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 charlieviettq/awesome-agent-skill --skill algo-seo-contentgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/algo-seo-content)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-seo-content"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-seo-content/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/charlieviettq/awesome-agent-skill/algo-seo-content"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-seo-content.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 | $0.00075 | $0.01028 |
| Opus 5 | $0.00037 | $0.00514 |
| Sonnet 5 | $0.00015 | $0.00206 |
| Haiku 4.5 | $0.00007 | $0.00103 |
Grade A, and why
"algo-seo-content" 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 12d 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
94% identical to algo-seo-content — 8 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content SEO Strategy
Overview
Content SEO is the systematic process of creating and optimizing content to match search intent and rank organically. The pipeline: keyword research → intent mapping → content creation → on-page optimization → performance monitoring. Success depends on intent match, not keyword density.
When to Use
Trigger conditions:
- Planning new content to capture organic search traffic
- Optimizing existing underperforming content
- Conducting keyword research and content gap analysis
When NOT to use:
- When the issue is technical (page speed, crawlability) — use technical SEO
- When the issue is off-page (backlinks, authority) — use backlink analysis
Algorithm
IRON LAW: Content Must Match SEARCH INTENT
A perfectly optimized page targeting the wrong intent will NOT rank.
Four intent types:
1. Informational — wants to learn ("how to", "what is")
2. Navigational — wants a specific site ("github login")
3. Commercial — comparing options ("best CRM 2025")
4. Transactional — wants to buy/do ("buy iPhone 16 case")
Check SERP results to determine actual intent before writing.
Phase 1: Input Validation
Define target topic/niche. Gather seed keywords from brainstorming, competitor analysis, and tools (Ahrefs, SEMrush, Google Keyword Planner). Gate: Seed keyword list with search volume and difficulty estimates.
Phase 2: Core Algorithm
- Keyword clustering: Group related keywords by intent and topic
- SERP analysis: Check top 10 results for each cluster — identify intent, content format, and depth
- Content gap analysis: Find keywords competitors rank for that you don't
- Content brief: Define: target keyword, intent, format (guide/list/comparison), word count benchmark, required subtopics from SERP analysis
- On-page optimization: Title tag (keyword front-loaded), meta description, H1/H2 structure, internal links, image alt text
Phase 3: Verification
Check: title contains primary keyword, intent matches SERP, all key subtopics covered, internal links to related content. Gate: Content matches identified intent and covers SERP-derived subtopics.
What ships with it
3 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.
- 12d ago First seen · 90 lines · 75 tokens per session scan A 6e618414cf3d
"algo-seo-content" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 75 tokens to every session and 1,028 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to algo-seo-content, differing in 8 lines, and is treated as a copy.
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