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 Infrasity-Labs/dev-gtm-claude-skills --skill blog-analyzegit clone --depth 1 https://github.com/Infrasity-Labs/dev-gtm-claude-skillsWrote 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/infrasity-labs/dev-gtm-claude-skills/blog-analyze)<a href="https://agentmods.dev/skills/infrasity-labs/dev-gtm-claude-skills/blog-analyze"><img src="https://agentmods.dev/badge/skills/infrasity-labs/dev-gtm-claude-skills/blog-analyze/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/infrasity-labs/dev-gtm-claude-skills/blog-analyze"><img src="https://agentmods.dev/badge/skills/infrasity-labs/dev-gtm-claude-skills/blog-analyze.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 | $0.00137 | $0.03680 |
| Opus 5 | $0.00068 | $0.01840 |
| Sonnet 5 | $0.00027 | $0.00736 |
| Haiku 4.5 | $0.00014 | $0.00368 |
Grade A, and why
blog-analyze 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 13d 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
91% identical to blog-analyze — 19 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 — 331 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blog Analyzer: Quality Audit & Scoring
Scores blog posts on a 0-100 scale across 5 categories and provides prioritized improvement recommendations. Includes AI content detection analysis. Works with local files or published URLs.
Reference documents (paths from repo root):
skills/blog/references/quality-scoring.md: full scoring checklistskills/blog/references/eeat-signals.md: E-E-A-T evaluation criteriaskills/blog/references/ai-slop-detection.md: two-tier reflex methodology (v1.8.0)skills/blog/references/editorial-heuristics.md: ordinal 0-4 rubric, P0-P3 severity (v1.8.0, used with--rubric)skills/blog/references/cognitive-load.md: per-section concept density (v1.8.0, used with--cognitive-load)
Input Handling
- Local file: Read the file directly
- URL: Fetch with WebFetch, extract content
- Directory: Scan for blog files, audit all (batch mode)
- Flags:
--format json|table,--batch,--sort score,--rubric,--cognitive-load
Optional Modes (v1.8.0)
--rubric: in addition to the 100-point score, emit the ordinal 0-4 editorial-heuristics rubric with P0-P3 severity tags. Seeskills/blog/references/editorial-heuristics.md. The 100-point JSON schema is preserved; the rubric is added as a siblingrubricfield.--cognitive-load: runscripts/cognitive_load.pyagainst the post and embed the per-section load heatmap as a siblingcognitive_loadfield. Seeskills/blog/references/cognitive-load.md.
Both modes are additive. The default behavior (no flags) is unchanged.
Scoring Process
Step 1: Content Extraction
Read the blog post and extract:
- Frontmatter (title, description, date, lastUpdated, author, tags)
- Heading structure (H1, H2, H3 with hierarchy)
- Paragraph count and word counts per paragraph
- Statistics (any number claims with or without sources)
- Images (count, alt text presence, format)
- Charts/SVGs (count, type diversity)
- Links (internal, external, broken)
- FAQ section presence
- Schema markup (types present)
- Meta tags (title, description, OG tags, twitter cards)
- Sentence lengths for burstiness analysis
- Vocabulary tokens for diversity scoring
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.
- 13d ago First seen · 331 lines · 137 tokens per session scan A 7f5d97873ff0
blog-analyze is a skill published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 137 tokens to every session and 3,680 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to blog-analyze, differing in 19 lines, and is treated as a copy.
Other skills, from other repositories
geo-audit
Full website GEO+SEO audit with parallel subagent delegation. Orchestrates a comprehensive Generative Engine Optimization audit across AI citability, platform analysis, technical infrastructure, content quality, and schema markup. Produces a composite GEO Score (0-100) with prioritized action plan.
geo
GEO-first SEO analysis tool. Optimizes websites for AI-powered search engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) while maintaining traditional SEO foundations. Performs full GEO audits, citability scoring, AI crawler analysis, llms.txt generation, brand mention scanning, platform-specific…
geo-brand-mentions
Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
geo-llmstxt
Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.
agent-readiness-scan
Use when a client audit, GEO/AI-visibility snapshot, or remediation re-scan needs the Cloudflare agent-readiness score from isitagentready.com — e.g. Theo client audits, "is the site agent-ready", markdown negotiation / MCP / llms.txt / Content-Signal checks, or tracking score deltas after Tier 0/1 fixes.
found-by-ai
Measure whether AI engines actually recommend a business when buyers ask. Runs the free live scan at areyoufoundbyai.com (no auth, 60s), reads the verdict and the rivals AI names instead, hands back the fix plan, and wires monitored sites into a fix-and-re-measure loop over MCP.