recommendations

recommendations is a skill for Claude Code, Codex from eric861129/SKILLS_All-in-one. It costs 108 tokens per session (3,402 once invoked), scanned A, original, MIT.

An integration guide for the TasteRay API, which returns personalized recommendations for areas such as films, restaurants, products, travel, and jobs. It emphasizes collecting a person's preferences and context before requesting results.

In plain words
What is it for?
Use it to build recommendation features, prepare preference and history context, rank options, explain why they fit, and handle uncertain or failed responses.
Why use it?
It helps avoid generic recommendations based on too little information. It also addresses low confidence, rate limits, and API errors when presenting results.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to build recommendation features, prepare preference and history context, rank options, explain why they fit, and handle uncertain or failed responses.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/eric861129/skills_all-in-one/recommendations
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add eric861129/SKILLS_All-in-one --skill recommendations
Clone the repo
git clone --depth 1 https://github.com/eric861129/SKILLS_All-in-one

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for recommendations

README.md
[![agentmods](https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/recommendations/github.svg)](https://agentmods.dev/skills/eric861129/skills_all-in-one/recommendations)
Your own site
<a href="https://agentmods.dev/skills/eric861129/skills_all-in-one/recommendations"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/recommendations/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for recommendations

Your own site · 80×15
<a href="https://agentmods.dev/skills/eric861129/skills_all-in-one/recommendations"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/recommendations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,402 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00108 $0.03402
Opus 5 $0.00054 $0.01701
Sonnet 5 $0.00022 $0.00680
Haiku 4.5 $0.00011 $0.00340

Measured 9d ago against content hash 4002c9d24ed9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

recommendations scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

public/SKILLS/Data & Analysis/recommendations/SKILL.md · 548 lines

How it starts

The opening of the file, as written. The whole thing — 548 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Recommendations

Personalized recommendations through the TasteRay API.

Goal

When making recommendations or reviewing recommendation-related code—whether API integrations, context building, or presentation logic—your goal is to achieve a 10/10 score.

Score all work on a 0-10 scale based on adherence to the principles and techniques in this skill. Provide your assessment as X/10 with specific feedback on what's working and what needs improvement to reach 10/10.

A 10/10 means the work:

  • Embodies the core principle (understanding precedes recommendation)
  • Builds rich context before calling the API
  • Presents recommendations with personalized explanations
  • Handles edge cases gracefully (low confidence, rate limits, errors)
  • Avoids all anti-patterns

Iterate until you reach 10/10.


Core Principle

Understanding precedes recommendation.

Great recommendations come from deep understanding of the person—their preferences, constraints, history, and context. Never call the API without first building meaningful context from the conversation.

Key insight: A recommendation is only as good as the context that informed it.


API Overview

The TasteRay Recommendation API provides personalized recommendations across multiple verticals.

Base URL

https://api.tasteray.com

Authentication

All requests require an API key in the header:

X-API-Key: your-api-key

Core Endpoints

Endpoint Method Description
/v1/recommend POST Get personalized recommendations
/v1/explain POST Get detailed explanation for a single item
/v1/usage GET Check quota and usage statistics

See: API Reference


The Recommendation Flow

Every recommendation follows this pattern:

1. Build context from conversation
   ↓
2. Call POST /v1/recommend
   ↓
3. Interpret confidence scores
   ↓
4. Present with personalized explanations
   ↓
5. Iterate based on feedback

Read the full file on GitHub · 548 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 548 lines · 108 tokens per session scan A 4002c9d24ed9

Subscribe to this mod's changes

recommendations is a skill published in the GitHub repository eric861129/SKILLS_All-in-one (52 stars, last pushed 4mo ago), licensed MIT. It adds 108 tokens to every session and 3,402 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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