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AC huo15-juxingyi-configure

用聚星逸 fsk- 密钥调 /v1/models 接口,把最新模型列表写入 openclaw.json。模型列表完全来自接口实时返回,不依赖本地硬编码数据。支持日常更新(--update 保留主模型)、切换、查看。一个 Key 调 50+ 顶级大模型。

ClawHub Agent Skills author: Job Zhao v1.3.0 MIT-0 11 files body ≈ 1 694 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

用聚星逸 fsk- 密钥调 /v1/models 接口,把最新模型列表写入 openclaw.json。模型列表完全来自接口实时返回,不依赖本地硬编码数据。支持日常更新(--update 保留主模型)、切换、查看。一个 Key 调 50+ 顶级大模型。

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsWriting and documentsMedia and videotype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 0

✓ No critical or high findings

Files scanned: 11. 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 "displayName"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "aliases"

Process rating: all ten parameters 50/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
  • 100Steps. 43 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1694 tokens
  • 100Progress reporting. Reports progress
  • low 11 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (9 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 126: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 43 items
  • +4Has examples (11 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 1 scripts are documented
  • +1License stated

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

External checks

ClawHub: clean
This skill does what it says: it configures a Juxingyi model provider for OpenClaw, with disclosed network use, local config writes, backups, and plaintext-key tradeoffs.
LLM: benign (high) · VirusTotal: · 19 Jul 2026