SKILLEMALL.ai
RU Sign in

BF learning-system

AI 领域系统学习体系。管理知识图谱、深度学习笔记、实战复盘和关联网络。触发场景：学习计划、知识图谱更新、深度研究某个 AI 主题、实战复盘总结、调研后沉淀知识、每周学习回顾。当用户说'学了什么'、'总结一下'、'沉淀知识'、'复盘'、'更新图谱'、'深入研究'、'写笔记'、'学习回顾'、'review what I learned'、'update knowledge map'、'deep dive'、'recap'、'what did I learn' 时使用。当改完代码/读完论文/做完调研后需要提炼和归纳时使用。

ClawHub Agent Skills author: 青雲 v0.1.0 11 files body ≈ 958 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process F 31/100 · Will not run — References files that are not bundled: 相对路径

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: 相对路径
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

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. The text references files that are not there: add them or drop the references.
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-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: 相对路径

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: 相对路径
  • 0Tools and files. 1 referenced file(s) missing: 相对路径
  • 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 (learning-system) differs from the folder (learning-system-skill)
  • 100Steps. 59 steps
  • 100Execution cost. Instruction body is 958 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 262: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 59 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (5 of 6)
  • +3All 2 scripts are documented

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

External checks

ClawHub: suspicious
The skill is a coherent learning tracker, but its weekly review can aggregate local memory, notes, and code-change history and send a summary through Feishu without clear opt-in or preview controls.
LLM: suspicious (high) · VirusTotal: benign · 28 May 2026