agentleFS
olcayseyganolcayseygan/crumora/skills/humanize/SKILL.md

humanize

Skill0 starsChanged 42 days agoMITView on GitHub

Rebuilds an interface's interaction around the person in scored rounds. Each round walks the tasks through the render as a first-time person, rebuilds the flow three ways in parallel, gates each on every task completing, and a blind judge ranks them against the champion from their task traces, the frozen people, tasks and rubric; the best that beats the champion and survives the interaction checklist takes the throne. Stops when none does, 4 rounds max. Use when the user says "/humanize", "this UI is terrible", "the UX is bad", "make this usable", "make it feel right", "users get lost here", "too many clicks", "improve the user experience", "make the interaction better", "make it human", or the Turkish "ux'i iyilestir", "kullanici deneyimini iyilestir", "bu tasarim sacma", "olmasi gerektigi gibi yap", "etkilesimi iyilestir", "kullanici burada kayboluyor", "cok tikla gidiyor". For how a screen looks and reads use compose; for the fixed 73-pattern pass use muster; for measuring rendered pixels use gauge.

74 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use humanize-redo, humanize-korean and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • dawn840705Skill

    humanize-redo

    가장 최근 윤문 결과를 2차로 다시 다듬는다 — 특정 카테고리·문단·강도 조정도 가능. humanize-korean 의 윤문 콜을 기존 run_id 에 재실행해 잔존 finding 을 처리한다. 트리거 — "$humanize-redo". **명시적 슬래시 호출 전용** — 자연어 후속 요청은 `humanize-korean` 오케스트레이터가 받는다.

    Also in 5 projects that use this

  • mskim8717Skill

    humanize-korean

    AI(ChatGPT·Claude·Gemini 등)가 쓴 한글 텍스트를 "사람이 쓴 글처럼" 윤문해주는 오케스트레이터 스킬. 번역투·영어 인용 과다·기계적 병렬·관용구·피동태 남용·접속사 남발·리듬 균일성·이모지/불릿 과다 등 10대 카테고리 40+ AI 티 패턴을 탐지·분류해 내용은 한 글자도 건드리지 않고 문체·리듬·표현만 자연스러운 한국어로 재작성한다. 트리거 — "AI 티 없애줘", "AI 같은 글 자연스럽게", "GPT/ChatGPT 문체", "AI 번역투 고쳐", "사람이 쓴 것처럼 윤문", "AI 윤문", "ChatGPT 티 제거", "한글 AI 탐지·윤문", "AI 글 사람처럼", "번역투 제거", "영어 인용 많은 글 윤문", "AI 글 티 안 나게", "휴머나이저", "humanize Korean", "AI detector bypass 한글". 후속 작업 — "특정 카테고리만 다시", "윤문 강도 조정", "장르 바꿔서", "이 문단만", "2차 윤문" 도 모두 이 스킬. 단순 맞춤법·오탈자 교정은 직접 처리, 번역은 번역 스킬, 내용 추가·삭제를 동반한 재작성은 별도 집필 스킬.

    Also in 3 projects that use this

  • CoralGameSkill

    perf-profile

    Structured performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.

    Also in 3 projects that use this

  • Rayan55050Skill

    content

    Создание контента: посты, статьи, тексты

    Also in 3 projects that use this

  • luischanciSkill

    proofread

    Run the proofreading protocol on manuscript files. Checks grammar, typos, overflow, consistency, and academic writing quality. Produces a report without editing files.

    Also in 3 projects that use this

  • insight68Skill

    pricing

    Help figure out pricing for a product or service using minimalist entrepreneur principles. Use when someone is setting prices, considering price changes, or struggling with what to charge.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 64 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read the flow's real context — routes, state, API calls, validation, error handling, permissions — not just the markup.

    line 24
  2. Distinctive:

    Use the project's components and tokens.

    line 70
  3. Distinctive:

    Behaviour the product depends on is not negotiable (MUST): Every binding, event, API call, server-side validation, permission check and …

    line 72
  4. Distinctive:

    Possible mistakes: always state that the walk is a proxy for a real person, not a usability test, then name guessed people, tasks outside …

    line 173

“Common” is measured: kept by at least 6 in 10 of the 64 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

humanize — rebuild the interface around the person

The tournament skill for behaviour: it judges what a person goes through to get the job done — steps, hesitations, waits, mistakes, recovery — not how the surface looks (that is compose).

