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AC local-file-organizer

本地文件整理虾 — 自动分类、重命名并归档本地文件。 当以下情况时使用此 Skill： (1) 需要自动整理本地目录（下载文件夹、桌面、项目归档等） (2) 需要按文件类型分类（文档/图片/视频/音频/压缩包/代码） (3) 需要检测并清理重复文件 (4) 需要批量重命名文件，统一命名格式 (5) 用户提到"整理文件"、"文件分类"、"自动归档"、"重命名文件"、"清理重复"、"文件管理"、"桌面整理"、"照片分类"、"文档归档"、"文件夹整理"

ClawHub Agent Skills author: Ricky v1.0.0 MIT-0 6 files body ≈ 311 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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%
99
Quality 40%
79
Run on models
none yet
Process rating
C
53/100
Has gaps
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/naming-conventions.md:16
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | 截图 | `Screenshot_YYYY-MM-DD_HHMMSS` | `Scre…000` |
    table

Files scanned: 6. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 11 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 311 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 226: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 11 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
This is a local file organizer that can move files and write local reports, so it needs careful use but does not show hidden, deceptive, or malicious behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026