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 guia-matthieu/clawfu-skills --skill voice-injection-rewritergit clone --depth 1 https://github.com/guia-matthieu/clawfu-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/guia-matthieu/clawfu-skills/voice-injection-rewriter)<a href="https://agentmods.dev/skills/guia-matthieu/clawfu-skills/voice-injection-rewriter"><img src="https://agentmods.dev/badge/skills/guia-matthieu/clawfu-skills/voice-injection-rewriter/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/guia-matthieu/clawfu-skills/voice-injection-rewriter"><img src="https://agentmods.dev/badge/skills/guia-matthieu/clawfu-skills/voice-injection-rewriter.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 | $0.00058 | $0.03559 |
| Opus 5 | $0.00029 | $0.01780 |
| Sonnet 5 | $0.00012 | $0.00712 |
| Haiku 4.5 | $0.00006 | $0.00356 |
Grade A, and why
voice-injection-rewriter 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 9d 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 — 377 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Voice Injection Rewriter
Transform AI-generated text into authentic, voice-consistent content — not by faking humanity, but by applying real voice patterns from a specific person or brand.
When to Use This Skill
Use this skill when you need to:
- Rewrite AI drafts to match a specific person's voice
- Strip AI fingerprints from generated content before publishing
- Enforce voice consistency across AI-assisted content production
- Bridge the gap between AI efficiency and authentic brand expression
- Post-process any AI output (blog posts, emails, social, landing pages)
This skill is NOT a generic "humanizer." It requires voice input — either from brand-voice-learner output, ClawFu brand memory, or voice samples you provide.
Why this matters: Generic humanizers add fake imperfections (random typos, forced contractions) to trick detectors. That's an arms race you lose. This skill applies YOUR voice patterns — which produces naturally human text because it IS the voice of a real human.
Methodology Foundation
Core Principle: AI text sounds artificial not because it lacks typos, but because it lacks a specific person's vocabulary, rhythm, opinions, and structural habits. The fix is voice injection, not cosmetic imperfection.
Sources:
- NN/g Voice and Tone research
- Brand voice analysis methodology (ClawFu
brand-voice-learner) - AI detection pattern research (GPTZero, Originality.ai signal analysis)
- WhatsApp IA NDD Camp community insights on AI content workflows
The 201 insight: Most people try to make AI text "sound human" (generic). The actual skill is making AI text "sound like ME" (specific). The first is commodity. The second is craft.
What Claude Does vs What You Decide
| Claude Does | You Decide |
|---|---|
| Applies voice patterns to rewrite text | Which voice profile to use |
| Strips AI detection signals | How far to deviate from the original |
| Identifies voice mismatches | Final approval of tone and accuracy |
| Suggests voice-consistent alternatives | Where factual precision overrides voice |
| Runs enforcement checklists | Publication context and audience |
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.
- 9d ago First seen · 377 lines · 58 tokens per session scan A 252ef35f978b
voice-injection-rewriter is a skill published in the GitHub repository guia-matthieu/clawfu-skills (149 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 3,559 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 2026-09-03.
Other skills, from other repositories
ads-creative-development
How to produce ad creative that converts at performance scale. Hook patterns, format selection, video pacing, variation systems, sequential testing methodology, fatigue detection, brand-voice alignment without conversion dilution, and platform-specific creative norms. Triggers on ad creative, ad design, hook patterns…
ads-performance-analytics
How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media…
paid-media-strategy
A discipline for running paid media that does not light money on fire. Hypothesis writing for paid spend, channel selection, budget allocation, audience targeting, bid strategy, campaign types, what NOT to spend on, attribution reality, and the failure modes that produce expensive lessons. Triggers on paid media…
community-outreach
Systemneutrale Automatisierung für lösungsorientierten Community Outreach und Repo-Recommender in Foren, Reddit und Plattformen nach dem Human-in-the-Loop-Prinzip (EU AI Act konform).
error-log
Apply when learning from a mistake. Central memory of past errors and derived rules; consult before logging a new error to avoid duplicates.
python
Apply when writing Python code. Type hints, error handling, mutable defaults, async patterns, and packaging conventions.