SKILLEMALL.ai

BC quotation-writer

软件项目报价专家:结构化需求分析 × 工作量拆解 × 精美HTML报价文档输出。 凡用户提到以下任何情形,必须立即调用本技能: - 帮我做一份报价 / 帮我出报价单 / 帮我写报价文件 - 客户有需求,帮我估工期 / 估工时 / 估人天 - 这个项目怎么报价 / 这个需求多少钱 - 帮我分析工作量 / 帮我拆解需求 - 客户提了一些功能,帮我算一下要多少时间 - 我要给客户做一份项目评估 / 项目工时表 不适用于:非软件/IT类项目报价、纯财务报价单、与项目工作量无关的定价。

ClawHub Agent Skills author: BaoAI v1.0.0 MIT-0 3 files body ≈ 1 294 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

GeneratorSales and CRMInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
97
Quality 40%
72
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-high-entropy-token quotation-template.html:233
    High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
    <svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V…m-5 14H7…v2z"/></svg>
    placeholder
  • low Secrets in code secret-high-entropy-token quotation-template.html:422
    High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
    <span class="td-ico"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V…m-5 14H7…m0-
    placeholder
  • low Secrets in code secret-high-entropy-token quotation-template.html:430
    High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
    <span class="td-ico"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V…m-5 14H7…m0-
    placeholder

Files scanned: 3. 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 57/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 40Consistency. Frontmatter name (quotation-writer) differs from the folder (software-quotation-skill)
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 107 steps
  • 100Execution cost. Instruction body is 1294 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 240: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 107 items
  • +4Has examples (2 code blocks)

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

External checks

ClawHub: clean
This is a coherent software quotation-writing skill with no evidence of credential access, persistence, destructive behavior, or hidden data collection.
LLM: benign (high) · VirusTotal: · 29 May 2026