ClawHub

Wenyan Compress

v1.0.2

将白话文/英文压缩成简洁的文言文，节省token。支持保真模式、压缩比统计、忠实度检查

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by@cnskycn
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Benign
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Benign
high confidence
✓
Purpose & Capability
Name and description (compress colloquial/English into 文言文) match the included SKILL.md and the single Python file, which constructs prompts for an LLM. No extra binaries, credentials, or unrelated requirements are requested.
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Instruction Scope
SKILL.md describes features like auto language detection, fidelity checks, and compression statistics. These are consistent with LLM-driven text-processing behavior but are high-level—implementation is expected to be handled by the platform/model runtime rather than the skill code. The fidelity-check steps are described but not implemented in the shipped Python, which only prepares prompts; this is a design choice rather than a clear risk.
✓
Install Mechanism
No install spec; the skill is instruction-only with a small helper script. Nothing is downloaded or written to disk by an installer.
✓
Credentials
The skill declares no environment variables, credentials, or config paths. The functionality (prompt construction and model invocation by the host) does not justify additional secrets or access.
✓
Persistence & Privilege
always is false and the skill is user-invocable; it does not request elevated persistence or modify other skills. Autonomous model invocation is the platform default and not a unique concern here.
Assessment
This skill appears coherent and lightweight: it prepares prompts for an LLM to compress text into 文言文 and provides user-facing options (modes, fidelity checks). Before installing, confirm you are comfortable with the platform/model handling the actual text transformations (the skill itself does not call external APIs or store secrets). If you rely on precise fidelity for legal/financial texts, prefer using the described 保真 mode or avoid compressing originals that must remain exact.

Like a lobster shell, security has layers — review code before you run it.

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License

MIT-0
Free to use, modify, and redistribute. No attribution required.
Termshttps://spdx.org/licenses/MIT-0.html

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