mcpbeat Sign in

Asc Shots Pipeline Agent Skill

by rorkai

Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload pipelines.

3k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
946
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/rorkai/app-store-connect-cli-skills --skill asc-shots-pipeline

The instruction itself

12 sections, as written by the author

asc screenshots pipeline (xcodebuild -> AXe -> frame -> asc)

Use this skill for agent-driven screenshot workflows where the app is built and launched with Xcode CLI tools, UI is driven with AXe, and screenshots are uploaded with asc.

Current scope

  • Implemented now: build/run, AXe plan capture, frame composition, and upload.
  • Device discovery is built-in via asc screenshots list-frame-devices.
  • Local screenshot automation commands are experimental in asc cli.
  • Framing is pinned to Koubou 0.18.1 for deterministic output.
  • Feedback/issues: https://github.com/rorkai/App-Store-Connect-CLI/issues/new/choose

Defaults

  • Settings file: .asc/shots.settings.json
  • Capture plan: .asc/screenshots.json
  • Raw screenshots dir: ./screenshots/raw
  • Framed screenshots dir: ./screenshots/framed
  • Default frame device: iphone-air

1) Create settings JSON first

Create or update .asc/shots.settings.json:

{
  "version": 1,
  "app": {
    "bundle_id": "com.example.app",
    "project": "MyApp.xcodeproj",
    "scheme": "MyApp",
    "simulator_udid": "booted"
  },
  "paths": {
    "plan": ".asc/screenshots.json",
    "raw_dir": "./screenshots/raw",
    "framed_dir": "./screenshots/framed"
  },
  "pipeline": {
    "frame_enabled": true,
    "upload_enabled": false
  },
  "upload": {
    "version_localization_id": "",
    "device_type": "IPHONE_65",
    "source_dir": "./screenshots/framed"
  }
}

If you intentionally skip framing, set:

  • "frame_enabled": false
  • "upload.source_dir": "./screenshots/raw"

2) Build and run app on simulator

Use Xcode CLI for build/install/launch:

xcrun simctl boot "$UDID" || true

xcodebuild \
  -project "MyApp.xcodeproj" \
  -scheme "MyApp" \
  -configuration Debug \
  -destination "platform=iOS Simulator,id=$UDID" \
  -derivedDataPath ".build/DerivedData" \
  build

xcrun simctl install "$UDID" ".build/DerivedData/Build/Products/Debug-iphonesimulator/MyApp.app"
xcrun simctl launch "$UDID" "com.example.app"

Use xcodebuild -showBuildSettings if the app bundle path differs from the default location.

3) Capture screenshots with AXe (or asc screenshots run)

Prefer plan-driven capture:

asc screenshots run --plan ".asc/screenshots.json" --udid "$UDID" --output json

Useful AXe primitives during plan authoring:

axe describe-ui --udid "$UDID"
axe tap --id "search_field" --udid "$UDID"
axe type "wwdc" --udid "$UDID"
axe screenshot --output "./screenshots/raw/home.png" --udid "$UDID"

Minimal .asc/screenshots.json example:

{
  "version": 1,
  "app": {
    "bundle_id": "com.example.app",
    "udid": "booted",
    "output_dir": "./screenshots/raw"
  },
  "steps": [
    { "action": "launch" },
    { "action": "wait", "duration_ms": 800 },
    { "action": "screenshot", "name": "home" }
  ]
}

4) Frame screenshots with asc screenshots frame

The asc CLI pins framing to Koubou 0.18.1.

Install and verify before running framing steps:

pip install koubou==0.18.1
kou --version  # expect 0.18.1
# If Koubou reports missing device frames, run once with network access:
kou setup-frames

List supported frame device values first:

asc screenshots list-frame-devices --output json

Frame one screenshot (defaults to iphone-air):

asc screenshots frame \
  --input "./screenshots/raw/home.png" \
  --output-dir "./screenshots/framed" \
  --device "iphone-air" \
  --output json

Supported --device values:

  • iphone-air (default)
  • iphone-17-pro
  • iphone-17-pro-max
  • iphone-16e
  • iphone-17
  • mac

5) Upload screenshots with asc

Generate and review artifacts before upload:

asc screenshots review-generate --framed-dir "./screenshots/framed" --output-dir "./screenshots/review"
asc screenshots review-open --output-dir "./screenshots/review"
asc screenshots review-approve --all-ready --output-dir "./screenshots/review"

