clawdbites
Extract recipes from Instagram reels. Use when a user sends an Instagram reel link and wants to get the recipe from the caption. Parses ingredients, instructions, and macros into a clean format.
Install
mkdir -p .claude/skills/clawdbites && curl -L -o skill.zip "https://mcp.directory/api/skills/download/6465" && unzip -o skill.zip -d .claude/skills/clawdbites && rm skill.zipInstalls to .claude/skills/clawdbites
About this skill
Instagram Recipe Extractor
Extract recipes from Instagram reels using a multi-layered approach:
- Caption parsing — Instant, check description first
- Audio transcription — Whisper (local, no API key)
- Frame analysis — Vision model for on-screen text
No Instagram login required. Works on public reels.
When to Use
- User sends an Instagram reel link
- User mentions "recipe from Instagram" or "save this reel"
- User wants to extract recipe details from a video post
How It Works (MANDATORY FLOW)
ALWAYS follow this complete flow — do not stop after caption if instructions are missing:
- User sends Instagram reel URL
- Extract metadata using yt-dlp (
--dump-json) - Parse the caption for recipe details
- Check completeness: Does caption have BOTH ingredients AND instructions?
- ✅ YES: Present the recipe
- ❌ NO (missing instructions or incomplete): Automatically proceed to audio transcription — do NOT stop or ask the user
- If audio transcription needed:
- Download video:
yt-dlp -o "/tmp/reel.mp4" "URL" - Extract audio:
ffmpeg -y -i /tmp/reel.mp4 -vn -acodec pcm_s16le -ar 16000 -ac 1 /tmp/reel.wav - Transcribe:
whisper /tmp/reel.wav --model base --output_format txt --output_dir /tmp - Merge caption ingredients with audio instructions
- Download video:
- Present clean, formatted recipe (combining caption + audio as needed)
- User decides what to do (save to notes, add to wishlist, etc.)
Completeness check heuristics:
- Has ingredients = contains 3+ quantity+item patterns (e.g., "1 cup flour", "2 lbs chicken")
- Has instructions = contains action verbs (blend, cook, bake, mix, pour, add) + sequence OR numbered steps
Extraction Command
yt-dlp --dump-json "https://www.instagram.com/reel/SHORTCODE/" 2>/dev/null
Key fields from JSON output:
description— The caption containing the recipeuploader— Creator's namechannel— Creator's handlewebpage_url— Original URLlike_count— Popularity indicator
Recipe Parsing
Look for these patterns in the caption:
Macros:
- "X Calories | Xg P | Xg C | Xg F"
- "Macros per serving"
- "Cal/Protein/Carbs/Fat"
Ingredients:
- Lines starting with quantities (1 cup, 2 tbsp, 24oz)
- Lines with measurement units
- Emoji bullet points (🥩 🌽 🧀 etc.)
Sections:
- "For the [component]:"
- "Ingredients:"
- "Instructions:"
- "Directions:"
Output Format
Present extracted recipe cleanly:
## [Recipe Name]
*From @[handle]*
**Macros (per serving):** X cal | Xg P | Xg C | Xg F
### Ingredients
- [ingredient 1]
- [ingredient 2]
...
### Instructions
1. [step 1]
2. [step 2]
...
---
Source: [original URL]
User Actions After Extraction
Let the user decide what to do:
- "Save to my recipes" → Save to Apple Notes (if meal-planner skill available)
- "Add to wishlist" → Save to
memory/recipe-wishlist.json - "Just show me" → Display only, no save
- "Plan this for next week" → Hand off to meal-planner skill
Wishlist Storage
Optional storage for recipes user wants to try later:
memory/recipe-wishlist.json:
{
"recipes": [
{
"name": "Recipe Name",
"source": "instagram",
"sourceUrl": "https://instagram.com/reel/...",
"handle": "@creator",
"addedDate": "2026-01-26",
"tried": false,
"macros": {
"calories": 585,
"protein": 56,
"carbs": 25,
"fat": 28,
"servings": 3
},
"ingredients": [...],
"instructions": [...]
