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 skills/agent-engineer-master/skill-engineer/humanizernpx skills add Agent-Engineer-Master/skill-engineer --skill humanizergit clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineerWhat 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 | $0.00077 | $0.02557 |
| Opus 5 | $0.00039 | $0.01278 |
| Sonnet 5 | $0.00015 | $0.00511 |
| Haiku 4.5 | $0.00008 | $0.00256 |
Grade D, and why
humanizer scanned grade D with 2 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 2d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- Built with Agent Engineer Master — get your own production-ready skill: www.agentengineermaster.com/skill-engineer --> Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
- Humanizer tools (gpthuman.ai etc.) are now detectable — never recommend them How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer: Remove AI Patterns + Inject Human Quality
Full routing notes
Removes AI writing patterns and injects human quality — specificity, burstiness, and precise emotional language. Use when editing drafted content, cleaning up LinkedIn posts, product copy, or blog content so it doesn't sound like AI. Two-phase process: Phase 1 removes 33 AI tells (significance inflation, AI vocabulary, em dash overuse, contrastive negation, vague attributions, soulless structure); Phase 2 injects humanity (sentence length variation, specificity via "what kind?" technique, precise emotional language). An optional personal-brand mode (below) adapts the two phases to a specific author's voice, using a scoreable voice-schema gate and a deterministic eval harness. Do not use for generating new content from scratch, fact-checking, SEO scoring, or research. Does not use humanizer tools (gpthuman.ai, WriteHuman) — those are detectable; this skill does structural rewriting.
Two-phase editing skill. Phase 1 removes AI tells. Phase 2 injects specificity, burstiness, and emotional precision. Clean-but-generic is still a failure — both phases are required.
Setup
Load both reference files before starting:
references/patterns-reference.md— 33 AI tell patterns with before/after examplesreferences/specificity-techniques.md— "What kind?" technique, 7 levers, Emotions Wheel
Both are mandatory. Do not skip either.
Brand Context Intake
Before editing, identify the context. If not supplied, ask:
"Which context is this for? (a) Personal brand — LinkedIn/X/blog, (b) Analytical deliverable (report/memo/brief), (c) Generic content"
| Context | Risk level | Quality gate |
|---|---|---|
| Personal brand (LinkedIn/X/blog) | Highest | Use Personal-brand mode below (voice reference + schema gate + eval harness). Always offer 2 variations. Read-aloud test mandatory. At least one thing only the author could know. |
Analytical deliverable (paired with a write-report-style caller before its humanization pass) |
Highest | Match the register the document already uses — keep analytical clarity, strip generic AI texture. Zero em-dashes. Preserve evidence tags, codes, and cited numbers untouched — relocate, never delete. |
| Generic content | Standard | Run Phase 1 + Phase 2 only. No voice gate. |
What ships with it
16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/evals.json 5.2 KB
- evals/judge.md 2.2 KB
- evals/rubric.md 4.1 KB
- fixtures/aiish/linkedin_aiish.json 537 B
- fixtures/before-after/simple_rewrite.json 534 B
- fixtures/gold/linkedin_clean.json 862 B
- README.md 9.2 KB
- references/edge-cases.md 193 B
- references/learnings.md 564 B
- references/patterns-reference.md 27 KB
- references/specificity-techniques.md 6.9 KB
- references/voice-schema-rubric.md 1.1 KB
- references/voice-schema.md 3.9 KB
- scripts/eval_voice.py 7.5 KB runs code
- tests/test_eval_voice.py 2.0 KB runs code
- WARP.md 3.3 KB
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.
- 2d ago First seen · 168 lines · 77 tokens per session scan D 6e30416e4003
humanizer is a skill published in the GitHub repository Agent-Engineer-Master/skill-engineer (8 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 2,557 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it D with 2 findings (hidden instructions, subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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