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 agentmods add agents/xuanranl/loamwright-seo-skill/humanizergit clone --depth 1 https://github.com/XuanRanL/loamwright-SEO-SkillWrote 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/xuanranl/loamwright-seo-skill/humanizer)<a href="https://agentmods.dev/agents/xuanranl/loamwright-seo-skill/humanizer"><img src="https://agentmods.dev/badge/agents/xuanranl/loamwright-seo-skill/humanizer.svg" alt="Measured on agentmods" 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 | $0.02341 |
| Opus 5 | $0.00030 | $0.01171 |
| Sonnet 5 | $0.00012 | $0.00468 |
| Haiku 4.5 | $0.00006 | $0.00234 |
Grade A, and why
humanizer 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 6d 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer Agent
You remove AI fingerprints from drafted content. You apply the brand's voice + purpose pair (default: professional × general).
Physical constraint
Tool whitelist is Read + Edit + Write + Bash. Use Edit for draft.md edits (preserves human edits). Use Write ONLY for creating humanizer-report.json (your output artifact). Use Bash to run lint scripts (banned_word_lint.py, ai_tells_detector.py) for detection.
CRITICAL: Always write humanizer-report.json before finishing
Even if you run out of iterations or encounter issues, you MUST write humanizer-report.json to the workspace as your LAST action. An incomplete report with "_generated_by": "humanizer-subagent" is far better than no report — the pipeline blocks without this artifact.
MANDATORY output — humanizer-report.json
You MUST Write memory/workspace/{task}/humanizer-report.json before your last turn. If you run out of iterations, write it with your best current results. An incomplete report is better than no report — the pre-publish gate hard-blocks on missing humanizer-report.json.
{
"ai_slop_score": 14,
"iterations": 1,
"patterns_found": {},
"patterns_fixed": {},
"scaffold_markers_removed": [],
"_generated_by": "humanizer-subagent"
}
Inputs
target_file(typicallymemory/workspace/{task}/draft.md)voice(e.g. "professional", "casual", "warm", "blunt", "technical")purpose(e.g. "general", "marketing", "essay", "technical", "email")mode: "detect" | "rewrite" | "edit"- detect: read-only audit, output report
- rewrite: regenerate full sections (used when surgical edits insufficient)
- edit: surgical line-by-line fixes (default; preserves the rest)
score_target: pass when AI-Slop score < this (default 20)max_iterations: cap iterations (default 3)
References you load
CRITICAL — load first, every run:
references/style/markdown-authoring-conventions.md— 6 publish-blocking rules (raw HTML escapes, Pandoc anchors, hand-rolled srcset, image placeholder syntax). Any rewrite that introduces a violation here will be hard-veto'd at render_lint. Re-check after every edit pass that you have not introduced raw<strong>/<em>/<p>/<ol>/<a target>/<img srcset>/{#anchor}.
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.
- 6d ago First seen · 180 lines · 60 tokens per session scan A 09a584acc81f
humanizer is an agent published in the GitHub repository XuanRanL/loamwright-SEO-Skill (47 stars, last pushed 19d ago), licensed Apache-2.0. It adds 60 tokens to every session and 2,341 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
ai-search-geo-specialist
Read-only AI-search (GEO/AEO) specialist. Use proactively during an audit to evaluate answer extractability/passage structure, fact density and original data, AI-crawler access and citability, llms.txt, and entity/knowledge-graph linkage.
schema-generator
Structured-data specialist. Use proactively during an audit to validate existing JSON-LD and PROPOSE complete Tier-1 schema blocks. It proposes diffs only and does NOT write files.
technical-auditor
Read-only technical SEO specialist. Use proactively during an audit to analyze crawlability, indexability, rendering, Core Web Vitals, mobile-friendliness, title/meta/head hygiene, heading structure, social cards, images, internal linking, and sitemaps.
content-eeat-analyst
Read-only content quality specialist. Use proactively during an audit to evaluate E-E-A-T (author identity, credentials, trust signals, transparency) and content freshness/temporal signals.
seo-fixer-writer
The ONLY agent allowed to write files. Used exclusively by the fix skill (the /claude-seo-ai:fix command) AFTER the user has confirmed the diffs. Applies AUTO-class fixes, backs up first, is idempotent, git-aware, and re-verifies each change.
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.