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 iamwaqargulzar/Marketing-Agent-OS --skill product-feed-optimizergit clone --depth 1 https://github.com/iamwaqargulzar/Marketing-Agent-OSWrote 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/iamwaqargulzar/marketing-agent-os/product-feed-optimizer)<a href="https://agentmods.dev/skills/iamwaqargulzar/marketing-agent-os/product-feed-optimizer"><img src="https://agentmods.dev/badge/skills/iamwaqargulzar/marketing-agent-os/product-feed-optimizer/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/iamwaqargulzar/marketing-agent-os/product-feed-optimizer"><img src="https://agentmods.dev/badge/skills/iamwaqargulzar/marketing-agent-os/product-feed-optimizer.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 | $0.00144 | $0.00850 |
| Opus 5.5 | $0.00058 | $0.00340 |
| Sonnet 5.5 | $0.00029 | $0.00170 |
| Haiku 4.5 | $0.00014 | $0.00085 |
Grade A, and why
product-feed-optimizer 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 2mo 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.
This is a copy
66% identical to impeccable — 318 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Feed Optimizer
Quick Start
Use this skill for product feed optimizer. Start from the user’s concrete objective and available evidence; do not substitute generic marketing advice for task-specific analysis.
Skill Contract
- Reads: user-provided context; relevant project files;
.agents/product-marketing.mdwhen present; approved public or connected data sources. - Writes: recommendations and artifacts in the response by default. Persistent file/account changes require explicit request or authorization.
- Evidence: label consequential claims as
measured,user-provided,calculated,estimated, orproxy. Never upgrade uncertainty silently. - Side effects: do not publish, send, spend, delete, mutate accounts, or persist registry truth without user authorization.
- Freshness: verify current platform rules, search eligibility, ad policies, model/tool capabilities, laws, pricing, and other time-sensitive claims before acting.
Instructions
- Define the exact
product feed optimizerobjective, audience/scope, constraints and success metric before recommending action. - Load shared product-marketing context when it materially changes the answer; ask only for missing facts that block a decision.
- Collect the minimum evidence needed for product feed optimizer. Distinguish direct observations from assumptions and proxies.
- Execute the product feed optimizer analysis or artifact using the domain checklist below; prefer specific outputs over generic best-practice lists.
- Prioritize actions by impact, confidence, effort and dependency. Identify what would falsify important assumptions.
- For external side effects, publishing, sending, spend changes, account changes or persistent writes, obtain authorization first.
- Finish with decision-ready output, evidence labels, open loops, and no more than three next-best skills.
Domain Checklist
- Business objective
- Conversion definition
- Audience/keyword segmentation
- Creative/offer fit
- Budget and bidding assumptions
- Tracking QA
- Experiment structure
- Pacing/fatigue
- Attribution caveats
What ships with it
4 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.
- 2mo ago First seen · 76 lines · 144 tokens per session scan A e2b49a727639
product-feed-optimizer is a skill published in the GitHub repository iamwaqargulzar/Marketing-Agent-OS (8 stars, last pushed 3d ago), licensed Apache-2.0. It adds 144 tokens to every session and 850 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 66% identical to impeccable, differing in 318 lines, and is treated as a copy.
product-feed-optimizer compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| product-feed-optimizeriamwaqargulzar/Marketing-Agent-OS | Skill | 8 | 3d ago | Apache-2.0 | A | 144 | Claude Code |
| shoppingansvisor/ansvisor | Skill | 232 | yesterday | MIT | A | 20 | Claude Code, Codex |
| ansvisor-aeo-coach-standaloneansvisor/ansvisor | Skill | 232 | yesterday | MIT | A | 148 | Claude Code, Codex |
| aeroCanonry/canonry | Skill | 198 | yesterday | MIT | A | 93 | Claude Code |
| refreshansvisor/ansvisor | Skill | 232 | yesterday | MIT | A | 17 | Claude Code, Codex |
| wrongclaimansvisor/ansvisor | Skill | 232 | yesterday | MIT | A | 17 | Claude Code, Codex |
| seo-vertical-detectHainrixz/claude-seo-ai | Skill | 82 | 13d ago | MIT | A | 71 | Claude Code |