Blog Analyzer
Audit blog quality, SEO, trust signals, and AI citation readiness with a consistent 100-point score.
The skill primarily reads local files or fetches URLs and produces reports; it does not request rewriting, deletion, or privileged operations. Repository security material also describes dependency locking, credential protection, and least-privilege agent tooling. Points were deducted because URL data flow, WebFetch availability, confirmation boundaries, and handling of prompt-injection content are not explicit in this skill, and provenance cannot be fully established from the skill file alone.
The skill defines a clear analysis sequence, output formats, and some error-oriented behavior; repository evidence includes tests for parsing, scoring, links, structure, and nonexistent-file handling. Points were deducted because tests were not executed, dependencies are not self-contained at this path, WebFetch and optional packages are assumed, and abnormal-input failure feedback is not specified in sufficient detail.
Trigger phrases are precise, and the skill supports local files, directories, URLs, Markdown, MDX, HTML, batch analysis, and multiple export formats. Points were deducted because non-fit ranges, negative trigger conditions, Chinese-language calibration, and Chinese output support are not declared. The URL path depends on external fetching that may be unreachable from mainland-China networks, although local-file use remains viable.
Frontmatter provides name, description, user-invokable status, argument hint, and an MIT license; the document includes progressive steps, optional modes, scoring tables, JSON examples, and report templates. Points were deducted because this skill does not itself state maintainer ownership, version, changelog, installation dependencies, or troubleshooting guidance, and several referenced documents were not supplied for this review.
The task, scoring categories, prioritized recommendations, Markdown/JSON/table exports, and batch sorting behavior are concretely described. Repository tests cover many analyzer functions and an error result for missing files. Points were deducted because static review cannot validate score correctness, URL retrieval, citation truthfulness, or production-ready reports; several metrics and AI-detection thresholds are empirical claims, so human review remains necessary.
The repository supplies CI, tests, and integration regression tests, providing limited auditable evidence. Points were deducted because the evidence is primarily project-authored, with no independent third-party corroboration; code was not executed, and the referenced materials and actual analyzer implementation were not fully provided, so verification coverage is limited.
- Do not treat AI-probability, SEO scores, or citation conclusions as facts without reviewing the underlying content and sources.
- URL analysis requires external fetching; use local files or user-supplied content when mainland-China network access is unavailable.
- Before use, verify that referenced documents, optional dependencies, and cognitive_load.py exist, and review the indirect prompt-injection risk from fetched content.
What does this skill do, and when should you use it?
Blog Analyzer is the auditing skill within the claude-blog suite, focused on local files, directories, and published URLs. It scores posts across content quality, SEO optimization, E-E-A-T signals, technical elements, and AI citation readiness. It also examines sentence rhythm, known AI phrases, and vocabulary diversity, then produces prioritized fixes. It fits authors, marketing teams, and agencies that need repeatable editorial checks instead of one-off feedback.
Reads Markdown, MDX, and HTML files, or fetches URL content with WebFetch; extracts frontmatter, heading hierarchy, paragraph and word counts, statistics, images and alt text, charts, links, FAQs, schema markup, meta tags, sentence lengths, and vocabulary data; scores five categories and assigns a rating; calculates sentence-length variance, flags specified AI phrases, and computes type-token ratio; exports Markdown, JSON, or table reports; supports directory scanning, batch analysis, score sorting, and optional editorial-rubric and cognitive-load modes.
- A solo blogger wants a pre-publication audit of quality, SEO gaps, and AI citation readiness.
- A content team needs to scan a blog directory in batch and prioritize the lowest-scoring posts.
- An SEO editor wants checks for headings, keywords, internal links, external links, meta descriptions, and structured data.
- A marketing team needs to identify weak sourcing, missing author information, or limited experience signals.
- An editor wants to detect flat sentence rhythm, repeated AI-associated phrases, or low vocabulary diversity.
What are this skill's strengths and limitations?
- Uses a clearly defined five-category scoring model covering content, SEO, E-E-A-T, technical elements, and AI citation readiness.
- Produces concrete fixes grouped as Critical, High, Medium, and Low priority.
- Supports local files, URLs, directory batches, and Markdown, JSON, and table output.
- Includes burstiness, AI-phrase, and TTR checks to surface machine-like writing risks.
- Optional rubric and cognitive-load modes add further editorial diagnostics.
- Full scoring depends on repository reference documents, so every scoring detail cannot be reproduced from this SKILL.md alone.
- URL analysis requires WebFetch and network access; local and batch analysis require filesystem access.
- The source does not provide a standalone installation path for this sub-skill.
- AI-content detection is heuristic and cannot prove that text was generated by AI.
- The source provides no independent test coverage or cross-platform validation specifically for this sub-skill.
How do you install this skill?
This skill is published as part of the full Claude Code suite in the public AgriciDaniel/claude-blog repository. The source provides this public installation flow: run /plugin marketplace add AgriciDaniel/claude-blog in Claude Code, then run /plugin install claude-blog@agricidaniel-claude-blog; restart Claude Code afterward. The source does not document a standalone installation process for skills/blog-analyze/SKILL.md.
How do you use this skill?
In Claude Code, run /blog analyze <file> to audit a file. You can also provide a directory for batch analysis or a published URL. Supported options include --format json, --format table, --batch, --sort score, --rubric, and --cognitive-load. The skill description also lists natural-language triggers such as “analyze blog” and “blog health check.”
How does this skill compare with similar options?
Compared with asking Claude or ChatGPT for a one-off blog critique, this skill provides a fixed five-category score, batch processing, export formats, and prioritized remediation. Compared with manual review, it is better suited to repeatable editorial workflows, but it must be installed through the broader claude-blog suite and relies on its reference documents and runtime environment.