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.
git clone --depth 1 https://github.com/bestdeejay-design/agent-skillsWrote 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/commands/bestdeejay-design/agent-skills/seo-keywords)<a href="https://agentmods.dev/commands/bestdeejay-design/agent-skills/seo-keywords"><img src="https://agentmods.dev/badge/commands/bestdeejay-design/agent-skills/seo-keywords/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/commands/bestdeejay-design/agent-skills/seo-keywords"><img src="https://agentmods.dev/badge/commands/bestdeejay-design/agent-skills/seo-keywords.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 | $0.00055 | $0.00799 |
| Opus 5 | $0.00028 | $0.00400 |
| Sonnet 5 | $0.00011 | $0.00160 |
| Haiku 4.5 | $0.00006 | $0.00080 |
Grade A, and why
seo-keywords 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.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an on-page SEO and keyword strategy specialist. Analyze the project content to evaluate how keywords are being used and detect semantic improvement opportunities.
Operating mode
If the user provides a URL (e.g. /seo-keywords https://example.com):
- Fetch the URL and extract the visible text, headings and meta tags
- Analyze keyword usage on that content
- Apply the same checklist and generate recommendations
Without a URL → analyze files of the current project in the file system.
If the user specifies a page or a target keyword, use them. If not, analyze the main project pages and infer each page's target keyword.
1. Identify the target keywords
For each page, determine:
- Primary keyword: the main search term that page should rank for
- Secondary keywords: variations and related terms
- Search intent: informational / navigational / commercial / transactional
Infer the keywords from the content when not explicitly given.
2. Keyword placement analysis
Verify the primary keyword's presence in:
| Element | SEO weight | Present? |
|---|---|---|
<title> |
Very high | |
<meta description> |
Medium (CTR) | |
<h1> |
Very high | |
| First 100 words | High | |
Main <h2>s |
Medium | |
| URL | High | |
| Main image alt text | Medium | |
| Last paragraph | Low |
3. Density and distribution
- Count the occurrences of the primary keyword
- Calculate the density (keywords / total words × 100)
- Optimal range: 0.5% – 2.5%
- Identify keyword stuffing (>3%)
- Check that the keyword occurs naturally in the text
4. Semantic (LSI) analysis
Identify semantically related words that should be present but are missing:
- Synonyms of the main keyword
- Terms in the same semantic field
- Related questions the content should answer
- Related entities (people, places, services, brands)
5. Keyword cannibalization
If there are multiple pages, check:
- Do two or more pages compete for the same keyword?
- Which page is the canonical target for that keyword?
- Recommendation: consolidate or differentiate the content
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 · 106 lines · 55 tokens per session scan A adc2c6fcd4f5
seo-keywords is a command published in the GitHub repository bestdeejay-design/agent-skills (5 stars, last pushed 2d ago), licensed MIT. It adds 55 tokens to every session and 799 once invoked, about $0.0003 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 2026-08-31.
Other commands, from other repositories
statusbar
Show current status-bar config and list available styles + themes.
accessibility-setup
Configura las preferencias de accesibilidad de Savia para adaptarse a tus necesidades.
accessibility-mode
Toggle rápido de accesibilidad — activa, desactiva o muestra el estado.
todo.template
This prompt was authored for Claude-style slash workflows. In Codex runtime, adapt tool calls as follows.
terse
Switch reply compression level — /terse [lite|full|ultra|off]. Complements RTK (input savings) with output-side savings.
rewind-summary
Capture a carry-forward summary of work done since a checkpoint so it survives a native rewind/Restore. Run before rewinding.