Load the matching design skill first, if available: frontend-design for web UI, dataviz for charts, artifact-design for a published page.

Read ../_shared/tournament.md first — it defines the loop and binds this skill; the rubric below replaces its code rubric.


0. Pick the target and freeze the people and the tasks

Target = the argument (/humanize the checkout); none → ask one question: which screen, flow or component.

Read the flow's real context — routes, state, API calls, validation, error handling, permissions — not just the markup. If the real obstacle is upstream (API, backend errors, routing), say so before round 1.

Freeze for the whole run:

  1. The people. Who, how often (first visit / weekly / daily), on what input (mouse, touch, keyboard, screen reader), in what context. Unknown → stated as an assumption, marked guessed.
  2. The task set. Three to seven real tasks, each the person's goal in their own words, with a start state and an observable done state, ordered by frequency × consequence. Plus the stress paths, walked by every challenger at the round's viewports (tournament.md §4.3):
    • wrong input — typo, invalid value, duplicate;
    • first run and empty state;
    • throttled network and aborted request (page.route abort);
    • interruption — reload or navigate away mid-task, then come back;
    • regret — undo the thing just done;
    • long content — longest name, 40-item list, biggest number;
    • keyboard only;
    • narrow touch viewport.
  3. The constraints. What cannot move: API contract, data model, platform idiom, existing tokens and components, legal or compliance copy.

1. Setup (round 0)

tournament.md §1, work folder <scratchpad>/humanize/<target-slug>/, each r<N>/c<k>/ holding the source, trace.md and the step screenshots. Walk every task and stress path through the current interface (§3) before anything is designed — round 0's trace is the baseline — and run references/interaction.md on it; its misses cap the champion.

2. The challenger

The round itself — three ideas, parallel builders, one blind judge, crowning — is tournament.md §4. What each challenger is:

(a) An interaction idea — one sentence: what changes in how the person gets the job done. A restyle of the same flow is not an idea.

(b) Rebuilt from the job — from the people and the task set, not the current screens. Declare in r<N>/c<k>/contract.md:

  • flow map — per task, the states from start to done;
  • primary move — per screen, the one obvious next thing;
  • feedback contract — what answers each action, where, within how many ms;
  • error contract — what is prevented, caught (where, when), undone (how);
  • defaults — what is prefilled, remembered or inferred.

Use the project's components and tokens; visual work only as far as the interaction needs.

Behaviour the product depends on is not negotiable (MUST). Every binding, event, API call, server-side validation, permission check and analytics event travels through the rebuild.

(c) Gate (MUST) — tournament.md §3, plus every task in the set completes end to end in the rendered interface, driven with the browser or Electron tools, not reasoned from source. A task that cannot be finished takes the challenger out of the round. With no runnable check, unverified goes on the score sheet.

(d) Walked (§3) — every challenger; the champion's trace is the one from the round it won.

(e) Judged (§4 rubric, §5 judge inputs and red lines).

(f) Pre-crown check — only on challengers that beat the champion, highest-ranked first (tournament.md §4.7): references/interaction.md in full, driven through the render. Its caps (§4) apply before it can take the throne.

3. The walk

Drive each task through the render as the person frozen in §0 — knowing the goal, not the interface; no shortcuts from knowing where things are. Launch the project the way it already runs (run skill) and act with Playwright or the Electron tools.

Per step, one row in trace.md:

#Person wantsSeesDoesInterface answersms

plus the four cognitive-walkthrough questions, yes or no: will they try the right thing, will they see the control, will they connect it to the goal, will they see it worked. Every no is a friction point, logged with its step number and the question it failed.

