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-audit/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-audit)<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/seo-audit"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/seo-audit/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-audit"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/seo-audit.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 | $0.00031 | $0.01300 |
| Opus 5 | $0.00015 | $0.00650 |
| Sonnet 5 | $0.00006 | $0.00260 |
| Haiku 4.5 | $0.00003 | $0.00130 |
Grade A, and why
seo-audit 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.
This is a copy
86% identical to seo-audit — 13 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO SEO Audit
Goal
Audit a domain and produce a one-page HTML report that anyone, including a complete SEO beginner, can read once and act on. The whole report exists to support ONE action the owner can take this week; everything else is supporting detail.
Use this when asked for an SEO audit or review of a domain, especially when the output is a shareable report for a non-expert. For expert-facing analysis of a competitor or market, use competitor-analysis or competitive-landscape instead.
Required inputs
- Domain to audit
projectId(uselist_projects; if no project matches the domain, create one withcreate_project)
OpenSEO MCP tools
whoami: confirm connection and remaining credits before spending anything. If OpenSEO is not connected, stop and ask the user to connect it.list_projects/create_project: resolve theprojectId.run_site_audit: start the crawl (default page budget), then pollget_audit_statusand readget_audit_issues. Useget_audit_pageswhen per-page evidence helps.get_backlinks_overview: backlink and referring-domain picture; usually the deciding data for the "one thing".get_domain_overview: estimated organic traffic and organic keyword count. Skip when the site is clearly dead.research_keywords: keyword ideas with volume and difficulty, used to propose a starting focus area. One call with 1-3 seeds taken from what the site is actually about. Skip when the site is down.
Keep total spend modest: one audit, one backlinks overview, at most one domain overview, and at most one keyword-research call. Only the overview and keyword lookups spend credits.
Workflow
whoami, then resolve theprojectId.run_site_auditfor the domain. While it crawls, fetchget_backlinks_overview.- When the crawl finishes, read
get_audit_issues(andget_domain_overviewif the site is alive). - If the audit comes back broken or nearly empty (certificate errors, 5xx, one page crawled): investigate before writing. Check the certificate and redirect variants yourself, and search the web for the business. A dead domain often has a live successor site, which flips the whole recommendation to "redirect the old domain".
- Verify every finding you plan to report against the live page HTML by fetching pages yourself. Report nothing you have not seen evidence for.
- Decide the one thing. Derive it from the data, never from generic advice. Common patterns:
- Clean site, no backlinks: outreach to guests, partners, or directories, with a ready-to-send message.
- Dead domain, live successor site: permanent redirect via hosting support, with the exact sentence to send them.
- Blocked or noindexed pages: remove the block. It must be doable this week by a non-technical person, with copy-paste-ready mechanics included.
- When the site is healthy, propose a starting focus area: run one
research_keywordscall seeded from the site's actual topic, then pick one theme and 3 to 5 specific, low-difficulty keywords the site can realistically rank for, each with the page or post to make. This is a starting direction, not a keyword strategy; point the user at thekeyword-researchskill for the full workflow. Skip this step entirely when the site is down — the one thing is all that matters there. - Write the report using
template.htmlin this skill directory (see Output format). - Review before delivering: run an adversarial pass with a second agent or model if your environment has one, otherwise do a fresh self-review. Give the reviewer the verified facts and have it attack four things: claims beyond the facts, unglossed jargon, anything overwhelming for a beginner, and dramatic language. The reviewer may also flag true facts it was not given; check those against your evidence instead of "fixing" them.
- Deliver the report: if your environment can publish or preview HTML (for example as an artifact), do that; otherwise save the HTML file and tell the user to open it in their browser.
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 · 64 lines · 31 tokens per session scan A 7ecfd2df3ea0
seo-audit is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,221 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 1,300 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to seo-audit, differing in 13 lines, and is treated as a copy.
Other skills, from other repositories
Prompt Version Control Workflow
Sets up a prompt versioning system with naming conventions, diff tracking, A/B evaluation gates before promotion, and rollback triggers.
llm-tester
You are the LLM Tester, specializing in systematic prompt evaluation, red-teaming, and LLM output quality assurance. You replace "vibes-based" AI evaluation with rigorous, automated, and repeatable verification suites.
report-publisher
Publish an already validated report to an external release destination.
report-validator
Validate local report.json files without publishing or modifying them.
report-repair
Repair invalid local report.json files by inserting required report fields.
local-validator
Validate a local report.json file with a deterministic check-only script and no network access.