Occupation · SOC 39-3092.00
Select, fit, and take care of costumes for cast members, and aid entertainers. May assist with multiple costume changes during performances.
Also called: Costumer · Dresser · Wardrobe Assistant · Wardrobe Supervisor · Costume Draper · Costume Seamstress · Draper · Wardrobe Attendant · Costume Attendant · Costume Cutter · Costume Dresser · Costume Mistress
Job family: Personal Care and Service Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-39-3092-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
34th-percentile task overlap — yet about 1,800 openings a year (+5.9% projected, BLS) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Moderate | 37th | -0.5 | |
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 39th | 0.4 | |
| AI assistant applicability (Microsoft) Low | 31st | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.3), and including AI-powered software (γ 0.4). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.6 · 52nd percentile among occupations · Moderate
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Review scripts or other production information to determine a story's locale or period, as well as the number of characters and required costumes. | 0.5% | |
| Provide managers with budget recommendations and take responsibility for budgetary line items related to costumes, storage, or makeup needs. | 0.4% | |
| Design or construct costumes or send them to tailors for construction, major repairs, or alterations. | 0.3% | |
| Return borrowed or rented items when productions are complete and return other items to storage. | 0.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +5.9% by 2034 |
| Projected annual openings | 1,800 |
| Employment 2024 → 2034 | 6,700 → 7,100 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Typical AI autonomy | 3.0 / 5 · higher = AI acts more independently |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Review scripts or other production information to determine a story's locale or period, as well as the number of characters and required costumes. | — | 0.3% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Review scripts or other production information to determine a story's locale or period, as well as the number of characters and required costumes. | 93.9% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me review scripts or other production information to determine a story's locale or period, as well as the number of characters and required costumes. From: Review scripts or other production information to determine a story's locale or period, as well as the number of characters and required costumes. · 0.3% of measured AI use
All 21 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
Newer responsibilities O*NET has flagged as growing for this occupation.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Fine Arts | 3.6 | |
| Design | 3.2 | |
| Production and Processing | 3.2 | |
| Customer and Personal Service | 3.1 | |
| English Language | 3.1 | |
| Psychology | 3.1 | |
| Administration and Management | 3.0 | |
| Mechanical | 2.8 | |
| Mathematics | 2.8 | |
| Education and Training | 2.6 |
| Oral Comprehension | 3.6 | |
| Oral Expression | 3.6 | |
| Problem Sensitivity | 3.3 | |
| Near Vision | 3.3 | |
| Fluency of Ideas | 3.1 | |
| Information Ordering | 3.1 | |
| Category Flexibility | 3.1 | |
| Speech Clarity | 3.1 | |
| Written Comprehension | 3.0 | |
| Originality | 3.0 | |
| Deductive Reasoning | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Selective Attention | 3.0 | |
| Arm-Hand Steadiness | 3.0 | |
| Finger Dexterity | 3.0 | |
| Speech Recognition | 3.0 | |
| Written Expression | 2.9 | |
| Manual Dexterity | 2.9 | |
| Visual Color Discrimination | 2.8 |
| Active Listening | 3.1 | |
| Speaking | 3.1 | |
| Monitoring | 3.1 | |
| Reading Comprehension | 2.9 | |
| Critical Thinking | 2.9 |
| Coordination | 3.1 | |
| Social Perceptiveness | 3.0 | |
| Service Orientation | 2.9 | |
| Persuasion | 2.8 | |
| Negotiation | 2.8 | |
| Time Management | 2.8 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
| Example | Category | |
|---|---|---|
| Microsoft Excel | Spreadsheet software | Hot technology In demand |
| Microsoft Office software | Office suite software | Hot technology In demand |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Database software | Data base user interface and query software | |
| Garment tracking software | Inventory management software | |
| Web browser software | Internet browser software |
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Visual and Performing Arts . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| High School Diploma | 32.2% | |
| Bachelor's Degree | 29.6% | |
| Some College Courses | 14.8% | |
| Less than a High School Diploma | 10.4% | |
| Post-Secondary Certificate | 7.5% | |
| Associate's Degree (or other 2-year degree) | 3.9% | |
| Master's Degree | 1.5% |
The interests and personal qualities O*NET associates with people who do this work.
| Applied Arts and Design | 5.8 | |
| Visual Arts | 3.6 | |
| Media | 3.0 | |
| Performing Arts | 3.0 | |
| Personal Service | 3.0 | |
| Humanities | 2.9 | |
| Physical/Manual Labor | 2.4 | |
| Management/Administration | 2.1 |
| Artistic | 5.6 | |
| Conventional | 4.3 | |
| Realistic | 3.9 | |
| Social | 3.3 | |
| Enterprising | 2.8 |
| Dependability | 3.0 | |
| Attention to Detail | 2.4 | |
| Cooperation | 2.1 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $33,610 |
| 25th percentile | $39,240 |
| Median (50th) | $54,810 |
| 75th percentile | $83,090 |
| 90th percentile | $115,240 |
| People employed | 6,290 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Arts, Entertainment, and Recreation · Sector | 3,590 | $48,190 |
| Theater Companies and Dinner Theaters · National industry | 1,880 | $48,190 |
| Information · Sector | 1,320 | $102,160 |
| Educational Services · Sector | 610 | $46,700 |
| Accommodation and Food Services · Sector | 510 | $44,900 |
| Casino Hotels · National industry | 350 | $43,810 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 160 | $87,160 |
| Temporary Help Services · National industry | 90 | $80,210 |
| Professional, Scientific, and Technical Services · Sector | 60 | $118,060 |
| Television Broadcasting Stations · National industry | 40 | $59,220 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Theater Companies and Dinner Theaters · National industry | 636.61× | 1,880 |
| Arts, Entertainment, and Recreation · Sector | 33.31× | 3,590 |
| Casino Hotels · National industry | 25.46× | 350 |
| Information · Sector | 11.13× | 1,320 |
| Educational Services · Sector | 1.1× | 610 |
| Accommodation and Food Services · Sector | 0.88× | 510 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.43× | 160 |
Part of the Arts, Entertainment, & Design career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Costume Attendants — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
See where this work sits in the bigger picture.
Costume Attendants show 34th-percentile AI task overlap — and about 1,800 annual U.S. openings
Costume Attendants show 34th-percentile AI task overlap — and about 1,800 annual U.S. openings • Costume Attendants rank in the 34th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 1,800 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+5.9%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $54,810, across about 6,290 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Costume Attendants". https://singulariki.com/roles/role-39-3092-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Costume Attendants." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-39-3092-00
Singulariki. (2026). Costume Attendants. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-39-3092-00
@misc{singulariki-role-39-3092-00,
title = {Costume Attendants},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-39-3092-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.