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小智追问框架 v6.2 — 结构化追问与深度分析引擎。 核心能力：接收模糊判断 → 通过结构化追问收敛 → 输出带置信度的清晰结论。 ## 声明的运行时权限 | 资源 | 用途 | 路径 | |------|------|------| | SQLite 存储 | 跨会话批评记忆 | /workspace/ask-memory.db | | Python3 | Monte Carlo 模拟 | python3 -c | | Web Search | 外部核查 | batch_web_search | | Subagent | 独立 Critic | sessions_spawn |

ClawHub Agent Skills author: GloryJack v6.2.0 MIT-0 2 files body ≈ 681 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceSoftware developmentInfrastructuretype 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
49/100
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: 2. 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")

Process rating: all ten parameters 49/100

  • 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
  • 40Consistency. Frontmatter name (ask) differs from the folder (ask-skill)
  • 100Tools and files. No external tools needed
  • 100Steps. 16 steps
  • 100Execution cost. Instruction body is 681 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 296: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (5 code blocks)

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

External checks

ClawHub: suspicious
This is a coherent analysis skill, but it saves cross-session critique memory without clear user controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026