SKILLEMALL.ai

BC xiaozhi-teach-renewal-report

用学员真实的学习记录做一份阶段报告,让续课变成家长看完事实后的自主选择。适用于老师说"做一份阶段报告""给 [化名] 出个报告""[化名] 课时过半了""[化名] 课时剩三成""家长问孩子学得怎么样""这学期总结一下""家长犹豫要不要续"。流程:汇总课后记录与作业错因 → 写事实/进步/计划三段 → 无逐知识点分数时只出定性判断 → 给续课建议与话术。出报告前须先指定学员化名;它会读这名学员跨月的学习记录,家长可见的内容一律先过授权检查。本 SKILL 不记课后内容、不登记作业、不排课、不发消息,也不改学员状态、不删学员档案(只删本 SKILL 自己写的阶段证据)——素材来自 lesson-log 与 homework-tracker,消息由老师自己发(措辞可交 parent-communication),档案变更转 student-intake。

ClawHub Hermes v2.1.12 12 files body ≈ 3 657 tokens Open the sourceclawhub.ai analyzed 2 d ago

用学员真实的学习记录做一份阶段报告,让续课变成家长看完事实后的自主选择。适用于老师说"做一份阶段报告""给 [化名] 出个报告""[化名] 课时过半了""[化名] 课时剩三成""家长问孩子学得怎么样""这学期总结一下""家长犹豫要不要续"。流程:汇总课后记录与作业错因 → 写事实/进步/计划三段 →…

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

ProcedureData and analyticsLearningInfrastructuretype 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
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: 12. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 379 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. 25 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3657 tokens
  • 100Running it twice. No mutating operations
  • low 16 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 379: enough signal without eating the budget
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 25 items
  • +4Has examples (20 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: 73.

External checks

ClawHub: clean
This skill reads a named student's learning records to draft renewal reports, with clear limits on consent, sending, storage, and deletion.
LLM: benign (high) · VirusTotal: