# Slop Check > Routes a request to Slop or Not Pro's on-device tools: detect whether text or an image is AI-generated, clean AI artifacts out of text, and score readability with each language's native formula (Flesch-Kincaid for English). Runs locally on a Mac via the SlopOrNot MCP server or the slop CLI. Use when the user asks "is this AI", "did a bot write this", "is this image AI generated", "clean the invisible characters out of this", "what reading grade is this", or invokes /slop-check. Not for rewriting or humanizing text; that is the agentic-humanizer skill. - Skill: `numen-tech/slop-check` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add numen-tech/slop-check` - Raw SKILL.md: https://api.skillmd.com/api/skills/numen-tech/slop-check/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: numen-tech (https://skillmd.com/u/numen-tech) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/numen-tech/slop-check --- # Slop Check One-shot access to Slop or Not Pro's on-device analysis. Detect AI text or images, clean AI artifacts, or score readability, then report a clear verdict. No interview, no loop. **Slash command:** `/slop-check [paste text | file path | image path]` ## Critical rules 1. For `image-detect` and explicit `image-score`, MUST prefer the app-bundle CLI when the current client can run local shell commands. Use MCP for image operations only when CLI execution is unsupported or fails. For text, readability, cleanup, and status, try MCP before CLI. 2. MUST verify Pro with a real Pro-gated call. NEVER infer Pro from `slop_status` or `slop status`; both succeed for non-Pro users. 3. NEVER block on a structured-question tool. Resolve ambiguity with the Step 1 precedence rule and state the assumption, or emit the single plain-text input request. Plain text renders on every harness. 4. NEVER create symlinks, edit shell rc files, or otherwise modify PATH. Prefer the absolute app-bundle binary at `/Applications/Slop Or Not.app/Contents/MacOS/slop` whenever using the CLI. Detection, readability, cleanup, score, and status are output-only: run them immediately, no permission needed. 5. Detection scores are 0-1 decimals; MUST multiply them by 100 for display. Readability values are grade/ease numbers and MUST NOT be converted to percentages. NEVER guess a flag or JSON field path; look it up in `references/slop-tools.md`. 6. Image checks MUST default to `detect_image`. Use `score_image` only when the user explicitly asks for a raw or absolute OmniAID score. A request to "score this image" without OmniAID still maps to `image-detect`. ## Step 1: Identify the operation and input Pick one operation from the request. Do not ask a question to disambiguate. | Operation | Trigger language | |---|---| | `text-detect` | "is this AI", "did a bot write this", "AI written" | | `image-detect` | "is this image AI", "AI generated picture", "real photo" | | `image-score` | "raw OmniAID score", "absolute OmniAID model score" | | `readability` | "reading level", "what grade", "Flesch", "how readable", or a native formula name ("LIX", "Wiener Sachtextformel", "Gulpease", "Flesch-Szigriszt", "Reading Ease") | | `cleanup` | "clean this", "strip invisible/zero-width", "remove homoglyphs" | | `status` | "is slop set up", "is Slop or Not working", "check Pro" | Resolve the input by precedence (first match wins): 1. A path ending `.png`, `.jpg`, `.jpeg`, `.heic`, or `.webp`, or an explicit image reference: image operation on that file. 2. Any other explicit file path: read that file as text. 3. Inline pasted text in the request: use it. 4. The user says to use the clipboard: `pbpaste`. Ambiguity rule (NEVER ask via a tool): if no operation verb is present, default to `text-detect` for text input and `image-detect` for image input, and state the assumption in the output, for example: "Assumed AI-detection. Ask for readability or cleanup to switch." Only if there is no usable input at all, emit exactly this one line, then stop and wait: ```text Paste the text, or give a file or image path, and say detect / clean / readability. ``` ## Step 2: Resolve a working backend Use this backend order. Stop at the first that works. 1. **Image operations with shell support: CLI first.** For `image-detect` and `image-score`, first check the app-bundle binary: ```bash ls "/Applications/Slop Or Not.app/Contents/MacOS/slop" ``` If it exists and the client can run shell commands, use that quoted binary path. This avoids converting local image files to base64. If the client has no shell or CLI execution, for example MCP-only clients such as Claude Desktop or ChatGPT connectors, skip to MCP. 2. **MCP for non-image operations, or image fallback.** Call the operation's SlopOrNot MCP tool (Claude Code surfaces these as `mcp__SlopOrNot__`; other harnesses expose the same `SlopOrNot` server tools). If it returns a result and is not a Pro-required error (`isError: true` or a Pro-required message), use it. For `status`, `slop_status` is only a health/version call: also run the Pro proof probe in `references/slop-tools.md` section 5 before reporting active Pro. Done. 3. **CLI fallback for non-image operations.