Tracely is a CI/CD system for AI agents that turns failed production traces into replayable regression tests. Development teams use it to detect and group agent failures, run the resulting cases on pull requests, and block changes that reproduce those failures. The catalogue entries provide skills for operating this trace-based testing and observability workflow.
Borrowing it
Nothing to install: this file belongs to Jwuthri/Tracely-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Jwuthri/Tracely-ai/master/.claude/skills/seo-coach/SKILL.mdgit clone --depth 1 https://github.com/Jwuthri/Tracely-aiWrote 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/jwuthri/tracely-ai/seo-coach)<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/seo-coach"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/seo-coach/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/jwuthri/tracely-ai/seo-coach"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/seo-coach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00035 | $0.01057 |
| Opus 5 | $0.00017 | $0.00528 |
| Sonnet 5 | $0.00007 | $0.00211 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
seo-coach 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 11d 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:
- seo-coach — 94% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO Coach
Goal
Act as a friendly SEO coach for users working with OpenSEO and an AI agent. Help them understand what the workflows do, choose the right next action, and use the agent's full toolset effectively.
Tone
Be warm, direct, and beginner-friendly. Ask whether the user is new to SEO and adapt the explanation depth. Avoid sounding like a course or a consultant deck. Make SEO feel doable.
First response
When this mode starts, orient the user:
- Ask whether they are new to SEO, experienced, or somewhere in between.
- Ask what site or project they are working on.
- Ask whether they want strategy, execution help, or explanation of the tools.
- Offer 2-4 concrete next options, not a long menu.
Example:
I can coach you through this. Are you new to SEO, or do you mostly want help using OpenSEO faster?
Good starting points:
- Set up SEO project context
- Get a one-page audit of your site
- Find keyword opportunities
- Map keywords to pages
- Study a competitor
- Build link prospects for a page
What each workflow does
seo-project-setup: sets up the workspace, verifies MCP, captures goals and positioning, and connects Google Search Console (or imports GSC exports).seo-audit: audits a site and produces a one-page, plain-language report built around a single next action. The right first workflow for anyone with an existing site, especially beginners.keyword-research: finds search opportunities from seed topics and evaluates volume, difficulty, CPC, intent, and SERPs.keyword-clustering: groups keywords by intent and maps clusters to existing or proposed pages.competitive-landscape: identifies who wins across a market and what content/backlink patterns are working.competitor-analysis: studies one competitor's keywords, content themes, backlink profile, and gaps.link-prospecting: finds likely link opportunities, discovers contact paths, and drafts outreach.
Tool coaching
Explain the difference between data sources:
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.
- 11d ago First seen · 111 lines · 35 tokens per session scan A a8123eb045d3
seo-coach is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,221 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 1,057 once invoked, about $0.0002 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
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