claude-blog is a Claude Code skill suite for planning, writing, optimizing, auditing, localizing, and refreshing blog content. It is for content and SEO workflows that produce articles and related publishing artifacts while checking drafts against defined delivery criteria. The catalogue entries provide the skills, agents, plugins, and instruction used by this workflow.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AgriciDaniel/claude-blognpx agentmods add skills/agricidaniel/claude-blog/blog-styleWrote 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/agricidaniel/claude-blog/blog-style)<a href="https://agentmods.dev/skills/agricidaniel/claude-blog/blog-style"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-blog/blog-style/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/agricidaniel/claude-blog/blog-style"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-blog/blog-style.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 | $0.00058 | $0.00672 |
| Opus 5 | $0.00029 | $0.00336 |
| Sonnet 5 | $0.00012 | $0.00134 |
| Haiku 4.5 | $0.00006 | $0.00067 |
Grade A, and why
blog-style 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blog Style - Writing Style Learning
Learn an author voice profile from existing posts, then use it as a baseline for VOICE.md, blog-persona, and blog-write. The profile captures measurable style signals so future drafts can preserve the author's cadence, vocabulary, and tone.
Commands
| Command | Purpose |
|---|---|
/blog style learn <paths> |
Analyze sample posts and generate a voice profile |
Learn Workflow
Use 5 to 10 representative posts from the same author, brand, or editorial voice. Accept individual markdown files, MDX files, text files, or a directory containing posts.
Run the local learner:
python3 scripts/style_learn.py <paths> --format markdown
For machine-readable output:
python3 scripts/style_learn.py <paths> --format json --output voice-profile.json
For a VOICE.md-ready block:
python3 scripts/style_learn.py <paths> --format markdown --output VOICE.md
If fewer than the requested minimum sample count is supplied, warn and continue. The default minimum is 5 posts.
Profile Fields
The learner aggregates the existing blog analyzer across each sample post:
- Sentence length mean and median
- Sentence length burstiness as corpus variance
- Vocabulary richness as type-token ratio
- Transition-word sentence rate
- Passive-voice sentence rate
- AI trigger words per 1,000 words as a baseline to preserve or avoid
- Paragraph-length distribution
- First-person usage rate
- Heading-as-question ratio
- Signature phrases from top 2-gram and 3-gram content phrases with stopwords removed
- Tone descriptors derived from the measured metrics
Consuming the Profile
Drop the markdown block into project VOICE.md when the goal is durable
project context. Blog-write can use the style baselines as drafting targets:
- Keep average sentence length near the learned mean.
- Match the learned sentence variation unless the user asks for a tighter or looser cadence.
- Preserve signature phrases only when they fit the topic naturally.
- Treat the AI trigger baseline as a ceiling when the author rarely uses those terms.
- Use the first-person and heading-question rates to decide how personal and question-led the draft should feel.
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 · 89 lines · 58 tokens per session scan A b8d4277cecb7
blog-style is a skill published in the GitHub repository AgriciDaniel/claude-blog (2,116 stars, last pushed 7d ago), licensed MIT. It adds 58 tokens to every session and 672 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 2026-08-30.
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