humanizer

humanizer

Turn a rough draft or context dump into writing that sounds like the user: direct, specific, technically credible, and not overproduced.

This skill is usually a second pass after another agent gathers context and writes a first draft. It can also create the first draft when the needed context is available.

Inputs

Use whatever the user provides:

• Draft text to rewrite.
• Target format: cover letter, article, update, post, email, profile blurb, prompt, or note.
• Audience and stakes.
• Voice samples or prior writing.
• Source context from a repo, job post, docs, notes, or knowledge profile.

If the user asks for "my style" and gives no sample, infer from the current conversation first. For higher-stakes writing, pull local context when relevant:

• Profile: $KNOWLEDGE_DIR/profile/USER.md
• Work history: $KNOWLEDGE_DIR/profile/WORK.md
• Job stories: $KNOWLEDGE_DIR/skills/jobs/data/story-bank.md or skills/jobs/data/story-bank.md from this repo
• Project-specific repo/docs/results when writing about a project.

Do not fabricate credentials, dates, metrics, motivations, links, or results. If a claim is useful but unsourced, ask for evidence or mark it as an assumption.

Modes

• Rewrite: improve an existing draft while preserving meaning.
• Voice match: analyze 1-3 samples, then rewrite using that rhythm, directness, and vocabulary level.
• Draft from context: create a compact first draft from supplied sources, then run the humanizer pass.
• Audit only: list AI tells and concrete fixes without rewriting.

Workflow

1. Define the job of the text.
   • What is it for, who reads it, how long should it be, and what should happen after they read it?
2. Gather only useful evidence.
   • Pull concrete facts, examples, decisions, tradeoffs, and outcomes.
   • For articles or benchmark updates, prefer actual repo results, commands, tables, traces, and caveats over abstract claims.
   • For applications, choose the 2-3 strongest match points instead of listing everything.
3. Build a short voice profile for this task.
   • Default user voice: direct, pragmatic, concise, skeptical of fluff, comfortable with technical specifics.
   • Preserve thinking style, not chat typos.
   • Use first person when it helps. Do not hide behind corporate phrasing.
4. Rewrite or draft.
   • Start with the actual point, not a ceremonial intro.
   • Use concrete nouns and verbs.
   • Keep paragraphs short. One idea per paragraph.
   • Let the text have a real opinion, tradeoff, or uncertainty when appropriate.
5. Anti-AI pass.
   • Remove generic hype: exciting, compelling, impactful, dynamic, innovative, cutting-edge, passionate, thrilled.
   • Remove stock frames: "I am writing to express", "Throughout my career", "In today's rapidly evolving landscape", "plays a crucial role", "it is worth noting".
   • Replace vague authority with sources, or delete it.
   • Replace inflated significance with what actually happened.
   • Break rule-of-three lists when they feel ornamental.
   • Prefer "is" over "serves as", "uses" over "leverages", "shows" over "showcases".
   • Remove dangling "-ing" clauses that add fake depth.
6. Honesty check.
   • Could this have been written by anyone about anything? Add specifics or cut it.
   • Is any claim unsupported? Qualify or remove it.
   • Is it too polished to sound like a person? Add a real constraint, concrete detail, or simpler sentence.
7. Return the output.
   • For rewrite, voice match, or draft-from-context: return the finished text first.
   • For audit only: use the "Quick Audit Output" format.
   • Add a short note only when useful: assumptions, removed claims, or optional alternate angle.

User Voice Defaults

• Plain English, concise but not clipped.
• Specific technical language when it carries information.
• Calm confidence rather than sales energy.
• Measured opinions and visible tradeoffs.
• Minimal ceremony.
• No forced warmth, jokes, or theatrical vulnerability.
• Avoid em dashes unless the target text really benefits from them.

Format Guidance

Technical Articles and Benchmark Updates

• Lead with the actual result or question.
• Include what changed, how it was measured, and what is still uncertain.
• Avoid pretending the result is universal when it is benchmark- or repo-specific.
• Use concrete model names, task counts, commands, dates, and links when available.
• Keep caveats readable, not apologetic.

Cover Letters and Applications

• Default to 250-450 words.
• Open with the real fit between the role and the user's work.
• Use 2-3 concrete evidence threads.
• Close calmly. Do not beg, over-flatter, or inflate enthusiasm.

Agent Prompts and Notes

• Keep instructions operational.
• Prefer direct constraints and exact paths.
• Remove motivational filler.
• Make success criteria explicit.

Quick Audit Output

When asked to audit, use:

AI tells:
- ...

Fixes:
- ...

Suggested rewrite:
...