For reviewed multi-locale sets, prefer the plan/apply flow so existing remote screenshot counts are included before upload:

asc screenshots plan --app "APP_ID" --version "1.2.3" --review-output-dir "./screenshots/review" --output json
asc screenshots apply --app "APP_ID" --version "1.2.3" --review-output-dir "./screenshots/review" --confirm --output json

Upload from the configured source directory (default ./screenshots/framed when framing is enabled):

asc screenshots upload \
  --version-localization "LOC_ID" \
  --path "./screenshots/framed" \
  --device-type "IPHONE_65" \
  --output json

List or validate before upload when needed:

asc screenshots sizes --output table
asc screenshots list --version-localization "LOC_ID" --output table

Agent behavior

  • Always confirm exact flags with --help before running commands.
  • Re-check command paths with asc screenshots --help because screenshot commands are evolving quickly.
  • Keep outputs deterministic: default to JSON for machine steps.
  • Prefer asc screenshots list-frame-devices --output json before selecting a frame device.
  • Ensure screenshot files exist before upload.
  • Use explicit long flags (--app, --output, --version-localization, etc.).
  • Treat screenshot-local automation as experimental and call it out in user-facing handoff notes.
  • Use asc screenshots plan / asc screenshots apply for reviewed batches when you need append-limit guardrails across existing remote screenshots.
  • If framing fails with a version error, re-install pinned Koubou: pip install koubou==0.18.1.
  • If framing fails because device frames are missing, run kou setup-frames once with network access.

6) Multi-locale capture (optional)

Do not use xcrun simctl launch ... -e AppleLanguages for localization.

-e is an environment variable pattern and does not reliably switch app language.

For this pipeline, use simulator-wide locale defaults per UDID. This works with

asc screenshots capture, which relaunches the app internally.

# Map each locale to a dedicated simulator UDID.
# (Create these simulators once with `xcrun simctl create`.)
declare -A LOCALE_UDID=(
  ["en-US"]="UDID_EN_US"
  ["de-DE"]="UDID_DE_DE"
  ["fr-FR"]="UDID_FR_FR"
  ["ja-JP"]="UDID_JA_JP"
)

set_simulator_locale() {
  local UDID="$1"
  local LOCALE="$2"            # e.g. de-DE
  local LANG="${LOCALE%%-*}"   # de
  local APPLE_LOCALE="${LOCALE/-/_}" # de_DE

  xcrun simctl boot "$UDID" || true
  xcrun simctl spawn "$UDID" defaults write NSGlobalDomain AppleLanguages -array "$LANG"
  xcrun simctl spawn "$UDID" defaults write NSGlobalDomain AppleLocale -string "$APPLE_LOCALE"
}

for LOCALE in "${!LOCALE_UDID[@]}"; do
  UDID="${LOCALE_UDID[$LOCALE]}"
  echo "Capturing $LOCALE on $UDID..."
  set_simulator_locale "$UDID" "$LOCALE"

  xcrun simctl terminate "$UDID" "com.example.app" || true
  asc screenshots capture \
    --bundle-id "com.example.app" \
    --name "home" \
    --udid "$UDID" \
    --output-dir "./screenshots/raw/$LOCALE" \
    --output json
done

If you launch manually (outside asc screenshots capture), use app launch arguments:

xcrun simctl launch "$UDID" "com.example.app" -AppleLanguages "(de)" -AppleLocale "de_DE"

7) Parallel execution for speed

Run one locale per simulator UDID in parallel:

#!/bin/bash
# parallel-capture.sh

declare -A LOCALE_UDID=(
  ["en-US"]="UDID_EN_US"
  ["de-DE"]="UDID_DE_DE"
  ["fr-FR"]="UDID_FR_FR"
  ["ja-JP"]="UDID_JA_JP"
)

capture_locale() {
  local LOCALE="$1"
  local UDID="$2"
  local LANG="${LOCALE%%-*}"
  local APPLE_LOCALE="${LOCALE/-/_}"

  echo "Starting $LOCALE on $UDID"
  xcrun simctl boot "$UDID" || true
  xcrun simctl spawn "$UDID" defaults write NSGlobalDomain AppleLanguages -array "$LANG"
  xcrun simctl spawn "$UDID" defaults write NSGlobalDomain AppleLocale -string "$APPLE_LOCALE"
  xcrun simctl terminate "$UDID" "com.example.app" || true

  asc screenshots capture \
    --bundle-id "com.example.app" \
    --name "home" \
    --udid "$UDID" \
    --output-dir "./screenshots/raw/$LOCALE" \
    --output json

  echo "Completed $LOCALE"
}

for LOCALE in "${!LOCALE_UDID[@]}"; do
  capture_locale "$LOCALE" "${LOCALE_UDID[$LOCALE]}" &
done

wait
echo "All captures done. Now framing..."

