blog-analyze

Name: blog-analyze
Author: AgriciDaniel

1.4kinstalls251forks

How to install blog-analyze?

agricidaniel/claude-blog/blog-analyze

$npx -y skills add AgriciDaniel/claude-blog --skill blog-analyze

Installs into the current project.

Summary

Audit and score blog posts on a 5-category 100-point scoring system covering content quality, SEO optimization, E-E-A-T signals, technical elements, and AI citation readiness. Includes AI content detection (burstiness, phrase flagging, vocabulary diversity). Supports export forma

Files · 1

View on GitHub

SKILL.md

1	# Blog Analyzer: Quality Audit & Scoring
2
3	Scores blog posts on a 0-100 scale across 5 categories and provides prioritized
4	improvement recommendations. Includes AI content detection analysis. Works with
5	local files or published URLs.
6
7	Reference documents (paths from repo root):
8	- `skills/blog/references/quality-scoring.md`: full scoring checklist
9	- `skills/blog/references/eeat-signals.md`: E-E-A-T evaluation criteria
10	- `skills/blog/references/ai-slop-detection.md`: two-tier reflex methodology (v1.8.0)
11	- `skills/blog/references/editorial-heuristics.md`: ordinal 0-4 rubric, P0-P3 severity (v1.8.0, used with `--rubric`)
12	- `skills/blog/references/cognitive-load.md`: per-section concept density (v1.8.0, used with `--cognitive-load`)
13
14	## Input Handling
15
16	- Local file: Read the file directly
17	- URL: Fetch with WebFetch, extract content
18	- Directory: Scan for blog files, audit all (batch mode)
19	- Flags: `--format json\|table`, `--batch`, `--sort score`, `--rubric`, `--cognitive-load`
20
21	### Optional Modes (v1.8.0)
22
23	- `--rubric`: in addition to the 100-point score, emit the ordinal 0-4 editorial-heuristics rubric with P0-P3 severity tags. See `skills/blog/references/editorial-heuristics.md`. The 100-point JSON schema is preserved; the rubric is added as a sibling `rubric` field.
24	- `--cognitive-load`: run `scripts/cognitive_load.py` against the post and embed the per-section load heatmap as a sibling `cognitive_load` field. See `skills/blog/references/cognitive-load.md`.
25
26	Both modes are additive. The default behavior (no flags) is unchanged from v1.7.1.
27
28	## Scoring Process
29
30	### Step 1: Content Extraction
31
32	Read the blog post and extract:
33	- Frontmatter (title, description, date, lastUpdated, author, tags)
34	- Heading structure (H1, H2, H3 with hierarchy)
35	- Paragraph count and word counts per paragraph
36	- Statistics (any number claims with or without sources)
37	- Images (count, alt text presence, format)
38	- Charts/SVGs (count, type diversity)
39	- Links (internal, external, broken)
40	- FAQ section presence
41	- Schema markup (types present)
42	- Meta tags (title, description, OG tags, twitter cards)
43	- Sentence lengths for burstiness analysis
44	- Vocabulary tokens for diversity scoring
45
46	### Step 2: Score Each Category
47
48	Load `references/quality-scoring.md` for the full checklist. Score each:
49
50	#### Content Quality (30 points)
51	\| Check \| Points \| Pass Criteria \|
52	\|-------\|--------\|---------------\|
53	\| Depth/comprehensiveness \| 7 \| Covers topic thoroughly, no major gaps \|
54	\| Readability (Flesch 60-70) \| 7 \| Flesch 60-70 ideal, 55-75 acceptable; Grade 7-8; Gunning Fog 7-8 \|
55	\| Originality/unique value markers \| 5 \| Original data, case studies, first-hand experience \|
56	\| Sentence & paragraph structure \| 4 \| Avg sentence 15-20 words, ≤25% over 20; paragraphs 40-80 words; H2 every 200-300 words \|
57	\| Engagement elements \| 4 \| Summary box, callouts, varied content blocks. Accepts: "TL;DR", "Key Takeaways", "The Bottom Line", "What You'll Learn", "At a Glance", "In Brief" \|
58	\| Grammar/anti-pattern \| 3 \| Passive voice ≤10%, AI trigger words ≤5/1K, transition words 20-30%, clean prose \|
59
60	Readability Bands (apply per persona, or use default):
61
62	\| Audience \| Flesch Grade \| Flesch Ease \| Scoring Impact \|
63	\|----------\|-------------\|-------------\|----------------\|
64	\| Consumer \| 6-8 \| 60-80 \| Full points if in range \|
65	\| Professional \| 8-10 \| 50-60 \| Full points if in range \|
66	\| Technical \| 10-12 \| 30-50 \| Full points if in range \|
67	\| Default (no persona) \| 7-8 \| 60-70 \| Current scoring unchanged \|
68
69	Content clarity is the #2 factor for AI citation probability (+32.83%
70	score differential). Average US adult reads at 7th-8th grade level.
71
72	#### SEO Optimization (25 points)
73	\| Check \| Points \| Pass Criteria \|
74	\|-------\|--------\|---------------\|
75	\| Heading hierarchy with keywords \| 5 \| H1 -> H2 -> H3, no skips, keyword in 2-3 headings \|
76	\| Title tag (40-60 chars, keyword, power word) \| 4 \| Front-loaded keyword, positive sentiment \|
77	\| Keyword placement/density \| 4 \| Natural integration, no stuffing, in first 100 words \|
78	\| Internal linking (3-10 contextual) \| 4 \| Descriptive anchor text, bidirectional \|
79	\| URL structure \| 3 \| Short, keyword-rich, no stop words, lowercase \|
80	\| Meta description (150-160 chars, stat) \| 3 \| Fact-dense, includes one stati

