SKILLEMALL.ai
GitHub ↗ RU Sign in

BD teacher-capability-probe

对 WorkBuddy 内部其他大模型/专家技能（教师）做「能力探针与评测」，量化其能力边界与失败模式， 产出结构化能力画像——这是用户指定的核心机制「跨模型蒸馏」工程化收口的关键子能力 （发现→探针→提取→合成→对抗验证）之一。让蒸馏不再凭手感，而是用覆盖性探针任务量化教师能力边界， 指导下一步该蒸馏哪些能力、规避哪些失败模式。触发词：教师能力探针、能力评测、能力画像、 teacher probe、capability evaluation、蒸馏前评测、能力边界、失败模式分析。

ClawHub Agent Skills author: qq435912743 v1.0.0 MIT-0 5 files body ≈ 499 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

对 WorkBuddy 内部其他大模型/专家技能（教师）做「能力探针与评测」，量化其能力边界与失败模式， 产出结构化能力画像——这是用户指定的核心机制「跨模型蒸馏」工程化收口的关键子能力 （发现→探针→提取→合成→对抗验证）之一。让蒸馏不再凭手感，而是用覆盖性探针任务量化教师能力边界，…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmenttype 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%
72
Run on models
none yet
Process rating
D
46/100
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.
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: 5. 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")
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "display_name_en"
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "visibility"

Process rating: all ten parameters 46/100

  • 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
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 16 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 499 tokens
  • 100Running it twice. No mutating operations

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 242: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (2 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
The skill’s core probe tool is local and coherent, but it also includes an unrelated persistent memory module that can store usage history, free-form notes, errors, and user preferences.
LLM: suspicious (high) · 14 Aug 2026