiYuan Note through the siyuan-note-cli command-line tool, using notes as context for AI tasks.
As a process B 75/100 · Nearly there — weak spots: result and completion
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 75/100
- 0Result and completion. Does not say what the result is
- 55Failures and branches. 1 branches
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 17 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1670 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 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)
- +3Output format is not stated: the model decides each time
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +3Description length 559: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (16 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.