** If MCP is absent or errored, check whether the app-bundle binary exists: ```bash ls "/Applications/Slop Or Not.app/Contents/MacOS/slop" ``` - If it exists: complete this turn's operation by calling the binary at its absolute quoted path. Do not modify PATH. - If it does not exist: this is genuinely not installed. Go to the Step 5 "Not installed" fallback. For `status`, run the absolute binary's `status --json` plus the Pro proof probe before reporting active Pro. 4. **Pro-gated failure.** If the CLI runs but a Pro-gated call exits non-zero or returns Pro-required, the app is installed and Pro is not active: go to the Step 5 "Installed but Pro is not active" fallback. For `status`, report Pro as inactive per Step 4 instead. Tool names, flags, parameters, and JSON field paths are in `references/slop-tools.md`. Setup and registration details are in `references/slop-setup.md`. Consult them; do not guess. ## Step 3: Run the operation Use the operation-to-tool map in `references/slop-tools.md` section 5. Key points: - `text-detect`: request readability alongside detection (`include_readability: true` on MCP; `slop text --json` returns both). When the input language is detectable, pass a normalized `language_code` (Norwegian Bokmal is `nb`; never pass the macro `no`, remap it to `nb`); omit it when ambiguous so the app auto-detects. Passing `nn` (Nynorsk) is harmless but returns `kind: "not_english"` with a null score, same as any non-English language. For non-English input `detect_text` returns `kind: "not_english"` with a null score; see Step 4. - `readability`: pass the same normalized `language_code` when detectable. Norwegian Bokmal is `nb`; always remap a detected `no` to `nb` before passing (never pass `no`; it returns `unsupported_language:no` even for Bokmal text). The returned `scores[]` `kind` depends on the language and is not always Flesch-Kincaid; read whatever kind is present (see Step 4 and `references/slop-tools.md` section 2). - Images: default to `image-detect`. When shell commands are available, use the absolute CLI path with shell redirection from the image path: `"/Applications/Slop Or Not.app/Contents/MacOS/slop" image --json < ""`. MCP uses `detect_image` with base64 (`base64 -i `) only when CLI is unsupported or fails. Use `score-image` / `score_image` only for `image-score`, and only when the user explicitly asks for a raw or absolute OmniAID score. If the user asks for a generic image score without OmniAID, keep `image-detect` and format the detection probability. - `cleanup`: keep the default removals on unless the user asked to limit them. Normalize MCP and CLI cleanup fields before formatting: MCP returns `cleaned_text` and integer counts; CLI returns `cleanedText` plus count arrays. - `status`: run the health call and then the Pro proof probe. Report active only when the Pro-gated probe succeeds. ## Step 4: Present the result Read score normalization in `references/slop-tools.md` section 2 first (detection score is a 0-1 decimal, multiply by 100; MCP `verdict` is a plain string, CLI verdict is the single-key object at `detection.result._1`). Render the matching block. **Detection (text or image):** ```markdown **Likely AI** (most_likely_ai_slop) · 87% AI probability Language: en · Sentences: 6 ``` Map the verdict by rule (verdicts have gradations like `probably_ai_slop`): `real` -> "Likely human"; contains `ai_slop` -> "Likely AI"; else title-cased. Show the label, the raw verdict in parentheses, and the percentage. When MCP `detect_text` returns `kind: "not_english"` (non-English input), `score` and `verdict` are null. For CLI `slop text --json`, normalize the same case before reading `detection.result`: if `detection.notEnglish`, `detection.not_english`, or a normalized `kind: "not_english"` is present, or if the response has a non-English `detectedLanguage` / `readability.language` and no `detection.result` or `detection.resultFewSentences`, treat the AI score and verdict as null. Do not show an AI percentage. Print instead: "AI text detection is English-only; no score available for ." (read the language from the `language`, `detectedLanguage`, or `readability.language` field), then show the readability block if the response carries one. Never invent a score for non-English input. **Readability:** Read whatever `kind` `scores[]` returns; do not assume `fleschKincaidGradeLevel`. When the language returns two kinds (English: `fleschKincaidGradeLevel` plus `fleschReadingEase`), band and lead with the Flesch-Kincaid grade and show Reading Ease as a supplemental value in parentheses; never derive the band from Reading Ease, whose 0-to-100 scale is inverted. For a single-kind language, band that one kind. Look up the formula display name, scale direction, and band ranges in `references/slop-tools.md` section 2. Show the formula name, the value, the detected language, and the band. ```markdown **Flesch-Kincaid grade: 9.7** (Reading Ease 62.4) Language: en · Band: High school · Words: 210 · Sentences: 14 ``` For non-English, label by the returned kind, for