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/fcakyon/claude-codex-settings/humanizenpx skills add fcakyon/claude-codex-settings --skill humanizegit clone --depth 1 https://github.com/fcakyon/claude-codex-settingsWhat 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 | $0.00056 | $0.02326 |
| Opus 5 | $0.00028 | $0.01163 |
| Sonnet 5 | $0.00011 | $0.00465 |
| Haiku 4.5 | $0.00006 | $0.00233 |
Grade A, and why
humanize 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 3d 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize
Write like a person with a reason to write. Choose words for meaning, not to sound polished. Match the user's vocabulary, tone, and level of formality. Keep useful detail and remove generic filler.
These patterns are warning signs, not proof of AI authorship. Fix the underlying weakness rather than hiding a tell with a synonym.
Rewrite method
- Identify the main claim, useful facts, and requested action.
- Delete throat-clearing, repeated conclusions, and claims that add no information.
- Replace vague importance and opinion claims with names, dates, actions, measurements, or sources.
- Use the simplest sentence structure that preserves the meaning.
- Match the format to the task. Do not turn a short answer into an article.
Content problems
Inflated importance and broad trends
Do not claim that ordinary facts mark a major shift, reflect a wider movement, or leave a lasting legacy unless the evidence makes that connection.
Bad: The office opened in 1989, marking a pivotal moment in the evolution of regional administration.
Better: The office opened in 1989 to manage regional statistics.
Bad: The bridge stands as a testament to the town's enduring spirit and its place in the broader history of trade.
Better: The bridge opened in 1924 and carried the main road across the river until 1981.
Shallow analysis attached to facts
Avoid adding an -ing phrase that only tells the reader that a fact matters. Explain the real consequence or stop after the fact.
Bad: The station has six platforms, underscoring its crucial role in regional transport.
Better: The station has six platforms and handles 42 scheduled trains each day.
Bad: The archive added 3,000 photographs, highlighting its commitment to preserving local history.
Better: The archive added 3,000 photographs from the city newspaper's collection.
Promotional or travel-guide language
Describe subjects instead of selling them. Remove praise that could fit a brochure, company profile, property listing, or tourism page.
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.
- 3d ago First seen · 259 lines · 56 tokens per session scan A 92cf9bd27d0e
humanize is a skill published in the GitHub repository fcakyon/claude-codex-settings (1,117 stars, last pushed yesterday), licensed Apache-2.0. It adds 56 tokens to every session and 2,326 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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