Or use xargs with locale:udid pairs:

printf "%s\n" \
  "en-US:UDID_EN_US" \
  "de-DE:UDID_DE_DE" \
  "fr-FR:UDID_FR_FR" \
  "ja-JP:UDID_JA_JP" | xargs -P 4 -I {} bash -c '
    PAIR="{}"
    LOCALE="${PAIR%%:*}"
    UDID="${PAIR##*:}"
    LANG="${LOCALE%%-*}"
    APPLE_LOCALE="${LOCALE/-/_}"
    xcrun simctl boot "$UDID" || true
    xcrun simctl spawn "$UDID" defaults write NSGlobalDomain AppleLanguages -array "$LANG"
    xcrun simctl spawn "$UDID" defaults write NSGlobalDomain AppleLocale -string "$APPLE_LOCALE"
    xcrun simctl terminate "$UDID" "com.example.app" || true
    asc screenshots capture --bundle-id "com.example.app" --name "home" --udid "$UDID" --output-dir "./screenshots/raw/$LOCALE" --output json
  '

8) Full multi-locale pipeline example

#!/bin/bash
# full-pipeline-multi-locale.sh

declare -A LOCALE_UDID=(
  ["en-US"]="UDID_EN_US"
  ["de-DE"]="UDID_DE_DE"
  ["fr-FR"]="UDID_FR_FR"
  ["es-ES"]="UDID_ES_ES"
  ["ja-JP"]="UDID_JA_JP"
)

DEVICE="iphone-air"
RAW_DIR="./screenshots/raw"
FRAMED_DIR="./screenshots/framed"

# Step 1: Parallel capture with per-simulator locale defaults
for LOCALE in "${!LOCALE_UDID[@]}"; do
  (
    UDID="${LOCALE_UDID[$LOCALE]}"
    LANG="${LOCALE%%-*}"
    APPLE_LOCALE="${LOCALE/-/_}"

    xcrun simctl boot "$UDID" || true
    xcrun simctl spawn "$UDID" defaults write NSGlobalDomain AppleLanguages -array "$LANG"
    xcrun simctl spawn "$UDID" defaults write NSGlobalDomain AppleLocale -string "$APPLE_LOCALE"
    xcrun simctl terminate "$UDID" "com.example.app" || true

    asc screenshots capture \
      --bundle-id "com.example.app" \
      --name "home" \
      --udid "$UDID" \
      --output-dir "$RAW_DIR/$LOCALE" \
      --output json
    echo "Captured $LOCALE"
  ) &
done
wait

# Step 2: Parallel framing
for LOCALE in "${!LOCALE_UDID[@]}"; do
  (
    asc screenshots frame \
      --input "$RAW_DIR/$LOCALE/home.png" \
      --output-dir "$FRAMED_DIR/$LOCALE" \
      --device "$DEVICE" \
      --output json
    echo "Framed $LOCALE"
  ) &
done
wait

# Step 3: Generate review (single run, aggregates all locales)
asc screenshots review-generate \
  --framed-dir "$FRAMED_DIR" \
  --output-dir "./screenshots/review"

# Step 4: Upload (run per locale if needed)
for LOCALE in "${!LOCALE_UDID[@]}"; do
  asc screenshots upload \
    --version-localization "LOC_ID_FOR_$LOCALE" \
    --path "$FRAMED_DIR/$LOCALE" \
    --device-type "IPHONE_65" \
    --output json
done

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

13k tokens
Capacity
by microsoft
vendor ×3

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

6k tokens scripts
Customize
by microsoft
vendor ×3

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

8k tokens
Deploy Model
by microsoft
vendor ×3

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

26k tokens scripts
Preset
by microsoft
vendor ×3

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

9k tokens
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take rorkai/asc-shots-pipeline from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.