preference-learner

A personalization tool that records a user's writing habits, communication preferences, and working style, then applies them in later interactions.

In plain words
What is it for?
Use it to keep responses and generated writing consistent with an author's preferred style and communication habits.
Why use it?
It reduces the need to repeat preferred tone, length, formatting, and writing rules each time.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/ckokoski/authoragent/preference-learner
Any agent
npx skills add Ckokoski/AuthorAgent --skill preference-learner
Clone the repo
git clone --depth 1 https://github.com/Ckokoski/AuthorAgent

Made for: Claude Code, Codex.

Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,728 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00020 $0.01728
Opus 5 $0.00010 $0.00864
Sonnet 5 $0.00004 $0.00346
Haiku 4.5 $0.00002 $0.00173

Measured 2d ago against content hash 0794076c1992, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

preference-learner 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 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.

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.

skills/_archived/core/preference-learner/SKILL.md · 221 lines

How it starts

The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Preference Learner — Core Skill

Every author is different. This skill builds a living profile of the user's preferences, habits, and working style — then applies it to every interaction so AuthorClaw feels increasingly personalized.

What Gets Learned

Writing Preferences

writing:
  # Detected from user revisions and feedback
  dialogue_tags: "simple (said/asked only)"
  description_density: "moderate (1-2 sensory details per scene)"
  paragraph_length: "short (2-4 sentences)"
  chapter_length: "2500-3500 words"
  pov_preference: "third person limited"
  tense: "past"
  profanity_level: "mild"
  romance_heat_level: "closed door"
  violence_level: "moderate"
  humor_style: "dry, situational"

  # Specific dos and don'ts
  always:
    - "Start chapters with action or dialogue, never description"
    - "End chapters on a hook or question"
    - "Use Oxford comma"
  never:
    - "Use adverbs in dialogue tags (said softly, whispered quietly)"
    - "Start sentences with 'Suddenly'"
    - "Use the word 'whilst'"

Communication Preferences

communication:
  response_length: "concise (under 200 words unless writing prose)"
  status_update_frequency: "after each major step"
  question_threshold: "only ask if truly ambiguous (err on acting)"
  emoji_usage: "moderate"
  formality: "casual, friendly"
  explanation_depth: "brief unless asked for detail"
  preferred_channel: "telegram"

Working Style

workflow:
  active_hours: "6am-10pm"
  most_productive_time: "morning (6am-noon)"
  session_length: "30-60 minutes"
  break_reminders: true
  daily_word_goal: 2000
  preferred_goal_size: "medium (5-8 steps)"
  review_preference: "review after each chapter, not after each scene"
  file_organization: "by project, then by chapter"
  naming_convention: "chapter-01-title.md"

Genre & Market Preferences

market:
  primary_genre: "psychological thriller"
  subgenres: ["domestic suspense", "unreliable narrator"]
  target_audience: "women 25-45, fans of Gillian Flynn"
  publishing_path: "traditional (querying agents)"
  comp_titles: ["The Wife Between Us", "The Last Thing He Told Me"]
  word_count_target: 80000
  series_vs_standalone: "standalone with series potential"

Read the full file on GitHub · 221 lines

Changes

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.

  1. 2d ago First seen · 221 lines · 20 tokens per session scan A 0794076c1992

Subscribe to this mod's changes

preference-learner is a skill published in the GitHub repository Ckokoski/AuthorAgent (102 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 1,728 once invoked, about $0.0001 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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