Fetches axiom-build-debugging from charleswiltgen/axiom and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/axiom-build-debugging
Restart Cursor to activate axiom-build-debugging. Access via /axiom-build-debugging in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
pod 'Alamofire','5.8.0'# Exact versionpod 'SwiftyJSON','~> 5.0.0'# Any 5.0.x
SPM:
.package(url:"...", exact:"1.2.3")
Strategy 2: Use Version Ranges
When you want bug fixes but not breaking changes:
CocoaPods:
pod 'Alamofire','~> 5.8'# 5.8.x but not 5.9pod 'SwiftyJSON','>= 5.0','< 6.0'# Range
SPM:
.package(url:"...", from:"1.2.0")// 1.2.0 and higher.package(url:"...",.upToNextMajor(from:"1.0.0"))// 1.x.x but not 2.0.0
Strategy 3: Fork and Pin
When you need custom modifications:
# Fork repo on GitHub# Clone your forkgit clone https://github.com/yourname/package.git
# In Package.swift, use your fork.package(url: "https://github.com/yourname/package", branch: "custom-fixes")
Strategy 4: Exclude Transitive Dependencies
When a dependency's dependency conflicts:
SPM (not directly supported, use workarounds):
// Instead of this:.package(url:"https://github.com/problematic/package")// Fork it and remove the conflicting dependency from its Package.swift
CocoaPods:
# Exclude specific subspecspod 'Firebase/Core'# Not all of Firebasepod 'Firebase/Analytics'
Build Configuration Issues
Debug vs Release Differences
Symptom: Builds in Debug, fails in Release (or vice versa)
# For workspacexcodebuild -workspace YourApp.xcworkspace -scheme YourScheme build
# For project only (no CocoaPods)xcodebuild -project YourApp.xcodeproj -scheme YourScheme build
Pressure Scenarios: When to Resist "Quick Fix" Advice
The Problem
Under deadline pressure, senior engineers and teammates provide "quick fixes" based on pattern-matching:
"Just regenerate the lock file"
"Increment the build number"
"Delete DerivedData and rebuild"
These feel safe because they come from experience. But if the diagnosis is wrong, the fix wastes time you don't have.
Critical insight Time pressure makes authority bias STRONGER. You're more likely to trust advice when stressed.
Red Flags — STOP Before Acting
If you hear ANY of these, pause 5 minutes before executing:
❌ "This smells like..." (pattern-matching, not diagnosis)
❌ "Just..." (underestimating complexity)
❌ "This usually fixes it" (worked once ≠ works always)
❌ "You have plenty of time" (overconfidence about 24-hour turnaround)
❌ "This is safe" (regenerating lock files CAN break things)
Your brain under pressure Trusts these phrases because they sound confident. Doesn't ask "but do they have evidence THIS is the root cause?"
Mandatory Diagnosis Before "Quick Fix"
When someone senior suggests a fix under time pressure:
Step 1: Ask (Don't argue)
"I understand the pressure. Before we regenerate lock files,
can we spend 5 minutes comparing the broken build to our
working build? I want to know what we're fixing."
Step 2: Demand Evidence
"What makes you think it's a lock file issue?"
"What changed between our last successful build and this failure?"
"Can we see the actual error from App Store build vs our build?"
Step 3: Document the Gamble
If we try "pod install":
- Time to execute: 10 minutes
- Time to learn it failed: 24 hours (next submission cycle)
- Remaining time if it fails: 6 days
- Alternative: Spend 1-2 hours diagnosing first
Cost of being wrong with quick fix: High
Cost of spending 1 hour on diagnosis: Low
Step 4: Push Back Professionally
"I want to move fast too. A 1-hour diagnosis now means we
won't waste another 24-hour cycle. Let's document what we're
testing before we submit."
Why this works
You're not questioning their expertise
You're asking for evidence (legitimate request)
You're showing you understand the pressure
You're making the time math visible
Real-World Example: App Store Review Blocker
Scenario App rejected in App Store build, passes locally.
Senior says "Regenerate lock file and resubmit (7 days buffer)"
What you do
❌ WRONG: Execute immediately, fail after 24 hours, now 6 days left
✅ RIGHT: Spend 1 hour comparing builds first
Comparison checklist
Implementation Guide
Prerequisites
›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate into regular workflow if valuable
Common Pitfalls
⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation
Best Practices
✓ Do
+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns
✗ Don't
−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment
💡 Pro Tips
★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation
