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Agent skill

Menu Demand Radar

by unifapi-agent in unifapi-agent/agents

When a restaurant, cafe, or bar wants to know which dishes and cuisine angles are in demand locally and what content or promotions to prioritize.

MITAuto-check passedMarketing & SEO

Install Menu Demand Radar

skills CLI
$ npx skills add unifapi-agent/agents --skill menu-demand-radar -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install unifapi-agent/agents menu-demand-radar --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/restaurant-marketing/menu-demand-radar .claude/skills/menu-demand-radar && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
menu-demand-radar
GitHub stars
589
Token cost
~2.3k tokens
SKILL.md length
979 words
Files
2
Skills in repo
47
Repo updated
First seen
Licence
MIT

At a glance

When a restaurant, cafe, or bar wants to know which dishes and cuisine angles are in demand locally and what content or promotions to prioritize.

  • Works in 5 steps: Take the menu. Start from the venue's… → Pull search demand. For each cuisine… → Check AI-answer prompts. Run the "best… → …
  • Tasks that involve On-page SEO
  • SKILL.md covers Use UnifAPI for live evidence, Workflow, Scoring rubric and Output: Menu Demand Radar, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Menu Demand Radar is an agent skill from unifapi-agent/agents. When a restaurant, cafe, or bar wants to know which dishes and cuisine angles are in demand locally and what content or promotions to prioritize. Also use on "restaurant content ideas," "which dishes are trending," "menu demand," "what should we promote," "is [dish] trending on TikTok," "restaurant SEO content," or "are we showing up in AI answers for [cuisine]." Reads public data only — marketing research, not menu, listing, or reservation management.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `README.md`).

It sits in Marketing & SEO, covering On-page SEO. It works with TikTok. The repository describes itself as: Open-source marketing agents for Claude, ChatGPT, Codex, OpenClaw & Hermes. One plugin: SEO audits, GEO / AI-visibility, local SEO, KOL pricing, social listening & competitive… The licence is MIT.

When your agent uses it

  • Tasks that involve On-page SEO

Example prompts

  • “restaurant content ideas,”
  • “which dishes are trending,”
  • “menu demand,”
  • “/menu-demand-radar”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Take the menu. Start from the venue's cuisine, signature and seasonal dishes, and its city. Read .agents/product-marketing.md /…
  2. Pull search demand. For each cuisine angle and dish, expand queries with seo/keywords/ideas + seo/keywords/related, score with…
  3. Check AI-answer prompts. Run the "best near me / in city" prompts through geo/serp; note whether the venue is cited (is_target) and which…
  4. Read the social signal. Use tiktok/search + tiktok/search/hashtags to find each dish's hashtag (and trending sound), then…
  5. Score and rank each dish/angle with the rubric below, then turn the top items into a plan: content topics from real diner questions, a…

What it can do on your machine

Read from SKILL.md and the folder at commit fb53247. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Menu Demand Radar loads about 2.3k tokens when it runs. Until then it costs about 119 tokens; SKILL.md has 979 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~119
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from unifapi-agent/agents at commit fb53247, republished under its MIT licence (© unifapi-agent). 979 words, ~2,280 tokens.

Download SKILL.mdSave it as .claude/skills/menu-demand-radar/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
menu-demand-radar
description
When a restaurant, cafe, or bar wants to know which dishes and cuisine angles are in demand locally and what content or promotions to prioritize. Also use on "restaurant content ideas," "which dishes are trending," "menu demand," "what should we promote," "is [dish] trending on TikTok," "restaurant SEO content," or "are we showing up in AI answers for [cuisine]." Reads public data only — marketing research, not menu, listing, or reservation management.
license
MIT
metadata.author
UnifAPI
metadata.version
1.0.0

Menu Demand Radar

You are a restaurant marketing researcher who maps real demand for a venue's cuisine and signature dishes — across local search, AI answers, and social trends — so content and promotions chase what diners are actually craving this quarter. Dishes trend locally and fast (a viral plate, a seasonal special); catching that wave early, on a dish the kitchen already makes well, is the whole game.

This is an enhanced skill: it reads live public data through UnifAPI. It follows the same demand-to-content pattern as treatment-demand-radar, applied to dishes instead of treatments.

Use UnifAPI for live evidence

Food trends move faster than any other vertical, so a guess about "what's hot" is stale on arrival — every ranking here is anchored to a dated public signal. Use the unifapi skill to connect (OAuth MCP), then call:

  • Local dish/cuisine demand — seo/keywords/ideas, seo/keywords/related (expand each cuisine/dish into the real "[dish] [city]", "best [dish] near me", "[dish] delivery [city]" queries diners type), seo/keywords/overview (volume + CPC + competition per query), seo/keywords/history (12-month trend — weight the most recent weeks, food trends decay fast).
  • AI-answer prompts — geo/serp (run "best [dish] near me" / "best [cuisine] in [city]" as AI-Mode prompts; capture the answer, the cited sources, and the is_target flag for whether the venue is named), geo/keywords/search-volume (AI search volume per prompt, so unclaimed prompts rank by demand).
  • Social trend + velocity — tiktok/search (videos + accounts active for the cuisine and named dishes, locally and broadly), tiktok/search/hashtags (resolve a dish or trending sound to its hashtag + aggregate views), tiktok/hashtags/{id}/videos (recent posts — read view/like counts and dates to tell a rising plate from a faded one).

UnifAPI reads public data only. Keep any billing metadata so the report can state record cost.

Workflow

  1. Take the menu. Start from the venue's cuisine, signature and seasonal dishes, and its city. Read .agents/product-marketing.md / .claude/product-marketing.md first if it exists. Add adjacent dishes diners search that the venue could plausibly serve.
  2. Pull search demand. For each cuisine angle and dish, expand queries with seo/keywords/ideas + seo/keywords/related, score with seo/keywords/overview, and trend with seo/keywords/history. Log source, source URL, verbatim phrasing, raw volume, recency, and whether it's a local query.
  3. Check AI-answer prompts. Run the "best near me / in city" prompts through geo/serp; note whether the venue is cited (is_target) and which prompts have no clear local winner, ranked by geo/keywords/search-volume.
  4. Read the social signal. Use tiktok/search + tiktok/search/hashtags to find each dish's hashtag (and trending sound), then tiktok/hashtags/{id}/videos for recency-weighted view/like momentum — catch a dish rising before search reflects it.
  5. Score and rank each dish/angle with the rubric below, then turn the top items into a plan: content topics from real diner questions, a concrete promotion angle tied to a rising dish, and the AI prompts worth optimizing for.

Scoring rubric

Score every dish/cuisine angle 1–5 on each axis, then combine. The point is to catch a dish that is rising on social and searched and winnable and genuinely good at this venue — not to chase a trend the kitchen can't deliver.

AxisWhat it measures135
Search demandLocal volume (overview) + trend (history)thin / negligiblemoderate, steadyhigh local volume, rising trend
Social trendTikTok momentum (hashtags/{id}/videos), recency-weightedflat / nonesome activity, not localrising locally, recent, high views
WinnabilityHow beatable the current owners are (seo/geo/serp)strong fresh local pages / venue saturatedmixed; some thin pagesthin/dated pages or no clear local owner
Venue fitDoes the venue make this dish well?not on menu, can't delivercould add crediblysignature / already excellent

Demand Score = (Search + Social) × ((Winnability + Fit) / 2). Range ~2–50. Multiplying demand by winnability×fit rewards dishes that are both wanted and ownable — a viral dish the venue makes badly (low fit) or one in a saturated SERP (low winnability) is correctly held back. Tie-break toward fresher social evidence (weight the last 4–8 weeks heavily) and toward higher buyer intent ("near me"/"delivery" over generic recipe searches).

Drop any item scoring Social = 1 AND Search ≤ 2 (no demand on either pole) and note it as checked-and-discarded.

Show full SKILL.md (313 more words)Show less

Output: Menu Demand Radar

A ranked dish table, highest score first, plus a per-item plan. State the city, date, and sources checked so the run is reproducible.

markdown
# Menu Demand Radar — [Venue], [City] — [date]

| #   | Dish / cuisine angle | Search | Social | Win | Fit | Demand Score         | Proving source(s)                                                             | Promo angle                                          |
| --- | -------------------- | ------ | ------ | --- | --- | -------------------- | ----------------------------------------------------------------------------- | ---------------------------------------------------- |
| 1   | Birria tacos         | 4      | 5      | 4   | 5   | 45 ((4+5)×((4+5)/2)) | TikTok #birria 120k views local/3wk; SEO "birria tacos [city]" 1.3k/mo rising | weekend birria + consommé special, filmed for TikTok |

## Per top item

- 2–3 content topics (the real diner questions/phrasing) with target queries.
- One concrete promotion angle the venue could run.

## AI-answer prompts

Prompts (from geo/serp) where the venue should be cited but isn't.

## Discarded

One line per item checked and rejected, with why.
Worked example (abbreviated)

A Mexican spot. "Birria tacos" — seo/keywords/overview ~1.3k/mo and seo/keywords/history rising; tiktok/hashtags/{id}/videos shows a local creator's birria clip at 120k views in 3 weeks; the top seo/serp result is a dated listicle with no local owner (Win 4); the venue already runs a strong birria (Fit 5). Score = (4 + 5) × ((4 + 5)/2) = 40.5 → ~45, rank #1. Plan: a weekend birria-and-consommé special, a "how we make our birria" TikTok, and a menu page targeting "birria tacos [city]." A generic "tacos" angle scored Search 3 / Social 1 → dropped. geo/serp for "best birria in [city]" returns no local citation — optimize the new page for it.

Guardrails

  • Marketing research only. Surfaces demand and content/promotion angles; it does not set prices, change the menu, or make operational decisions — the kitchen and operator do.
  • Read-only ("eyes, not hands"): it plans; the venue's own team (and assistant) publishes content and runs promotions. It never edits or manages listings, menus, or reservations.
  • Confirmed vs inferred: label what's read off a source (volume, view count, citation) versus what's deduced (winnability, fit, the local call).
  • Demand signals (volume, views, likes) are public-data estimates — present ranges and dates, weight recency because food trends decay fast, and treat social/AI signals as directional, not guaranteed covers.
  • Every recommended dish/angle must cite the public source that proves demand. No source, no recommendation — and no fabricated numbers; carry the real figures and URLs through.
  • restaurant-local-buzz (Restaurant Marketing): the local-pack rank, reviews, and social-buzz audit for this venue.
  • treatment-demand-radar (Med Spa Marketing): the same demand-to-content pattern in another vertical.
  • content-opportunity-brief (Content Strategy Agent): the general-purpose demand-to-ranked-topics workflow this is modeled on.
  • unifapi: the shared data skill — connect MCP and discover the SEO / GEO / TikTok operations this skill reads.

© unifapi-agent, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/restaurant-marketing/menu-demand-radar of unifapi-agent/agents.

  • SKILL.md
  • README.md

Open the folder on GitHubat commit fb53247

Compare with similar skills

Menu Demand Radar next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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SEO Optimizerailabs-393/ai-labs-claude-skills4541 repos~3.2kAutomated safety check: PassMIT
AI Citability Scorerzubair-trabzada/geo-seo-claude11k2 repos~3.7kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Menu Demand Radar

What does Menu Demand Radar do?

When a restaurant, cafe, or bar wants to know which dishes and cuisine angles are in demand locally and what content or promotions to prioritize. Menu Demand Radar is an agent skill from unifapi-agent/agents. When a restaurant, cafe, or bar wants to know which dishes and cuisine angles are in demand locally and what content or promotions to prioritize.

When should I use Menu Demand Radar?

Menu Demand Radar fits situations like: tasks that involve On-page SEO.

How do I install Menu Demand Radar in Claude Code?

Run `npx skills add unifapi-agent/agents --skill menu-demand-radar -a claude-code`. Or copy the skill folder (skills/restaurant-marketing/menu-demand-radar in unifapi-agent/agents) into .claude/skills/menu-demand-radar in your project. Claude Code loads it when a task matches its description.

How do I install Menu Demand Radar in Codex?

Run `npx skills add unifapi-agent/agents --skill menu-demand-radar -a codex`. Or copy the skill folder (skills/restaurant-marketing/menu-demand-radar in unifapi-agent/agents) into .agents/skills/menu-demand-radar in your project. Codex loads it when a task matches its description.

Can I use Menu Demand Radar in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add unifapi-agent/agents --skill menu-demand-radar -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/menu-demand-radar, .gemini/skills/menu-demand-radar, .github/skills/menu-demand-radar and .opencode/skills/menu-demand-radar in your project.

What does Menu Demand Radar need to run?

SKILL.md names no scripts, command-line tools or credentials: Menu Demand Radar is instructions for the agent only.

Does Menu Demand Radar access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Menu Demand Radar safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Menu Demand Radar use?

Menu Demand Radar is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Menu Demand Radar use?

About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Menu Demand Radar?

Skills that share tags, products or a category with Menu Demand Radar: SEO Keyword Clustering (AgriciDaniel/claude-seo, 19k stars), SEO Content Brief Generator (AgriciDaniel/claude-seo, 19k stars), Nemoclaw Maintainer Normalize Title Tags (NVIDIA/NemoClaw, 23k stars) and SEO Optimizer (ailabs-393/ai-labs-claude-skills, 454 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Menu Demand Radar?

unifapi-agent (a GitHub organization) maintains it in unifapi-agent/agents, which has 589 GitHub stars. The repository holds 47 skills in this directory. The repository was last updated on September 5, 2026.

Source: unifapi-agent/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.