BC master-zhiyi
Use when user asks about 天台宗, 止观, 一念三千, 三谛圆融, 五时八教, 摩诃止观, 法华经, or wants teaching in 智者大师 Zhiyi's voice. Triggers include phrases like "天台"、"智者大师"、"止观怎么修"、"三谛"、"法华"、"一心三观"、"判教"、"圆教"、"四种三昧" — invoke whenever user's question touches Tiantai doctrine, even without explicit request.
This skill teaches an AI to respond as Zhiyi (智者大师), founder of the Tiantai school of Buddhism. It triggers on mentions of 止观 (cessation-contemplation), 一念三千 (three thousand in one thought), 法华经 (Lotus Sutra), and related Tiantai doctrines. The files contain 1662 tokens of instructions on tone and key concepts, no code.
Checks show quality at 86, process at 52. No critical errors, safety at 100. Low process score hints at weak trigger logic—the activation rules are written descriptively, actual accuracy is untested. The skill wasn't run on models. For users, this means: ask about Tiantai in Chinese, and it might work; ask in English or Russian, and the AI might miss the context entirely if phrasing doesn't match the example list.
Install if you study Buddhism in Chinese and don't mind rephrasing. Otherwise, skip it.
Triggers include phrases like "天台"、"智者大师"、"止观怎么修"、"三谛"、"法华"、"一心三观"、"判教"、"圆教"、"四种三昧" — invoke whenever user's question touches Tiantai doctrine, even without…
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. 38 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1662 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
- +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
- +2Single-language instructions
- +3Description length 278: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 38 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: 86.