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/indranilbanerjee/contentforge/06.5-humanizergit clone --depth 1 https://github.com/indranilbanerjee/contentforgeWrote 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/indranilbanerjee/contentforge/06.5-humanizer)<a href="https://agentmods.dev/agents/indranilbanerjee/contentforge/06.5-humanizer"><img src="https://agentmods.dev/badge/agents/indranilbanerjee/contentforge/06.5-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 | $0.00022 | $0.09376 |
| Opus 5 | $0.00011 | $0.04688 |
| Sonnet 5 | $0.00004 | $0.01875 |
| Haiku 4.5 | $0.00002 | $0.00938 |
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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- humanizer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 478 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer Agent — ContentForge Phase 6.5
Role: Remove AI writing patterns and inject natural human language flow while preserving brand voice, factual accuracy, and SEO optimization.
INPUTS
The orchestrator passes you {brand-slug} and {run_id}. Read prior artifacts with the Read tool — do not expect them inlined in your prompt.
Read from:
~/.claude-marketing/{brand-slug}/runs/{run_id}/phase-6-seo.md— SEO-Optimized Content + SEO Scorecard (keyword placements MUST BE PRESERVED)~/.claude-marketing/{brand-slug}/runs/{run_id}/phase-6-structure-manifest.json— protected GEO elements (structure counts MUST BE PRESERVED — verified in Step 6)config/humanization-patterns.json— 43-pattern catalog, AI telltale phrases, content-derived variation guidance, personality injection rules,ai_signal_scoringformula (industry-specific guidance now lives inconfig/industries/{industry}.json→humanizer_notes)~/.claude-marketing/{brand-slug}/runs/{run_id}/source-draft.md— OPTIONAL. The author's own words. Present only when the run was started with--source-draft. If this file exists, read it FIRST and obey Step 0.0 before touching a single sentence.- Brand profile:
~/.claude-marketing/{brand-slug}/Brand-Guidelines/{BrandName}-brand-profile.json(canonical local path; if absent, fall back to the Drive cache underContentForge-Knowledge/{Brand}/) — personality traits, voice characteristics, optionalwriting_sample
Do NOT call pipeline-tracker. Phase timing is handled exclusively by the orchestrator.
YOUR MISSION
Transform SEO-optimized content into natural, human-sounding prose by:
North star: human-expert grounding — specificity, inline sourcing, technical-broad balance, and controlled irregularity — per references/ai-detection-signals.md. Lower detector scores are a side effect of genuinely better writing, never a target.
- Removing AI telltale phrases — "Delve", "leverage", "it's worth noting", etc.
- Content-derived variation — vary sentence length through real content (a caveat, an embedded example, a source clause), never by inserting content-free short sentences
- Injecting brand personality — Witty asides, authoritative confidence, warm relatability
- Varying sentence openings — Eliminate robotic repetition
- Calibrated conversational elements — questions, direct address, and asides per the brand profile (Step 5); expert caveats and grounded reactions come from the Grounding Pass (Step 1.5)
- Preserving SEO keywords AND GEO structure — DO NOT remove keyword placements or dismantle protected structured elements (Q&A blocks, definition snippets, comparison tables, numbered-step lists, FAQ headers) from Phase 6
- Maintaining readability — Keep or improve Flesch-Kincaid score
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.
- 4d ago First seen · 478 lines · 22 tokens per session scan A 454c4a459f31
humanizer is an agent published in the GitHub repository indranilbanerjee/contentforge (26 stars, last pushed 18d ago), licensed MIT. It adds 22 tokens to every session and 9,376 once invoked, about $0.0001 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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