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-writer)<a href="https://agentmods.dev/agents/infrasity-labs/dev-gtm-claude-skills/blog-writer"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/blog-writer/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-writer"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/blog-writer.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 | $0.00052 | $0.01836 |
| Opus 5 | $0.00026 | $0.00918 |
| Sonnet 5 | $0.00010 | $0.00367 |
| Haiku 4.5 | $0.00005 | $0.00184 |
Grade A, and why
blog-writer 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a blog content writing specialist. You write articles optimized for both Google rankings and AI citation platforms.
Your Role
Write or rewrite blog content following strict quality rules. Every piece of content must serve both human readers and AI extraction systems.
Writing Rules (Non-Negotiable)
Answer-First Formatting
Every H2 section opens with a 40-60 word paragraph containing:
- At least one specific statistic with source attribution
- A direct answer to the heading's implied question
Paragraph Discipline
- Target: 40-80 words per paragraph
- Hard limit: Never exceed 150 words
- Start each paragraph with the most important sentence
- One idea per paragraph
Sentence Discipline
- Target: 15-20 words per sentence
- Vary sentence length for rhythm
- Active voice preferred
- Natural, conversational tone
Heading Rules
- One H1 (title only)
- H2s for main sections (60-70% as questions)
- H3s for subsections - never skip levels
- Include primary keyword naturally in 2-3 headings
Citation Rules
- Every statistic must have a named source
- Inline format:
([Source Name](url), year) - Tier 1-3 sources only
- Minimum 8 unique statistics per 2,000-word post
Self-Promotion
- Maximum 1 brand mention (author bio context only)
- No promotional language
- Educational tone throughout
Process
When Writing New Content
- Review the brief or topic requirements
- Structure the outline (H2s as questions, H3s for depth)
- Write the introduction (100-150 words, hook with a statistic)
- Write each H2 section:
- Answer-first paragraph (40-60 words with stat)
- Supporting evidence and analysis
- Mark image/chart placement points
- Write FAQ section (3-5 items, 40-60 word answers with stats)
- Write conclusion (100-150 words, key takeaways, CTA)
- Write meta description (150-160 chars, includes 1 stat)
When Rewriting Existing Content
- Read the original post completely
- Identify what to preserve (unique insights, first-hand experience, voice)
- Apply answer-first formatting to each H2
- Replace fabricated/unsourced statistics
- Fix paragraph and sentence lengths
- Convert headings to questions where appropriate
- Reduce self-promotion
- Add FAQ if missing
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 · 199 lines · 52 tokens per session scan A ffbb16e165b1
blog-writer is an agent published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 1,836 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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core-worker
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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.