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/chianw/c31/humanize-ainpx skills add ChianW/C31 --skill humanize-aigit clone --depth 1 https://github.com/ChianW/C31Wrote 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/chianw/c31/humanize-ai)<a href="https://agentmods.dev/skills/chianw/c31/humanize-ai"><img src="https://agentmods.dev/badge/skills/chianw/c31/humanize-ai.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 5.1 | $0.00078 | $0.00791 |
| Opus 5 | $0.00039 | $0.00396 |
| Sonnet 5 | $0.00016 | $0.00158 |
| Haiku 4.5 | $0.00008 | $0.00079 |
Grade A, and why
humanize-ai 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 5d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize CLI
Command-line tools for detecting and auto-fixing AI writing patterns.
Scripts
analyze.py — Detect AI Patterns
Scans text and reports AI vocabulary, puffery, chatbot artifacts, and auto-replaceable phrases.
# Analyze a file
python scripts/analyze.py input.txt
# Analyze from stdin
echo "This serves as a testament to our commitment" | python scripts/analyze.py
# JSON output for programmatic use
python scripts/analyze.py input.txt --json
Output example:
==================================================
AI PATTERN ANALYSIS - 5 issues found
==================================================
AI VOCABULARY:
• testament: 1x
• crucial: 2x
AUTO-REPLACEABLE:
• "serves as" → "is": 1x
• "in order to" → "to": 1x
humanize.py — Auto-Replace Patterns
Performs automatic replacements for common AI-isms.
# Humanize and print to stdout
python scripts/humanize.py input.txt
# Write to output file
python scripts/humanize.py input.txt -o output.txt
# Include em dash replacement
python scripts/humanize.py input.txt --fix-dashes
# Quiet mode (no change log)
python scripts/humanize.py input.txt -q
What it fixes automatically:
- Filler phrases: "in order to" → "to", "due to the fact that" → "because"
- Copula avoidance: "serves as" → "is", "boasts" → "has"
- Sentence starters: removes "Additionally,", "Furthermore,", "Moreover,"
- Curly quotes → straight quotes
- Chatbot artifacts: removes "I hope this helps", "Let me know if", etc.
Workflow
-
Analyze first to see what needs fixing:
python scripts/analyze.py document.txt -
Auto-fix safe replacements:
python scripts/humanize.py document.txt -o document_clean.txt -
Manual review for AI vocabulary and puffery flagged by analyze (these require human judgment)
-
Re-analyze to confirm improvements:
python scripts/analyze.py document_clean.txt
Customizing Patterns
What ships with it
5 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.
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.
- 5d ago First seen · 127 lines · 78 tokens per session scan A 8651ee663560
humanize-ai is a skill published in the GitHub repository ChianW/C31 (1 stars, last pushed 10d ago), licensed MIT. It adds 78 tokens to every session and 791 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
compare-harnesses
Diff two scaffolded harnesses (ADR-031). Reports manifest meta drift + host list + per-file fingerprint changes (added/removed/changed). Exits 0 IDENTICAL, 1 DRIFT, 2 missing manifest. Use --bundle for the ADR-031 schema-1 JSON envelope.
create-harness
Scaffold your own focused AI agent harness — pick host (Claude Code, Codex, pi.dev, Hermes), template, agents, skills, and ship a npm-publishable harness with its own npx CLI. Use when a user asks to "create my own agent harness", "scaffold a harness", "make a custom Claude Code plugin like ruflo", or "build a…
diag-harness
Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable npm install @metaharness/[email protected] next step. Exits 2 if no .harness/manifest.json at path.
oia-manifest
Emit .harness/oia-manifest.json declaring layer alignment with the OIA v0.1 9-layer reference architecture. Self-describes the harness's MCP wiring, witness signing, audit log, identity posture (always 'none' at v0.1). --check verifies an existing manifest, --dry-run prints without writing, --json emits to stdout.
repo-genome
7-section readiness scorecard for a LOCAL repo. Reports repo type + agent topology + MCP risk + test confidence + release readiness + recommended harness plan + scorecard. Exit 0 ready, 1 needs-work, 2 blocked. --json for the 6-field scorecard, --bundle for the ADR-031 schema-1 envelope.
example-harness
Scaffold a ready-made AI agent harness in one command from the 19 published @metaharness/ example packages — 9 host integrations (Claude Code, Codex, Hermes, pi.dev, OpenClaw, RVM, Copilot, OpenCode, GitHub Actions) + 10 vertical pods (devops, research, trading, support, legal, coding, education, sales, gaming…