}
]
}
Error Handling
If yt-dlp fails:
- Check if URL is valid Instagram reel format
- May be a private account — inform user
- Suggest user paste caption text manually as fallback
If no recipe found in caption (IMPORTANT):
After extracting, scan the caption for recipe indicators:
- Ingredient quantities (numbers + units like oz, cups, tbsp, lbs)
- Recipe sections ("For the...", "Ingredients:", "Instructions:")
- Cooking verbs (bake, cook, sauté, mix, combine)
- Macro information (calories, protein, carbs, fat)
If none found, tell the user clearly:
"I pulled the caption but it doesn't look like the recipe is there — it might just be a teaser or the recipe is only shown in the video itself. Here's what the caption says:
[show caption]
A few options:
- Check the comments — sometimes creators post recipes there
- Check their bio link — might lead to the full recipe
- Describe what you saw in the video and I can help find a similar recipe"
Recipe detection heuristics:
HAS_RECIPE if caption contains:
- 3+ ingredient-like patterns (quantity + food item)
- OR "recipe" + ingredient list
- OR macro breakdown + ingredients
- OR numbered/bulleted instructions
NO_RECIPE if caption is:
- Mostly hashtags
- Just a description/teaser
- Under 100 characters
- No quantities or measurements
Integration with meal-planner
The meal-planner skill can reference this skill:
- When planning meals, check wishlist for untried recipes
- Suggest wishlist recipes that match pantry items
- Mark recipes as "tried" after they're used in a meal plan
Audio Transcription (V2) — MANDATORY FALLBACK
When caption is missing instructions, ALWAYS transcribe the audio automatically. Do not stop and ask the user — just do it. This is the most common case since creators often put ingredients in captions but speak the instructions.
Step 1: Download video
yt-dlp -o "/tmp/reel.mp4" "https://instagram.com/reel/XXX"
Step 2: Extract audio
ffmpeg -i /tmp/reel.mp4 -vn -acodec pcm_s16le -ar 16000 -ac 1 /tmp/reel.wav
Step 3: Transcribe with Whisper
/Users/kylekirkland/Library/Python/3.14/bin/whisper /tmp/reel.wav --model base --output_format txt --output_dir /tmp
Step 4: Parse transcript for recipe Look for cooking instructions, ingredients mentioned verbally.
Inference for Missing Measurements
ALWAYS infer quantities when not provided. Never present a recipe without amounts — estimate based on context and standard package sizes.
Vague Language → Specific Amounts
| What they say | Infer |
|---|---|
| "some chicken" | ~1 lb |
| "a bit of garlic" | 2-3 cloves |
| "handful of spinach" | ~2 cups |
| "drizzle of oil" | 1-2 tbsp |
| "season to taste" | ½ tsp salt, ¼ tsp pepper |
| "splash of soy sauce" | 1-2 tbsp |
| "a few tablespoons" | 2-3 tbsp |
| "some rice" | 1 cup dry |
| "cheese on top" | ½ - 1 cup shredded |
| "diced onion" | 1 medium onion |
| "bell peppers" | 2 peppers |
Standard Package Sizes (when item mentioned without amount)
| Ingredient | Standard Package | Infer |
|---|---|---|
| Puff pastry | 17oz sheet | 1 sheet |
| Ground beef/turkey | 1 lb pack | 1 lb |
| Chicken breast | ~1.5 lb pack | 1.5 lbs |
| Sausage links | 14oz / 4-5 links | 1 package |
| Bacon | 12oz / 12 slices | ½ package (6 slices) |
| Shredded cheese | 8oz bag | 1-2 cups |
| Tortillas | 8-10 count | 1 package |
| Canned beans | 15oz can | 1 can |
| Broth/stock | 32oz carton | 1-2 cups |
| Pasta | 16oz box | 8oz (half box) |
| Rice | 2 lb bag | 1-2 cups dry |
Context-Aware Scaling
By recipe type:
- Stir fry for 2 → 1 lb protein, 4 cups veggies
- Soup/stew → 1.5-2 lbs protein, 4 cups broth
- Sheet pan meal → 1.5 lbs protein, 3-4 cups veggies
- Appetizers → smaller portions, estimate ~12-15 pieces per batch
By servings mentioned:
- "Serves 4" → Scale standard amounts for 4
- "Meal prep for the week" → Assume 5-8 servings
- No servings mentioned → Default to 4 servings
By protein target (if user has macro goals):
- 40-50g protein per serving → ~6-8oz cooked meat per portion
- Scale recipe protein accordingly
Output Format
Always present inferred amounts clearly:
### Ingredients
- 1 lb ground turkey *(estimated)*
- 1 medium onion, diced *(estimated)*
- 2 cups broth *(estimated based on typical soup)*
Mark inferred quantities with (estimated) so user knows what came from the source vs inference.
