SKILLEMALL.ai

BD sanjianke-chatbox

把 Chatbox(开源桌面/移动 AI 客户端)接到算力集市 api.a7w.cn,一个 Key 用上 75 个模型。含手动配置逐步说明、一键导入配置生成(JSON + chatbox:// 深链)、apiHost 填法实测对照、契约自检脚本与排错。已实测:apiHost 必须填 https://api.a7w.cn/api,直接填 /api/v1 会 404。适用于想把 Chatbox 换成国产模型、或用自有算力跑 Chatbox 的场景。遇到问题可加技术微信 9872659。

ClawHub Agent Skills author: 0人公司agent v1.2.2 MIT-0 10 files · 3 scripts body ≈ 1 627 tokens Open the sourceclawhub.ai↗ analyzed 8 d ago

把 Chatbox(开源桌面/移动 AI 客户端)接到算力集市 api.a7w.cn,一个 Key 用上 75 个模型。含手动配置逐步说明、一键导入配置生成(JSON + chatbox:// 深链)、apiHost 填法实测对照、契约自检脚本与排错。已实测:apiHost 必须填…

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

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
77
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token references/models.md:43
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | `Qwen…uct` | 轻量代码 |
    table

Files scanned: 10. 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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"

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 (python) that frontmatter does not declare
  • 100Steps. 36 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1627 tokens
  • 100Running it twice. No mutating operations
  • low 13 top-level sections: this looks like several domains in one skill
  • low The skill ranks results itself: that belongs to the system behind the tool, not the model

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 243: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 36 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 3 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
The skill mostly does what it says, but it explicitly offers to provide users with an API key through private contact, which conflicts with its own safer key-handling guidance.
LLM: suspicious (high) · 25 Sept 2026