BD self-improvement
捕获学习成果、错误和纠正，以实现持续改进，当出现以下情况，使用本技能： (1) 命令或操作意外失败， (2) 用户纠正 '不，那是错的...'，'实际上...'）， (3) 用户请求不存在的功能， (4) 外部 API 或工具失败， (5) 意识到其知识已过时或不正确， (6) 发现更好的方法处理重复任务。在执行重要任务前也要回顾学习记录。"
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 41/100
- 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 (self-improvement) differs from the folder (self-improving-x)
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 100Steps. 85 steps
- 100Execution cost. Instruction body is 1953 tokens
- 100Running it twice. No mutating operations
- low 14 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 172: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 85 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.
External checks
ClawHub: suspicious
This prompt-only skill is not malware, but it can automatically save conversation/error details and change future agent guidance without clear consent or redaction controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026