Combined Extraction Flow
1. TRY CAPTION (instant)
└── yt-dlp --dump-json → parse description
└── Recipe found? → DONE ✅
└── Check for "pinned" / "in comments" / "check comments" → FLAG
2. IF FLAGGED: CHECK FOR CREATOR COMMENT
└── Look through comments for creator's username
└── If creator comment found with recipe → DONE ✅
└── If not found → continue + notify user
3. TRY AUDIO (30-60 sec)
└── Download video
└── Extract audio with ffmpeg
└── Transcribe with Whisper (base model)
└── Parse transcript for recipe
└── Infer missing measurements
└── Recipe found? → DONE ✅
4. PRESENT RESULTS + PROMPT IF NEEDED
└── Show what was extracted from audio
└── If "pinned" was flagged, tell user:
"The creator mentioned the full recipe is pinned in the comments.
I extracted what I could from the audio, but if you want the
exact measurements, paste the pinned comment here and I'll
merge it with what I found."
5. TRY FRAME ANALYSIS (if audio incomplete)
└── Extract 5-8 key frames with ffmpeg
└── Send to Claude vision
└── Ask: "Extract any recipe text, ingredients, or measurements shown"
└── Merge findings with audio transcript
6. FALLBACK (nothing found)
└── Inform user: "Recipe wasn't in caption or audio/video"
└── Offer: search for similar recipe based on video title/description
Frame Analysis
Extract key frames and analyze with vision model.
Extract frames:
# Extract 1 frame every 5 seconds
ffmpeg -i /tmp/reel.mp4 -vf "fps=1/5" /tmp/frame_%02d.jpg
# Or extract specific number of frames evenly distributed
ffmpeg -i /tmp/reel.mp4 -vf "select='not(mod(n,30))'" -vsync vfr /tmp/frame_%02d.jpg
Send to vision model: Use Claude's image analysis to read each frame:
- Recipe cards / title screens
- Ingredient lists shown on screen
- Measurements in text overlays
- Step-by-step inst
Content truncated.
More by openclaw
View all skills by openclaw →You might also like
flutter-development
aj-geddes
Build beautiful cross-platform mobile apps with Flutter and Dart. Covers widgets, state management with Provider/BLoC, navigation, API integration, and material design.
drawio-diagrams-enhanced
jgtolentino
Create professional draw.io (diagrams.net) diagrams in XML format (.drawio files) with integrated PMP/PMBOK methodologies, extensive visual asset libraries, and industry-standard professional templates. Use this skill when users ask to create flowcharts, swimlane diagrams, cross-functional flowcharts, org charts, network diagrams, UML diagrams, BPMN, project management diagrams (WBS, Gantt, PERT, RACI), risk matrices, stakeholder maps, or any other visual diagram in draw.io format. This skill includes access to custom shape libraries for icons, clipart, and professional symbols.
godot
bfollington
This skill should be used when working on Godot Engine projects. It provides specialized knowledge of Godot's file formats (.gd, .tscn, .tres), architecture patterns (component-based, signal-driven, resource-based), common pitfalls, validation tools, code templates, and CLI workflows. The `godot` command is available for running the game, validating scripts, importing resources, and exporting builds. Use this skill for tasks involving Godot game development, debugging scene/resource files, implementing game systems, or creating new Godot components.
ui-ux-pro-max
nextlevelbuilder
"UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 8 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient."
nano-banana-pro
garg-aayush
Generate and edit images using Google's Nano Banana Pro (Gemini 3 Pro Image) API. Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports both text-to-image generation and image-to-image editing with configurable resolution (1K default, 2K, or 4K for high resolution). DO NOT read the image file first - use this skill directly with the --input-image parameter.
fastapi-templates
wshobson
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
Related MCP Servers
Browse all serversUnlock Instagram insights to boost advertising and marketing services. Analyze interactions for better IG engagement and
Transcribe for YouTube and other platforms. Extract accurate transcript of a YouTube video for accessibility, analysis,
Unlock AI-ready web data with Firecrawl: scrape any website, handle dynamic content, and automate web scraping for resea
Browser Use lets LLMs and agents access and scrape any website in real time, making web scraping and web page scraping e
Extend your developer tools with GitHub MCP Server for advanced automation, supporting GitHub Student and student packag
Serena is a free AI code generator toolkit providing robust code editing and retrieval, turning LLMs into powerful artif
Stay ahead of the MCP ecosystem
Get weekly updates on new skills and servers.