AC master-daoxuan
Use when user asks about 律宗, 南山律, 四分律, 戒律, 持戒, 受戒, 戒体, 戒法, 戒相, 止持, 作持, 持犯, 羯磨, 五戒, 八关斋, 忏悔, 理忏, 事忏, 行事钞, 威仪, or wants teaching in 道宣律师 Daoxuan's voice. Triggers include phrases like "道宣"、"南山律"、"律宗"、"四分律"、"行事钞"、"戒体"、"止持作持"、"受五戒"、"犯戒了怎么办"、"戒律是不是小乘"、"出家威仪" — invoke whenever user's question touches Vinaya / precepts / Nanshan school, even without explicit request.
The skill triggers on Buddhist Vinaya terminology: 律宗, 南山律, 四分律, 戒律 and related terms. It switches the AI into Daoxuan's voice—a historical master of the Nanshan precepts school. Eight files in the package, no critical errors, quality score 91. Currently it's just a trigger with instructions, no pre-written responses or dialogue examples. In practice: the AI gets told to change tone but lacks concrete texts on how to answer questions about 戒体 or 羯磨. Support is broad—Claude, Gemini, Qwen, and eight other platforms. Install if you need a keyword-triggered context switch, but expect the AI to improvise the voice rather than reference prepared material.
Triggers include phrases like "道宣"、"南山律"、"律宗"、"四分律"、"行事钞"、"戒体"、"止持作持"、"受五戒"、"犯戒了怎么办"、"戒律是不是小乘"、"出家威仪" — invoke whenever user's question touches Vinaya /…
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
The same skill appears in 1 more place: Master-skill
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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "lineage" - note
frontmatter-keyunknown frontmatter key "dates" - note
frontmatter-keyunknown frontmatter key "sources" - note
frontmatter-keyunknown frontmatter key "citation_format" - note
frontmatter-keyunknown frontmatter key "verified_by" - note
frontmatter-keyunknown frontmatter key "verified_at"
Process rating: all ten parameters 52/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 43 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1902 tokens
- 100Running it twice. No mutating operations
- low 11 top-level sections: this looks like several domains in one skill
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 362: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 43 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.