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 madguyevans-creator/resale-agent-skill-hub --skill broker-recognizegit clone --depth 1 https://github.com/madguyevans-creator/resale-agent-skill-hubWrote 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/madguyevans-creator/resale-agent-skill-hub/broker-recognize)<a href="https://agentmods.dev/skills/madguyevans-creator/resale-agent-skill-hub/broker-recognize"><img src="https://agentmods.dev/badge/skills/madguyevans-creator/resale-agent-skill-hub/broker-recognize/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/madguyevans-creator/resale-agent-skill-hub/broker-recognize"><img src="https://agentmods.dev/badge/skills/madguyevans-creator/resale-agent-skill-hub/broker-recognize.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 | $0.00095 | $0.00436 |
| Opus 5 | $0.00048 | $0.00218 |
| Sonnet 5 | $0.00019 | $0.00087 |
| Haiku 4.5 | $0.00010 | $0.00044 |
Grade A, and why
broker-recognize 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 10d 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.
What it actually says
broker-recognize: Photo → Product Info
Extracts structured product information from user-uploaded photos for C2C resale listing purposes.
Prerequisite: ANTHROPIC_API_KEY
This skill uses the Anthropic API (Claude Vision) for image analysis. Your current LLM model may not support multimodal vision, so the skill calls the Vision API directly.
export ANTHROPIC_API_KEY="sk-ant-..."
If the key is missing, the skill will output an error instructing the user to set it.
Workflow
- User provides 1-3 photos of the item
- Analyze using vision: brand, product name, model, category, condition (A-E), material, color, size, features, flaws, estimated original retail
- Present structured card to user for confirmation/correction
- Save product info to conversation context for next skill in pipeline
Condition Grade Scale
| Grade | Label | Criteria |
|---|---|---|
| A | Like New | No visible wear, original packaging if applicable |
| B | Excellent | Minor signs of use, no significant flaws |
| C | Good | Visible wear, minor flaws, fully functional |
| D | Fair | Noticeable wear/flaws, may need minor repair |
| E | For Parts | Significant damage, sold as-is |
Key Rule
Be honest about flaws. Transparency is the foundation of C2C trust. A lost honest sale is better than a return from undisclosed flaws.
Script
scripts/recognize.py provides the structured prompt template and output schema.
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.
- 10d ago First seen · 44 lines · 0 tokens per session scan A f8a255f34b64
broker-recognize is a skill published in the GitHub repository madguyevans-creator/resale-agent-skill-hub (94 stars, last pushed 3mo ago), licensed MIT. It adds 95 tokens to every session and 436 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 2026-08-30.
Other skills, from other repositories
seller-research
Use when researching a merchant, storefront, marketplace seller, or merchant of record for a buying decision, especially when identity, refund terms, fulfillment, counterfeit risk, domain history, or independent buyer outcomes are uncertain.
Product Photoshoot Workflow
A workflow for turning one product photo into a set of four e-commerce images from different visual directions.
sfcc-performance
Performance optimization strategies for Salesforce B2C Commerce Cloud including caching, efficient data retrieval, index-friendly APIs, and job optimization. Use when asked about SFCC performance, caching strategies, or optimization.
sfcc-ocapi-hooks
Guide for implementing OCAPI hooks in Salesforce B2C Commerce. Use this when asked to create OCAPI hooks, extend API endpoints, validate API requests, or modify API responses.
sfcc-scapi-hooks
Guide for implementing SCAPI hooks in Salesforce B2C Commerce. Use this when asked to create SCAPI hooks, extend Shopper API endpoints, validate API requests, or modify API responses for headless commerce.
sfcc-sfra-scss
Best practices for styling and theming SFRA storefronts using SCSS. Use when asked to create style overrides, theming, responsive layouts, or CSS customizations in SFCC.