SKILLEMALL.ai
GitHub ↗ RU Sign in

BF kimi-cli

使用 Kimi Code CLI 执行复杂代码任务。当用户需要： 1. 大型代码生成（完整功能模块、多文件项目） 2. 复杂代码重构（legacy 代码现代化、架构调整） 3. 深度 Bug 诊断（多文件关联错误、难以定位的问题） 4. 技术调研实现（新技术栈完整集成、PoC 开发） 5. 长时间运行的开发任务（需要持续交互） 适用场景：任务复杂度超出单次回复范围，需要多步骤自主执行或长时间运行时。 技术方案：基于 OpenClaw exec 的 PTY 模式，支持伪终端交互和后台会话。

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 1 397 tokens Open the sourcegithub.com↗ analyzed 2 d ago

使用 Kimi Code CLI 执行复杂代码任务。当用户需要： 1.

As a process F 42/100 · Will not run — References files that are not bundled: scripts/acp_check.py

IntegrationSoftware developmentAI and agentstype 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
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
Run on models
none yet
Process rating
F
42/100
Will not run
References files that are not bundled: scripts/acp_check.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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: 2. 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")
  • warning missing-ref reference to a missing file: scripts/acp_check.py

Process rating: all ten parameters 42/100

Will not run. References files that are not bundled: scripts/acp_check.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/acp_check.py
  • 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
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 37 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1397 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress

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
  • -218 emoji in the instructions: noise for the model
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 248: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 37 items
  • +4Has examples (14 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.