SKILLEMALL.ai

BD 深知可信咨询

当用户咨询政策法规、政务办事、税务社保、公积金、企业补贴、资质证照、行业标准、公共服务、合规义务、企业经营政策、投资技改税惠、办事条件、材料清单、申请路径、风险判断,或要求权威依据、可信溯源、带角标答案、深知可信咨询时,使用深知可信咨询。该 Clawhub Public 版调用深知可信统一问答接口 credibleChat 获取答案和参考材料,输出带真实来源角标和来源清单的咨询答案,并默认生成本轮可交互可信核验报告 HTML(首屏核验报告单:依据溯源/引用绑定/时效检查/类型覆盖/答案自检)与移除角标的干净 Markdown。Clawhub Public 版不内置 API Key,统一通过环境变量 DKNOWC_API_KEY 注入。

ClawHub Agent Skills author: DKnownAI v1.1.0 MIT-0 12 files body ≈ 2 138 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

IntegrationInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
65
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
This is a copy of a skill from another catalog; the rating counts the canonical one: 深知可信咨询 (ClawHub)

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: 12. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • 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 "permissions"
  • note frontmatter-key unknown frontmatter key "secrets"

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 (深知可信咨询) differs from the folder (dknownai-trusted-consulting)
  • 100Tools and files. No external tools needed
  • 100Steps. 46 steps
  • 100Execution cost. Instruction body is 2138 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
  • -31 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 321: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 46 items
  • +4Has examples (11 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.

External checks

ClawHub: suspicious
The skill is coherent as a remote citation-backed policy consultant, but it gives users misleading privacy and security assurances while sending consultation content to an external service and handling API-key onboarding.
LLM: suspicious (high) · 2 Sept 2026