People who liked this also viewed

6 picks

Type

blog-analyze

1.4kinstalls251forks

View on GitHub

How to install blog-analyze?

agricidaniel/claude-blog/blog-analyze

$npx -y skills add AgriciDaniel/claude-blog --skill blog-analyze

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Blog Analyzer: Quality Audit & Scoring
2
3	Scores blog posts on a 0-100 scale across 5 categories and provides prioritized
4	improvement recommendations. Includes AI content detection analysis. Works with
5	local files or published URLs.
6
7	Reference documents (paths from repo root):
8	- `skills/blog/references/quality-scoring.md`: full scoring checklist
9	- `skills/blog/references/eeat-signals.md`: E-E-A-T evaluation criteria
10	- `skills/blog/references/ai-slop-detection.md`: two-tier reflex methodology (v1.8.0)
11	- `skills/blog/references/editorial-heuristics.md`: ordinal 0-4 rubric, P0-P3 severity (v1.8.0, used with `--rubric`)
12	- `skills/blog/references/cognitive-load.md`: per-section concept density (v1.8.0, used with `--cognitive-load`)
13
14	## Input Handling
15
16	- Local file: Read the file directly
17	- URL: Fetch with WebFetch, extract content
18	- Directory: Scan for blog files, audit all (batch mode)
19	- Flags: `--format json\|table`, `--batch`, `--sort score`, `--rubric`, `--cognitive-load`
20
21	### Optional Modes (v1.8.0)
22
23	- `--rubric`: in addition to the 100-point score, emit the ordinal 0-4 editorial-heuristics rubric with P0-P3 severity tags. See `skills/blog/references/editorial-heuristics.md`. The 100-point JSON schema is preserved; the rubric is added as a sibling `rubric` field.
24	- `--cognitive-load`: run `scripts/cognitive_load.py` against the post and embed the per-section load heatmap as a sibling `cognitive_load` field. See `skills/blog/references/cognitive-load.md`.
25
26	Both modes are additive. The default behavior (no flags) is unchanged from v1.7.1.
27
28	## Scoring Process
29
30	### Step 1: Content Extraction
31
32	Read the blog post and extract:
33	- Frontmatter (title, description, date, lastUpdated, author, tags)
34	- Heading structure (H1, H2, H3 with hierarchy)
35	- Paragraph count and word counts per paragraph
36	- Statistics (any number claims with or without sources)
37	- Images (count, alt text presence, format)
38	- Charts/SVGs (count, type diversity)
39	- Links (internal, external, broken)
40	- FAQ section presence
41	- Schema markup (types present)
42	- Meta tags (title, description, OG tags, twitter cards)
43	- Sentence lengths for burstiness analysis
44	- Vocabulary tokens for diversity scoring
45
46	### Step 2: Score Each Category
47
48	Load `references/quality-scoring.md` for the full checklist. Score each:
49
50	#### Content Quality (30 points)
51	\| Check \| Points \| Pass Criteria \|
52	\|-------\|--------\|---------------\|
53	\| Depth/comprehensiveness \| 7 \| Covers topic thoroughly, no major gaps \|
54	\| Readability (Flesch 60-70) \| 7 \| Flesch 60-70 ideal, 55-75 acceptable; Grade 7-8; Gunning Fog 7-8 \|
55	\| Originality/unique value markers \| 5 \| Original data, case studies, first-hand experience \|
56	\| Sentence & paragraph structure \| 4 \| Avg sentence 15-20 words, ≤25% over 20; paragraphs 40-80 words; H2 every 200-300 words \|
57	\| Engagement elements \| 4 \| Summary box, callouts, varied content blocks. Accepts: "TL;DR", "Key Takeaways", "The Bottom Line", "What You'll Learn", "At a Glance", "In Brief" \|
58	\| Grammar/anti-pattern \| 3 \| Passive voice ≤10%, AI trigger words ≤5/1K, transition words 20-30%, clean prose \|
59
60	Readability Bands (apply per persona, or use default):
61
62	\| Audience \| Flesch Grade \| Flesch Ease \| Scoring Impact \|
63	\|----------\|-------------\|-------------\|----------------\|
64	\| Consumer \| 6-8 \| 60-80 \| Full points if in range \|
65	\| Professional \| 8-10 \| 50-60 \| Full points if in range \|
66	\| Technical \| 10-12 \| 30-50 \| Full points if in range \|
67	\| Default (no persona) \| 7-8 \| 60-70 \| Current scoring unchanged \|
68
69	Content clarity is the #2 factor for AI citation probability (+32.83%
70	score differential). Average US adult reads at 7th-8th grade level.
71
72	#### SEO Optimization (25 points)
73	\| Check \| Points \| Pass Criteria \|
74	\|-------\|--------\|---------------\|
75	\| Heading hierarchy with keywords \| 5 \| H1 -> H2 -> H3, no skips, keyword in 2-3 headings \|
76	\| Title tag (40-60 chars, keyword, power word) \| 4 \| Front-loaded keyword, positive sentiment \|
77	\| Keyword placement/density \| 4 \| Natural integration, no stuffing, in first 100 words \|
78	\| Internal linking (3-10 contextual) \| 4 \| Descriptive anchor text, bidirectional \|
79	\| URL structure \| 3 \| Short, keyword-rich, no stop words, lowercase \|
80	\| Meta description (150-160 chars, stat) \| 3 \| Fact-dense, includes one stati

People who liked this also viewed

6 picks

Type