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 T4wroot/agentic-seo --skill feedbackgit clone --depth 1 https://github.com/T4wroot/agentic-seoWrote 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/t4wroot/agentic-seo/feedback)<a href="https://agentmods.dev/skills/t4wroot/agentic-seo/feedback"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/feedback/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/t4wroot/agentic-seo/feedback"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/feedback.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 | $0.00068 | $0.00645 |
| Opus 5 | $0.00034 | $0.00322 |
| Sonnet 5 | $0.00014 | $0.00129 |
| Haiku 4.5 | $0.00007 | $0.00064 |
Grade A, and why
feedback-page-generator 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 6d 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
100% identical to feedback-page-generator — 0 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pages: Feedback / Roadmap
Guides feedback and roadmap pages that collect user input and communicate product direction. Often integrates with Canny, FeatureBase, UserVoice, or similar. Supports product-led growth and community engagement.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Initial Assessment
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read it for product and roadmap priorities.
Identify:
- Tool: Canny, FeatureBase, UserVoice, custom, or embedded form
- Scope: Feedback only, roadmap only, or both
- Primary goal: Collect requests, show transparency, build community
- Audience: Users, prospects, or both
Page Structure
| Section | Purpose |
|---|---|
| Headline | "Share Your Ideas" or "Product Roadmap" |
| Value | We listen; your input shapes the product |
| Feedback | Form or embed; categories (feature, bug, other) |
| Roadmap | In progress, planned, completed; or link to external board |
| Process | How we prioritize; what happens after you submit |
| CTA | Submit idea, vote, view roadmap |
Best Practices
Feedback Collection
- Low friction: Few fields; optional details
- Categories: Feature request, Bug, General
- Duplicate detection: "Similar ideas" to merge votes
Roadmap Display
- Status: In progress, planned, completed
- Transparency: Don't over-promise; "Exploring" vs "Committed"
- Update regularly: Stale roadmap hurts trust
Integration
- Embed: Canny, FeatureBase embed on your domain
- Or link: /feedback → feedback.yourproduct.com
- SEO: Often noindex for external boards; index if on your site
Output Format
- Headline and value proposition
- Page structure (feedback + roadmap sections)
- Process copy (how we use feedback)
- Integration notes (Canny, etc.)
- SEO (index vs noindex)
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.
- 6d ago First seen · 68 lines · 68 tokens per session scan A ebc70b7ef492
feedback-page-generator is a skill published in the GitHub repository T4wroot/agentic-seo (15 stars, last pushed 7d ago), licensed MIT. It adds 68 tokens to every session and 645 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to feedback-page-generator, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
fire-your-seo-agency
A procedure for improving how a website appears in search engines and how AI answer systems find and cite it. It covers search, answer-engine, generative-AI, and Naver visibility.
content-thicken
Evidence-driven blog driver. Takes ONE target from the fused search plus AI-answer content library, pulls the real questions it has to answer, drafts a thick long-form guide against a template contract, validates it deterministically, and stops at preview. Two modes, thicken an already-earning post IN PLACE (never…
backlink-outreach
Find, evaluate, pitch and track natural backlink and content partnerships. Prospects come from the GEO citation data rather than a generic blog search: the targets are the pages an AI already cites when answering your category questions. Research runs on the Monid tool layer (web search and scrape, authority metrics…
geo-monitor
Run and read the GEO pipeline, which measures whether AI answer engines mention, recommend and cite your site. Puts a fixed registry of real user questions to an answer engine through the Monid tool layer, detects the three signals plus competitors, and turns "a rival is named and we are not" into a tracked work…
seo-intake
Run and read the SEO intake pipeline. Pulls the organic keyword set for your domain and for each competitor through the Monid tool layer, computes the gap locally, and routes every keyword to the one action that can help it (write-new / striking / build-depth / defend / noise). Use when asked to refresh the SEO queue…
sf-tasks
Builds a prioritized task backlog from a Screaming Frog export or audit.json, with a configurable pipeline (which severities to include, grouping by issue type or URL, P1/P2/P3 priorities, effort estimates, and limits). For broken links, each task includes the location (source/destination/position/XPath). Use when…