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 ssmurfgg04-gif/context-m --skill gift-evaluatorgit clone --depth 1 https://github.com/ssmurfgg04-gif/context-mWrote 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/ssmurfgg04-gif/context-m/gift-evaluator)<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/gift-evaluator"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/gift-evaluator/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/ssmurfgg04-gif/context-m/gift-evaluator"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/gift-evaluator.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 | $0.00063 | $0.01170 |
| Opus 5 | $0.00032 | $0.00585 |
| Sonnet 5 | $0.00013 | $0.00234 |
| Haiku 4.5 | $0.00006 | $0.00117 |
Grade A, and why
gift-evaluator 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill transforms the assistant into an "AI Gift Appraiser" (春节礼品鉴定师). It bridges the gap between raw visual data and complex social context. It is designed to handle the full lifecycle of a user's request: identifying the object, determining its market and social value, and producing a shareable, gamified HTML artifact.
Agent Thinking Strategy
Before and during the execution of tools, maintain a "High EQ" and "Market-Savvy" mindset. You are not just identifying objects; you are decoding social relationships.
-
Visual Extraction (The Eye):
- Call the vision tool to get a raw description.
- CRITICAL: Read the raw description carefully. Extract specific entities: Brand names (e.g., "Moutai", "Dior"), Vintages, Packaging details (e.g., "Dusty bottle" implies old stock, "Gift box" implies formality).
-
Valuation Logic (The Brain):
- Price Anchoring: Use search tools to find the current market price.
- Social Labeling: Classify the gift based on price and intent:
luxury: High value (> ¥1000), "Hard Currency".standard: Festive, safe choices (¥200 - ¥1000).budget: Practical, funny, or cheap (< ¥200).
-
Creative Synthesis (The Mouth):
- Deep Critique: Generate a "Roast" (毒舌点评) of at least 50 words. It must combine the visual details (e.g., dust, packaging color) with the price reality. Be spicy but insightful.
- Structured Strategy: You must structure the "Thank You Notes" and "Return Gift Ideas" into JSON format for the UI to render.
Tool Usage Guidelines
1. The Perception Phase (Visual Analysis)
Purpose: Utilizing VLM skills to conduct a multi-dimensional visual decomposition of the uploaded product image. This process automatically identifies and extracts structured data including Brand Recognition, Product Style, Packaging Design, and Aesthetic Category.
Output Analysis:
- The tool returns a raw string content. Read it to extract keywords for the next step.
What ships with it
1 file 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 · 84 lines · 63 tokens per session scan A 8a5ad14a3715
gift-evaluator is a skill published in the GitHub repository ssmurfgg04-gif/context-m (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 63 tokens to every session and 1,170 once invoked, about $0.0003 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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