Find agent skills by outcome

What Apple checks: Apple's automated static analysis scans the binary for calls to private Objective-C selectors

undocumented framework symbols

> This is likely to be flagged. Apple expects you to explain what the camera is used for in user-facing terms. Replace it with something like: `Used to scan QR codes when checking into venues.` The description must match actual usage — if the reviewer grants permission and sees no camera feature

the app is rejected under 5.1.1.

Be specific. Do not give generic advice. Reference the actual guideline number. If the user provides code or feature details

tailor your response to their actual implementation.

blood grouping detection using image processing

blood grouping detection using image processing i need a complete code for this project to buil api or mini website using python

use_case: Art history dataset

style transfer model training

environmental_storytelling: The dusky city street

glowing lights

use_case: Training data for style transfer AI

art history analysis

environmental_storytelling: The interaction of shapes and colors—overlapping

intersecting

environmental_storytelling: The image is a visual pun on the dachshund's long body

exaggerating it to an absurd degree to create a decorative

Architecture & UI/UX Audit

Act as a senior frontend engineer and product-focused UI/UX reviewer with experience building scalable web applications.

- Never rewrite

delete

Productivity

- Append-only to these files: `task.md`

`implementation-plan.md`

- Write clear user stories

acceptance criteria

I'm a political science PhD candidate who uses R regularly but would never call myself an R person. I needed a Claude Code skill for base R — something without tidyverse

without ggplot2

writeLines(template

out_file)

# write.csv(df

%s_clean.csv

# write.csv(df

data_clean.csv

- `arrows`: `code = 1` (head at start)

`code = 2` (head at end

- `SANN` (simulated annealing): convergence code is always 0 — it never fails. `maxit` = total function evals (default 10000)

no other stopping criterion.

- `URLencode(url

reserved = FALSE)` by default does NOT encode reserved chars (`/`

- `write.csv` fixes `sep =

- `as.numeric(f)` returns integer codes

not original values. Use `as.numeric(levels(f))[f]` or `as.numeric(as.character(f))`.

- Factor indexing: `x[f]` uses integer codes of factor `f`

not its character labels. Use `x[as.character(f)]` for label-based indexing.

write.csv(df

output.csv