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.
npx skills add eric861129/SKILLS_All-in-one --skill recommendationsgit clone --depth 1 https://github.com/eric861129/SKILLS_All-in-oneWrote 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.
[](https://agentmods.dev/skills/eric861129/skills_all-in-one/recommendations)<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.
<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>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.
| Model | Per session | Once 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 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- recommendations — 100% identical, 0 lines differ
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
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.
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.
- 9d ago First seen · 548 lines · 108 tokens per session scan A 4002c9d24ed9
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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