Per task, totals taken from the run, never estimated:

  • steps — clicks, taps, key chords, fields typed, scrolls to find something;
  • decisions — points with more than one plausible move;
  • context switches — page changes, modals, tabs, windows;
  • recall load — things carried from one screen to another;
  • pointer travel — summed px between consecutive targets on desktop; on a phone, targets outside the thumb zone;
  • feedback latency — worst ms from action to visible response, measured with performance.now() or a trace, never off screenshots;
  • dead ends — states whose only way forward is back;
  • error cost — on stress paths, steps to recover and whether typed input was lost;
  • unguarded irreversibles — destructive actions with neither undo nor confirmation.

The §0 stress paths are walked as tasks too.

4. Scoring (the human-interaction rubric)

Replaces tournament.md §2. Six criteria, each 0-10, weighted total 0-100; the shared scoring rules (§3 there) still apply.

CriterionWeightWhat it measures
Task efficiency20Steps, decisions, context switches and travel for each task against the round 0 trace; the most frequent task is the cheapest; no step exists for the interface's sake rather than the person's — re-entering known data, confirming the obvious, visiting a page to press one button
Clarity of the next move20Walkthrough questions answer yes; one primary move per screen; labels in the person's words and buttons named for their result; things look like what they do; nothing needed hides behind hover, a menu or a mode; location, selection and mode always visible
Error prevention & recovery20Constraints and defaults stop mistakes before they happen; validation on blur, next to the field, saying what to do; nothing typed is ever lost to an error, back or reload; reversible actions execute with an undo instead of a confirm, window per muster undo-ux (5s default, 10s for bulk or delayed send); truly irreversible ones confirm by naming the object and the consequence
Feedback & system status15Every press answers visibly under 100ms; work past ~300ms shows progress where the action happened; long work says what is happening and can be left or cancelled; no double submit; success confirmed where the eye already is; state changes announced to assistive technology
Reach & inclusiveness15Every task completes keyboard-only in task order with a visible focus ring; shortcuts for the daily expert, discoverable; touch targets ≥ 44px inside thumb reach; screen reader completes every task; 200% zoom and 360px still complete; reduced motion respected; nothing by colour alone; text at AA
Memory & cognitive load10Recognition over recall; nothing carried between screens; smart defaults and remembered choices; few options per decision, long lists searchable; progressive disclosure that the frequent task never needs to open; an interrupted task resumes; same action, same name, same place everywhere

On top of the shared rules:

  • Every reason cites the trace — a task, a step number and a number.
  • Fewer steps is not automatically better. A step removed by hiding an option, guessing silently or merging two decisions into one unclear control counts against Clarity and Error recovery, not for Efficiency.

Caps, from references/interaction.md, applied at the pre-crown check: a task not completable keyboard-only holds Reach at ≤ 5; any miss in the Errors section holds Error prevention & recovery at ≤ 6.

5. Judging and red lines

tournament.md §4.4. The judge receives the frozen people, the task set, the rubric, and for every version (A, B, C…) its trace.md with its step screenshots and totals. Not the source, not which is the incumbent, not the round numbers. Every criterion verdict names a task and a step.

Red lines — rank below every clean version regardless of score:

  • a task in the set cannot be completed, or a stress path loses typed input the champion kept;
  • the most frequent task costs more steps or more decisions than in the champion;
  • an irreversible action lost its undo or its confirmation;
  • the keyboard-only path breaks, or the focus ring disappears anywhere on it;
  • any text fails AA contrast;
  • a validation, permission check, API call, binding or analytics event was dropped;
  • the project's existing tokens or components were ignored in favour of invented ones.

6. Finish and apply

tournament.md §5 — the apply-time check is the whole task set walked again on the applied result, stress paths included, at every declared viewport.

7. Final analysis

Format: tournament.md §6. This skill's table:

RoundInteraction ideas (c1 · c2 · c3)GatePool (best → worst)Top challenger: top task steps / friction pointsPre-crown checkChampion

Under How we did it: the winner's flow map with before → after steps, decisions and worst latency per task, its feedback and error contracts, and which idea from a losing challenger survived. Under Possible mistakes: always state that the walk is a proxy for a real person, not a usability test, then name guessed people, tasks outside the set, network conditions and devices not simulated, and timings taken on a dev build.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.