SKILLEMALL.ai

BC xiaozhi-weekly-review

把一周零散的学习记录整理成有结论、有证据、有下一步的周报,并带学生做一次自我复盘。学生说“帮我生成周报”“这周学习复盘”“总结一下这周”“下周重点是什么”时可激活;说“这个月怎么样”的转学习系统协调器。它只做周维度:不生成月报(转学习系统协调器)、不指挥其他 SKILL 干活、不分析单道错题(转错题本);自己只有三条出口——成长轨迹摘要写回学习DNA、复盘提醒交 IM 提醒、月报所需的周报摘要交学习系统协调器——都走交接协议,需学生当次同意。家庭版需要学生授权后才生成,学生可以否决。

ClawHub Hermes author: 小智伴学 v2.1.12 MIT-0 11 files body ≈ 1 834 tokens Open the sourceclawhub.ai analyzed 2 d ago

把一周零散的学习记录整理成有结论、有证据、有下一步的周报,并带学生做一次自我复盘。学生说“帮我生成周报”“这周学习复盘”“总结一下这周”“下周重点是什么”时可激活;说“这个月怎么样”的转学习系统协调器。它只做周维度:不生成月报(转学习系统协调器)、不指挥其他 SKILL…

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

AnalyzerData and analyticstype 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: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 244 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 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. 41 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1834 tokens
  • 100Running it twice. No mutating operations
  • low 15 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 5 example trigger phrases
  • +3Description length 244: enough signal without eating the budget
  • +4Structure: 29 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (13 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 coherent weekly learning review skill with consent-gated memory, sharing, and reminders, though its trigger wording and handover schema should be tightened.
LLM: benign (medium) · VirusTotal: · 7 Sept 2026