
Compare Cache Runs
- 223 installs
- 34 repo stars
- Updated March 27, 2026
- tuist/agent-skills
Helps with ai & agent building tasks.
About
compare-cache-runs is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- compare-cache-runs
- AI & Agent Building
- AI-coding skill
Compare Cache Runs by the numbers
- 223 all-time installs (skills.sh)
- +7 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #2,729 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 223 |
|---|---|
| repo stars | ★ 34 |
| Last updated | March 27, 2026 |
| Repository | tuist/agent-skills ↗ |
What it does
Helps with ai & agent building tasks.
Files
Compare Cache Runs
Quick Start
You'll typically receive two cache run identifiers. Follow these steps:
1. Run tuist cache-run list --json to find cache runs on each branch. 2. Run tuist cache-run show <id> --json for both base and head cache runs. 3. Compare duration, status, and cache hit rates. 4. Summarize cache changes with root cause analysis.
Step 1: Resolve Cache Runs
If base/head are cache run IDs or dashboard URLs
Fetch each directly:
tuist cache-run show <base-id> --json
tuist cache-run show <head-id> --jsonIf base/head are branch names
List recent cache runs on each branch:
tuist cache-run list --git-branch <base-branch> --json --page-size 1
tuist cache-run list --git-branch <head-branch> --json --page-size 1Then fetch full details with tuist cache-run show <id> --json.
Defaults
- If no base is provided, use the project's default branch (usually
main). - If no head is provided, detect the current git branch.
Step 2: Compare Top-Level Metrics
After fetching both cache runs, compare:
| Metric | What to check |
|---|---|
duration | Flag if head is >10% slower |
status | Flag if base succeeded but head failed |
cacheable_targets | Note if target count changed |
local_cache_target_hits | Compare local hit counts |
remote_cache_target_hits | Compare remote hit counts |
is_ci | Note if one is CI and the other local |
Compute cache hit rates:
- Base:
(local_hits + remote_hits) / cacheable_targets * 100 - Head: same formula
- Delta:
head_rate - base_rate
Step 3: Analyze Cache Invalidation
If cache hit rate dropped, the key question is: which target(s) caused the invalidation cascade?
Cache invalidation in tuist cache works the same way as in tuist generate: 1. A "root cause" target has a direct change (source file modified, build setting changed, etc.) 2. All targets that depend on the root cause target also get invalidated because their dependencies hash changes. 3. This cascade can invalidate many targets from a single root change.
Identifying root cause targets
Without the module cache target detail endpoint (available via MCP), use these heuristics:
1. Check git diff between the base and head commits to see which files changed. 2. Map changed files to their Tuist targets/modules. 3. Targets with direct source changes are likely root causes. 4. Targets that only changed due to dependency hash cascading are secondary invalidations.
Common root causes of cache invalidation
| Cause | Description |
|---|---|
| Source changes | Files in the target's source directory were modified |
| Resource changes | Assets, XIBs, storyboards, or other resources changed |
| Build settings | Target or project build settings were modified |
| Dependency changes | An external dependency version changed |
| Info.plist changes | The target's Info.plist was modified |
| Entitlements changes | The entitlements file was modified |
| Deployment target | The minimum deployment target changed |
| Headers | Public or project headers changed |
| Project settings | Shared project-level settings changed |
Step 4: Assess Impact
Categorize the cache invalidation:
- Expected: Source files were intentionally changed, causing expected cache misses.
- Unexpected: No source changes but cache was invalidated (build settings, Xcode version, etc.).
- Cascade: A small change invalidated many downstream targets.
For tuist cache specifically, also consider:
- Cache warming strategy: Was the cache run warming from scratch or incrementally?
- The
command_argumentsfield can reveal if different flags were used.
Summary Format
Produce a summary with:
1. Overall verdict: Cache hit rate improved, regressed, or stable. 2. Cache hit rate: Base rate vs head rate with delta. 3. Duration: Absolute and percentage change. 4. Root cause targets: Which targets had direct changes. 5. Cascade impact: How many targets were invalidated due to dependency cascading. 6. Recommendations: How to minimize cache invalidation.
Example:
Cache Run Comparison: base (run-123 on main) vs head (run-456 on feature-x)
Duration: 95.2s -> 142.8s (+50%) -- REGRESSION
Cache hit rate: 88% (44/50) -> 60% (30/50) (-28%) -- REGRESSION
Status: success -> success
Root cause: CoreModule had source changes (5 files modified).
This cascaded to 14 downstream targets that depend on CoreModule.
Cache invalidation breakdown:
- Direct changes: CoreModule (sources changed)
- Cascade: UIKit, Networking, Analytics, + 11 others (dependency hash changed)
Recommendations:
- Consider splitting CoreModule into smaller, more focused modules
- Use interface/implementation module pattern for frequently-changed modules
- Run `tuist cache` on the feature branch after rebasing to warm cachesDone Checklist
- Resolved both base and head cache runs
- Compared duration and cache hit rates
- Identified root cause targets for cache invalidation
- Analyzed cascade impact
- Provided actionable recommendations for reducing cache misses