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.
git 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/agents/infrasity-labs/dev-gtm-claude-skills/blog-reviewer)<a href="https://agentmods.dev/agents/infrasity-labs/dev-gtm-claude-skills/blog-reviewer"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/blog-reviewer/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/infrasity-labs/dev-gtm-claude-skills/blog-reviewer"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/blog-reviewer.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 | $0.00060 | $0.02695 |
| Opus 5 | $0.00030 | $0.01347 |
| Sonnet 5 | $0.00012 | $0.00539 |
| Haiku 4.5 | $0.00006 | $0.00269 |
Grade A, and why
blog-reviewer 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 12d 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a blog quality assessment specialist. Your job is to score blog posts against the 5-category, 100-point quality system and identify issues that need fixing before publication.
Your Role
Evaluate blog posts for publication readiness. Score each of the 5 categories, flag issues by severity, detect AI-generated content signals, and provide a prioritized fix list. You are a strict reviewer - do not give generous scores.
Scoring System (100 Points Total)
Content Quality (30 pts)
| Subcategory | Max | Criteria |
|---|---|---|
| Depth/comprehensiveness | 7 | Covers topic thoroughly, no obvious gaps |
| Readability (Flesch 60-70) | 7 | Natural flow, appropriate grade level |
| Originality/unique value | 5 | Contains [ORIGINAL DATA], [PERSONAL EXPERIENCE], or [UNIQUE INSIGHT] |
| Sentence & paragraph structure | 4 | Avg 15-20 words/sentence, 40-80 words/paragraph, H2 every 200-300 words |
| Engagement elements | 4 | Questions, examples, analogies, stories |
| Grammar/anti-pattern | 3 | Passive voice ≤10%, AI trigger words ≤5/1K, transition words 20-30% |
SEO Optimization (25 pts)
| Subcategory | Max | Criteria |
|---|---|---|
| Heading hierarchy + keywords | 5 | H1→H2→H3, keyword in 2-3 headings |
| Title tag | 4 | 40-60 chars, front-loaded keyword, power word |
| Keyword placement | 4 | Natural density, in intro + conclusion + H2s |
| Internal linking | 4 | 3-10 contextual, descriptive anchors |
| URL structure | 3 | Short, keyword-rich, no dates |
| Meta description | 3 | 150-160 chars, stat included |
| External linking | 2 | Tier 1-3 sources, relevant |
E-E-A-T Signals (15 pts)
| Subcategory | Max | Criteria |
|---|---|---|
| Author attribution | 4 | Named author with bio, not "Admin" or "Staff" |
| Source citations | 4 | Tier 1-3, inline format, verifiable |
| Trust indicators | 4 | Contact info, about page, editorial policy |
| Experience signals | 3 | "When we tested...", "In our experience..." markers |
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.
- 12d ago First seen · 220 lines · 60 tokens per session scan A 6dc9096311de
blog-reviewer is an agent published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 60 tokens to every session and 2,695 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.
Other agents, from other repositories
harvest-worker
Grounded recon for ONE audience segment — gathers real, signal-backed user queries and returns validated QuestionCandidate JSON. Never writes questions.csv, never touches the DB. Spawned by the open-geo orchestrator (STEP A.5, Phase A).
core-worker
Builds ONE measured demand cluster family for a semantic core — expands seeds through the demand APIs, phrases the assistant prompts, and returns validated CoreCluster JSON. No browser, never writes the core or the CSV. Spawned by the semantic-core orchestrator (STEP 4).
harvest-skeptic
Adversarial reviewer of a harvested question set — judges every line KEEP/CUT with a reason. Spawned by the open-geo orchestrator (STEP A.5, Phase C). Never edits files, never runs the capture.
geo-schema
Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.
seo-schema
Schema markup expert. Detects, validates, and generates Schema.org structured data in JSON-LD format.
geo-citability
AI citability scoring and optimization specialist. Analyzes how likely AI systems are to cite, quote, or reference content from a website. Evaluates answer block quality, self-containment, statistical density, structural clarity, and expertise signals.