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/mathews-tom/armory/humanizenpx skills add Mathews-Tom/armory --skill humanizegit clone --depth 1 https://github.com/Mathews-Tom/armoryWhat 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 | $0.00066 | $0.02782 |
| Opus 5 | $0.00033 | $0.01391 |
| Sonnet 5 | $0.00013 | $0.00556 |
| Haiku 4.5 | $0.00007 | $0.00278 |
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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize: AI Pattern Detection and Removal
Remove AI-generated writing patterns from text. Produce natural, human-sounding output that preserves meaning.
This is not a generic rewriter. It targets specific, documented AI-writing patterns catalogued by Wikipedia's WikiProject AI Cleanup from thousands of observed instances.
Workflow
Five phases. Each phase has a clear input, transformation, and output. Do not skip phases.
Phase 1: Detection Scan
Read the input text. Load references/detection-patterns.md. Scan for two categories of signals:
A. Lexical patterns (the 24 catalogued AI-writing patterns):
| Category | Patterns | Priority |
|---|---|---|
| Content inflation | Significance puffing, notability claims, superficial -ing analyses, promotional language, vague attributions, formulaic challenges sections | HIGH — loudest AI tells |
| Vocabulary | AI-frequency words, copula avoidance, filler phrases, excessive hedging | HIGH — statistically detectable |
| Structure | Rule of three, negative parallelisms, elegant variation, false ranges, inline-header lists | MEDIUM — structural fingerprints |
| Style | Em dash overuse, boldface overuse, title case headings, emoji decoration, curly quotes | MEDIUM — formatting tells |
| Communication | Chatbot artifacts, knowledge-cutoff disclaimers, sycophantic tone, generic conclusions | LOW — obvious, usually caught by author |
What ships with it
8 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.
- 3d ago First seen · 217 lines · 66 tokens per session scan A 5514c508c27f
humanize is a skill published in the GitHub repository Mathews-Tom/armory (316 stars, last pushed 5d ago), licensed MIT. It adds 66 tokens to every session and 2,782 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.
Other skills, from other repositories
manager
Sync session work into GitHub issues, or query track status across repos. Write: find/update issue + W-label. Read: "что по ". Triggers: "создай issue", "синкни сессию", "manager". Not day/week plans (daily-tasks).
weekly-retro
Use when the user is closing an ISO week, reviewing planned outcomes against evidence, interviewing work Areas, resolving unfinished commitments, or asking for "ретро", "weekly retro", "week review", or lessons from the week.
art-director
Orchestrate iterative visual style searches with branch prompts, decision graphs, feedback loops, and final direction selection.
corp-doctor
Use when a Personal Corp operating loop needs setup, repair, a new department, or task routing: HQ files and agent rules, GitHub issue workflow, corp- owner map, department repositories, or deciding which repo an issue belongs to. Triggers: "corp doctor", "почини контур", "заведи отдел", "куда положить задачу"…
html-draft
Use when user wants a standalone HTML diagram in flat engineering blueprint style — architecture diagrams, system flows, technical spec sheets, component maps. Generates one HTML file using Tailwind v4 (browser CDN) for layout and D3 v7 (CDN) for SVG diagrams. User-invoked only — do NOT auto-trigger. Triggers on…
idea
Use when capturing ONE new idea the user voices and wants recorded — "save this idea", "I have an idea", "log this idea", "/idea", "idea: ...". Creates a provenance-tracked folder (one folder per idea) in your ideas repo, dedups against an index, optionally mirrors to a GitHub Project view filtered by label:idea. NOT…