菜单
Two-layer AI-tell audit of any text — deterministic scanner (lexicon, constructions, stylometry with line numbers) plus a blind semantic judge. Use when the user wants to know whether text reads AI-generated, what specifically gives it away, and how to fix it.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/nagisanzenin/idiolect --skill audit命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | audit |
| description | Two-layer AI-tell audit of any text — deterministic scanner (lexicon, constructions, stylometry with line numbers) plus a blind semantic judge. Use when the user wants to know whether text reads AI-generated, what specifically gives it away, and how to fix it. |
| argument-hint | paste text or give a file path [--platform x] |
Two independent layers, merged into one report. The scanner catches what regex can see; the blind judge catches what only reading can (no stakes, hollow specificity, symmetric enthusiasm, tutorial cadence). Neither layer knows or cares who wrote the text.
ROOT="${CLAUDE_PLUGIN_ROOT:-${CODEX_PLUGIN_ROOT:-$IDIOLECT_ROOT}}"
# unset everywhere (opencode)? ROOT = two directories up from this SKILL.md
IDIO="python3 $ROOT/scripts/idiolect.py"
Save the text to a scratch file. Run $IDIO scan --file <f> --json [--platform <p>].
Spawn the blind judge — the idiolect-auditor agent — passing ONLY: the text (via the scratch file path), the platform if known, and nothing else. No authorship context, no "the user thinks this is AI," no scanner results (independence is the point; two correlated judges are one judge). On Codex, invoke it explicitly: $idiolect-auditor, audit this file: <path>. If subagent spawning is unavailable, do the semantic pass yourself in a deliberately separate step, AFTER writing down the scanner results, using the auditor's rubric (read $ROOT/agents/idiolect-auditor.md).
Merge into the report:
verdict: 62/100 — reads generated (scanner 58 · judge: generated-leaning)
deterministic tells (line-anchored):
L3 "seamlessly integrates" (lexical T1)
L7 its_not_x_its_y construction
— sentence lengths metronomic (CV 0.31; human ≥ 0.5)
semantic tells (judge):
- zero cost or stake attaches to any claim
- enthusiasm is symmetric across all six sentences
human texture present: 1 exact number; no temporal anchors, no first-person actions
top 5 fixes, in order of impact: ...
Frame it honestly, always: this is a linter verdict about how the text reads, not a forensic finding about how it was made. Skilled humans trip tells; edited AI passes. Never output "this was written by AI" — output "these N things make it read generated, here's the fix for each." If the user is auditing someone ELSE's text to accuse them (a student, an employee), say plainly: no tool can prove authorship from style, false-positive rates hit non-native writers hardest (Liang et al. 2023), and this report must not be used as evidence against a person.
If the user wants the fixes applied, hand off to /idiolect:humanize mode A with the report as the worklist.
Write a social post in one of the roster's voices (or the user's own `self` voice) — platform-native, craft-floored, scanner-verified. Use when the user wants a post, caption, announcement, reply, or any short-form text written as a distinct human voice.
One brief, N posts, N genuinely different people — voice-isolated writers, cross-post overlap detection, per-post verification receipts. Use for launching/promoting something across multiple posts or platforms without the posts sharing a fingerprint.
Rewrite existing text so it reads human — either scrubbed-and-souled in its own register, or fully re-voiced as one of the roster's people. Use when the user has a draft that "sounds like AI" or wants existing copy converted into a specific voice.