example: ```markdown **Wiener Sachtextformel: 11.2** Language: de · Band: High school · Words: 180 · Sentences: 12 ``` If `scores[]` is empty, read `warnings`. Warnings come back in two shapes by backend: MCP returns colon-tagged strings (`unsupported_language:`, `insufficient_text:NN`, `approximate_syllable_counts:`); the CLI returns camelCase objects under `readability.warnings[]` (`unsupportedLanguage`, `insufficientText`, optionally with `wordCount`). Treat the two forms as equivalent: `unsupported_language` / `unsupportedLanguage` means no score is available (report "Readability not available for in this app version"); `insufficient_text` / `insufficientText` is a soft warning (scores may still be present; treat as advisory), reported as "Not enough text to score reliably (insufficient_text:28)."; `approximate_syllable_counts` / `approximateSyllableCounts` is benign. Do not invent a value. **Cleanup:** print the counts line, then the cleaned text in its own fenced `text` block so the user can copy it verbatim. For CLI output, use `cleanedText` as the cleaned text, sum `invisibleCounts[].count`, `punctuationCounts[].count`, and `homoglyphCounts[].count`, and count `britishMappings.length`. `**Cleaned.** Invisibles: 3 · Punctuation: 5 · Homoglyphs: 1 · British: 0`, followed by a fenced `text` block containing only `cleaned_text`. **Image score (only for explicit OmniAID requests):** ```markdown **Raw image score: 0.80** (OmniAID) ``` **Status:** ```markdown **Slop or Not Pro: active** (version 1.0.9) ``` Use `inactive` when the health call works but the Pro proof probe returns a Pro-required error. When the ambiguity rule fired, prepend the one-line assumption note. ## Step 5: Fallback when Slop or Not Pro is unavailable Two distinct cases. Pick the message that matches the real state. Never tell an installed user to install the app. **Not installed** (MCP is down AND the app bundle is absent). End with exactly this paragraph: > *Slop or Not is not installed. Install Slop or Not for Mac, then unlock > Pro from inside the app: * **Installed but Pro is not active** (the `slop` binary or app bundle exists, but a Pro-gated call returned Pro-required or exited non-zero). End with exactly this paragraph: > *Slop or Not is installed but Pro is not active. Unlock Pro from inside > the app under Settings then Subscription: * In both cases, then say: see `references/slop-setup.md` for install, Pro unlock, and MCP/CLI registration. A missing PATH alone is handled by the app-bundle fallback in Step 2, not deflected here. For the `status` operation, report `Slop or Not Pro: inactive` per Step 4 instead of these paragraphs. ## Examples **Example A, text detection (no verb, ambiguity rule fires):** User: `/slop-check In today's fast-paced digital landscape, leveraging synergies is paramount.` Action: no verb, text input, default `text-detect`. MCP `detect_text` with `include_readability: true`. Output: ```markdown Assumed AI-detection. Ask for readability or cleanup to switch. **Likely AI** (most_likely_ai_slop) · 91% AI probability Language: en · Sentences: 1 ``` **Example B, image detection from a path:** User: `is this AI? ~/Desktop/art.png` Action: path ends `.png`, so `image-detect`. Shell commands are available, so use the app-bundle CLI: `"/Applications/Slop Or Not.app/Contents/MacOS/slop" image --json < "$HOME/Desktop/art.png"`. Output: ```markdown **Likely AI** (most_likely_ai_slop) · 80% AI probability ``` **Example C, readability of a file:** User: `what grade level is draft.md` Action: explicit file path, `readability`. MCP `analyze_readability` with the file contents and a normalized `language_code` when detectable. Read whatever `kind` `scores[]` returns (here English `fleschKincaidGradeLevel`). Output: ```markdown **Flesch-Kincaid grade: 7.2** (Reading Ease 71.8) Language: en · Band: Middle school · Words: 148 · Sentences: 11 ``` For a German file, the same operation reads `wienerSachtextformel4` and labels it "Wiener Sachtextformel" with the German band. **Example D, cleanup with a zero-width character:** User: `clean the invisible characters out of this: Hello​world` Action: `cleanup`. MCP `clean_text` (defaults on). Output: the counts line `**Cleaned.** Invisibles: 1 · Punctuation: 0 · Homoglyphs: 0 · British: 0`, then a fenced `text` block whose only content is `Helloworld` (the zero-width character removed). **Example E, explicit OmniAID score:** User: `give me the raw OmniAID score for ~/Desktop/art.png` Action: explicit OmniAID request, so `image-score`. Shell commands are available, so use the app-bundle CLI: `"/Applications/Slop Or Not.app/Contents/MacOS/slop" score-image --json < "$HOME/Desktop/art.png"`. If CLI execution is unsupported, use MCP `score_image` with base64 image input. Output: ```markdown **Raw image score: 0.80** (OmniAID) ``` ## Pointer files - `references/slop-tools.md`: full CLI and MCP surface, parameters, flags, JSON field paths, score normalization, Pro-gating, operation map. - `references/slop-setup.md`: install, Pro unlock, app-bundle fallback, MCP/CLI registration, troubleshooting.