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BD behavior-observability

给自主智能体/自动化流水线提供「行为可观测性」：把每一次动作（工具调用、LLM 调用、决策门、 审核结论）以结构化事件落盘，支持多维查询、指标聚合（错误率/平均时延/P95）与行为时间线回放， 便于事后审计、故障归因与策略调优。与 safety-guardrails（决策）和 human-in-loop-review（审核） 互补：它们的决策/审核事件都应 emit 到本日志，形成完整可信行为轨迹。 触发词：行为可观测性、行为追踪、审计日志、事件溯源、observability、trace、agent 监控、 行为时间线、错误率统计。

ClawHub Agent Skills author: qq435912743 v1.0.0 MIT-0 5 files body ≈ 512 tokens Open the sourceclawhub.ai↗ analyzed 36 h ago

给自主智能体/自动化流水线提供「行为可观测性」：把每一次动作（工具调用、LLM 调用、决策门、 审核结论）以结构化事件落盘，支持多维查询、指标聚合（错误率/平均时延/P95）与行为时间线回放， 便于事后审计、故障归因与策略调优。与 safety-guardrails（决策）和…

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

AnalyzerInfrastructureAI and agentstype 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. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 512 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 270: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 14 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 is not malicious, but it mixes observability with persistent preference-learning and self-modification guidance that users should review before installing.
LLM: suspicious (high) · 14 Aug 2026