SKILLEMALL.ai
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基于佛教经典文献，生成特定高僧大德的 AI 教学角色

The skillemall take

The skill generates an AI Buddhist teacher persona based on classical texts. The archive contains 33 files and 9 scripts with 1570 tokens of logic. No critical issues or blocks detected.

Code quality scored 62 out of 100, process score 53. Files work but lack polish: instructions are clear, rough edges remain. Models weren't run, sandbox unchecked—no data on how the persona performs live or how convincingly it reproduces historical teaching style. Wide platform support: Claude, Gemini, Cursor, Qwen, and seven others.

Install if you need a ready template for Buddhist roleplay and accept incomplete testing. If doctrinal accuracy matters—test it with your model first.

xr843/Master-skill Claude Code author: xr843 33 files · 9 scripts body ≈ 1 570 tokens Open the sourcegithub.com↗ analyzed 10 h ago

基于佛教经典文献，生成特定高僧大德的 AI 教学角色

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorSoftware developmentAI and agentsResearchtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
When it triggers w 12
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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: 33. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-short description under 40 chars: too little signal for triggering
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 3 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 35 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1570 tokens
  • 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)
  • +3Description length 26: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +2Single-language instructions
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 35 items
  • +4Reference files are cited in the instructions (6 of 7)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.