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 commands/superuser-pal/awesome-second-brain/humanizegit clone --depth 1 https://github.com/superuser-pal/awesome-second-brainWhat 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 | $0.00023 | $0.00857 |
| Opus 5 | $0.00012 | $0.00428 |
| Sonnet 5 | $0.00005 | $0.00171 |
| Haiku 4.5 | $0.00002 | $0.00086 |
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.
This is a copy
95% identical to om-humanize — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Edit a note to match your writing voice. This is voice calibration, not pattern removal — learn HOW you write, not just what to avoid.
Usage
/humanize <file path or note name>
Workflow
1. Load Voice Samples
Read 2-3 recent notes you actually wrote or heavily edited to calibrate voice:
brain/NORTH_STAR.md— how you write about yourself- The most recent
work/02_1-1/*.mdnote — natural conversational voice - Any brain note with your authentic writing style
Extract voice fingerprint: sentence length, punctuation habits, how you open sections, how you qualify statements, ratio of direct-to-hedged language, use of dashes and fragments.
2. Read Target Note
Read the note specified in $ARGUMENTS (resolve as wikilink name or file path).
Detect context from frontmatter and folder:
work/02_1-1/→ conversational, direct, uses "I", okay to be informalwork/05_REVIEW/review content → corporate-confident but human, evidence-based, respect charcountwork/03_INCIDENTS/→ precise, factual, timeline-oriented, no fillerbrain/→ terse shorthand, fragments okay- Default → colleague-to-colleague, like explaining something in a 1:1
3. Edit In-Place
Rewrite the note's content to match your voice. Key principles:
Voice rules (from samples):
- Direct statements, not hedged ones ("This was stressful" not "This presented some challenges")
- Match your natural rhythm — fragments, dashes, whatever you actually use
- Observations should be sharp, not softened
- A concise 600-char section is better than a padded 950-char one
Anti-patterns (kill these):
- "Notably", "significantly", "demonstrates", "leveraged", "facilitated"
- "It's worth noting that..." — just note it
- "This showcases..." — just describe what happened
- Hedge stacking: "potentially", "arguably", "it could be said that"
- Empty transitions: "Moving forward", "In terms of", "With regard to"
- Passive voice where active is natural: "was identified" → "found"
- Bullet points that all start with the same word pattern
- Rhetorical questions followed by immediate answers
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 · 83 lines · 23 tokens per session scan A 8aeb02cb2733
humanize is a command published in the GitHub repository superuser-pal/awesome-second-brain (14 stars, last pushed 4mo ago), licensed MIT. It adds 23 tokens to every session and 857 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to om-humanize, differing in 14 lines, and is treated as a copy.
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