BD knowledge-graph-skill
嵌入式知识图谱,持久化存储结构化知识,支持查询、合并、可视化与配置。嵌入式知识图谱,以JSON存储持久化结构化知识,通过CLI脚本查询,生成KGML摘要供会话上下文使用。核心能力包括KGML。支持自动化配置和灵活的参数设置，适覆盖多种使用场景，优化工作流程和效率。核心能力包括KGML。Use when 需要提升效率、自动化流程、批量处理、工作流优化时使用。不适用于需要人工创意判断的任务。 功能涵盖: knowledge, graph。 功能涵盖: skill。
嵌入式知识图谱,持久化存储结构化知识,支持查询、合并、可视化与配置。嵌入式知识图谱,以JSON存储持久化结构化知识,通过CLI脚本查询,生成KGML摘要供会话上下文使用。核心能力包括KGML。支持自动化配置和灵活的参数设置，适覆盖多种使用场景，优化工作流程和效率。核心能力包括KGML。Use when…
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
The same skill appears in 1 more place: ClawHub
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 8, column 14: description: 嵌入式知识图谱,持久化存储结构化知识,支持查询、合并、可视化与配置。嵌入式知识图谱,以JSON存储持久化结构化知识,通过CLI脚本查… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-long-hermesdescription is 232 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "summary_zh" - note
frontmatter-keyunknown frontmatter key "tools" - note
frontmatter-keyunknown frontmatter key "pricing_tier"
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
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (knowledge-graph-skill) differs from the folder (knowledge-graph-paid)
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 53 steps
- 100Execution cost. Instruction body is 3333 tokens
- low 28 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (7 tags): a typed call is more reliable
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
- +2Single-language instructions
- +3Description length 232: enough signal without eating the budget
- +4Structure: 55 headings
- +3Step-by-step instructions: 53 items
- +4Has examples (16 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.