SKILLEMALL.ai
GitHub ↗ RU Sign in

BD li_local_pdf_translate-linux

使用本地llama.cpp模型（如Hy-MT2-7B）批量翻译PDF文档，结合学术翻译三步法（直译→反思→雅化），支持中/英/日/韩/法/德/西/俄八大主要语言任意互译，逐页提取+翻译，按目标语言输出.md文件（Ubuntu/Linux 平台）

ClawHub Agent Skills author: Terry S Fisher v1.0.4 MIT-0 11 files · 1 script body ≈ 2 024 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

使用本地llama.cpp模型（如Hy-MT2-7B）批量翻译PDF文档，结合学术翻译三步法（直译→反思→雅化），支持中/英/日/韩/法/德/西/俄八大主要语言任意互译，逐页提取+翻译，按目标语言输出.md文件（Ubuntu/Linux 平台）

As a process D 41/100 · Unfinished process — 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
B
86/100
safety, quality, tests
Safety 60%
99
Quality 40%
66
Run on models
none yet
Process rating
D
41/100
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-password-literal start_server.sh:7
    Hard-coded password / key literal (may be an example) (quoted — discussed, not commanded)
    API_KEY="${API_…025}"
    quoted

Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "display_name_en"
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "visibility"

Process rating: all ten parameters 41/100

  • 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
  • 40Consistency. Frontmatter name (li_local_pdf_translate-linux) differs from the folder (li-local-pdf-translate-linux)
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 37 steps
  • 100Execution cost. Instruction body is 2024 tokens
  • 100Running it twice. No mutating operations
  • low 15 top-level sections: this looks like several domains in one skill

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
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +3Description length 122: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 37 items
  • +4Has examples (15 code blocks)
  • +3All 6 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: clean
This skill is a disclosed local-document translation helper; its main privacy caveat is that user-configured remote API endpoints would receive document text.
LLM: benign (high) · VirusTotal: · 5 Sept 2026