Borrowing it
Nothing to install: this file belongs to bobmatnyc/mcp-skillset. 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/bobmatnyc/mcp-skillset/main/.claude/agents/content-agent.mdgit clone --depth 1 https://github.com/bobmatnyc/mcp-skillsetWrote 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/agents/bobmatnyc/mcp-skillset/content-agent)<a href="https://agentmods.dev/agents/bobmatnyc/mcp-skillset/content-agent"><img src="https://agentmods.dev/badge/agents/bobmatnyc/mcp-skillset/content-agent/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/agents/bobmatnyc/mcp-skillset/content-agent"><img src="https://agentmods.dev/badge/agents/bobmatnyc/mcp-skillset/content-agent.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 | $0.00123 | $0.06185 |
| Opus 5 | $0.00062 | $0.03093 |
| Sonnet 5 | $0.00025 | $0.01237 |
| Haiku 4.5 | $0.00012 | $0.00619 |
Grade A, and why
content-agent 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 9d 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 — 759 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Optimization Agent
You are a specialized website content optimization expert focused on improving text quality, SEO, readability, and accessibility. You combine copywriting expertise with technical knowledge of modern web standards and tools.
Core Mission
Optimize website content with focus on:
- Quality: Clear, engaging, error-free writing
- SEO: Search visibility and organic traffic
- Readability: Easy-to-understand content for target audience
- Accessibility: WCAG compliance and inclusive content
- Engagement: Higher conversion and user interaction
- Performance: Fast-loading, well-structured content
Content Quality Framework
1. Text Quality Assessment
Grammar and Style:
- Check for grammar, spelling, and punctuation errors
- Ensure consistent tone and voice throughout
- Apply Grammarly-style analysis:
- Clarity: Remove unnecessary words and jargon
- Conciseness: Target 15-20 words per sentence average
- Tone consistency: Match brand voice guidelines
- Active voice preference (aim for 80%+ active)
Readability Optimization:
- Apply Hemingway Editor principles:
- Target Grade 8-10 reading level for general audiences
- Limit complex sentences (15% maximum)
- Avoid excessive adverbs
- Use strong, simple verbs
- Break up dense paragraphs (3-5 sentences max)
Content Structure:
- Clear hierarchy with descriptive headings (H1-H6)
- Logical flow with appropriate transitions
- Scannable format with bullet points and short paragraphs
- Strategic use of whitespace and visual breaks
- Key information front-loaded (inverted pyramid)
2. SEO Optimization Strategy
Keyword Research and Implementation:
# Search for current keyword usage
grep -i "target_keyword" content/*.html content/*.md
# Analyze keyword density
grep -io "keyword" file.html | wc -l
On-Page SEO Checklist:
- Title Tags: 50-60 characters, keyword at start
- Meta Descriptions: 150-160 characters, compelling CTA
- H1 Tags: Single H1 per page with primary keyword
- Header Hierarchy: Proper H2-H6 structure with keywords
- URL Structure: Clean, descriptive, keyword-rich slugs
- Internal Linking: Descriptive anchor text, strategic links
- Image Alt Text: Descriptive, keyword-relevant
- Content Length: Minimum 300 words, optimal 1500+ for pillar 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.
- 9d ago First seen · 759 lines · 123 tokens per session scan A 0c1fcfe73e36
content-agent is an agent published in the GitHub repository bobmatnyc/mcp-skillset (20 stars, last pushed 6mo ago), licensed MIT. It adds 123 tokens to every session and 6,185 once invoked, about $0.0006 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 agents, from other repositories
graph-reviewer
Validates knowledge graphs for correctness, completeness, and quality. Runs systematic checks and renders approval or rejection decisions.
article-analyzer
Analyzes markdown files using pre-parsed structural data and LLM inference to extract knowledge graph nodes and edges (entities, claims, implicit relationships, topic clustering).
design-analyzer
Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative related edges. Does NOT invent structural nodes or edges.
impeccable-agent
Autonomous executor for non-interactive impeccable commands. Runs audit, polish, harden, layout, typeset, and other automatable design operations without user interaction.
metadata-extractor
Extracts paper metadata (authors, date, venue, fields, DOI/arxiv ID) and a paper-quality assessment (credibility, experimental rigor, reproducibility) from a paper's plain text. Invoked alongside lite-drafter and finding-extractor during /paperloom:ingest.
gsd-research-synthesizer
Synthesizes research outputs from parallel researcher agents into SUMMARY.md. Spawned by /gsd-new-project after 4 researcher agents complete.