SKILLEMALL.ai

BD xiaozhi-chinese-classical-revival

文言文与古诗词专项:让古人开口,也把文言题、古诗鉴赏题的答法讲清楚。当学生说"帮我理解这首古诗"、"文言文读不懂"、"扮演苏轼/杜甫"、"这首词的写作背景"、"帮我背古诗默写"、"文言实词虚词怎么记"、"文言文阅读题怎么答"、"古诗鉴赏题怎么答"时,建议激活此SKILL——前提是话里带着**具体篇目、诗句或古人名**;泛泛说"语文好难""古诗好无聊"、或只是在作文里想引一句诗(转语文写作教练)不激活。模块:古人角色扮演 + 三级跳(背会→真懂→能用)+ 游戏化背诵 + 场景匹配 + 文言基础与答题规范。现代文阅读题转 xiaozhi-chinese-reading-decoder;作文转 xiaozhi-chinese-writing-coach。

ClawHub Hermes author: 小智伴学 v1000000.12.0 MIT-0 13 files body ≈ 2 609 tokens Open the sourceclawhub.ai analyzed 34 h ago

文言文与古诗词专项:让古人开口,也把文言题、古诗鉴赏题的答法讲清楚。当学生说"帮我理解这首古诗"、"文言文读不懂"、"扮演苏轼/杜甫"、"这首词的写作背景"、"帮我背古诗默写"、"文言实词虚词怎么记"、"文言文阅读题怎么答"、"古诗鉴赏题怎么答"时,建议激活此SKILL——前提是话里带着具体篇目、诗句或古人名;泛泛说…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

Generatortype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 328 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "grade_bands"
  • note frontmatter-key unknown frontmatter key "depends_on"
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"

Process rating: all ten parameters 49/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (xiaozhi-chinese-classical-revival) differs from the folder (chinese-classical-revival)
  • 100Tools and files. No external tools needed
  • 100Steps. 16 steps
  • 100Execution cost. Instruction body is 2609 tokens
  • 100Running it twice. No mutating operations
  • low 10 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

  • +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 8 example trigger phrases
  • +3Description length 328: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (28 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: clean
This is a disclosed Chinese classical-literature tutoring skill with scoped learning-profile memory and no executable code or hidden network behavior found.
LLM: benign (high) · VirusTotal: · 7 Sept 2026