SKILLEMALL.ai

CC xiaozhi-teach-geography-lesson-planner

帮地理老师做以核心素养为目标、以读图和真实情境为主线的教案:初中五个主题与高中必修、选择性必修模块的单元统筹,读图任务与问题链设计,地理实验、社会调查与野外考察方案,地图使用规范。仅在"地理 + 教案设计"两个条件同时成立时建议激活,例如"地球的公转这节课怎么设计""认识日本这一课怎么用一个情境串起来""家乡的社会调查怎么组织""高一大气受热过程怎么讲""认识中国分区这部分的复习课怎么上""地理期末读图题错得多,讲评课怎么设计"。不处理:地理学业水平考试的复习排期(初中学业水平考试与高中合格性考试转 xiaozhi-teach-bio-geo-review-planner;高中等级性考试转 xiaozhi-teach-review-planner)、命题与组卷(转 xiaozhi-teach-exam-designer)、其他学科教案(转对应学科 SKILL)。

ClawHub Hermes author: 小智伴学 v2.11.1 MIT-0 15 files body ≈ 3 124 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

帮地理老师做以核心素养为目标、以读图和真实情境为主线的教案:初中五个主题与高中必修、选择性必修模块的单元统筹,读图任务与问题链设计,地理实验、社会调查与野外考察方案,地图使用规范。仅在"地理 +…

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

AnalyzerLearningtype and topics are labelled automatically from the skill text
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
C
53/100
Has gaps
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: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 386 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 "id"
  • note frontmatter-key unknown frontmatter key "min_platform_version"
  • note frontmatter-key unknown frontmatter key "max_round_limit"
  • 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 47 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3124 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 7 example trigger phrases
  • +3Description length 386: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 47 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +1License stated

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

External checks

ClawHub: clean
This is a coherent China-focused geography lesson-planning skill for teachers, with disclosed classroom data use and no executable or hidden behavior.
LLM: benign (high) · VirusTotal: · 8 Oct 2026