Challenges & Solutions
Running third-party code inside AI agents introduces real risks. Understanding these challenges helps you deploy skills safely.
Safety Concerns
Skills are instructions that agents follow—but who wrote those instructions? A malicious skill could instruct an agent to:
- Exfiltrate sensitive data (API keys, credentials, source code)
- Execute malicious commands on your system
- Modify files in unexpected ways
- Make network requests to unknown endpoints
How We Address This
- •Automated security scanning — Every skill is analyzed for dangerous patterns
- •Secret detection — Flagging exposed credentials before deployment
- •Permission auditing — Clear visibility into what capabilities a skill requires
- •Publisher verification — Trusted publishers get verification badges
Harness Compatibility
Not every skill works on every harness. Common compatibility issues:
Tool Requirements
A skill requiring shell access won't work on a sandboxed harness. Skills needing browser automation require harnesses with that capability.
Skill Loading Paths
Different harnesses look for skills in different directories. Some use ~/.config/skills/, others use .claude/skills/ or custom paths.
Model Capabilities
A skill designed for a model with 200K context may fail on a harness using a 32K model.
How We Address This
- •Playground testing — Try skills across multiple harnesses before deploying
- •Compatibility badges — See which harnesses a skill has been tested on
- •Spec compliance checking — Skills following the standard are more portable
Token Consumption
Skills consume context window space. Every skill loaded means less room for your actual task. This creates real problems:
Context Overflow
Loading too many skills at once can exhaust the context window, leaving no room for the actual work.
Cost Implications
More tokens = higher API costs. Large skills loaded unnecessarily waste money on every request.
Performance Impact
Larger contexts mean slower responses. Keeping skills lean improves user experience.
Attention Dilution
More content = more places for the model's attention to wander. Focused context leads to better results.
Progressive Disclosure
The solution is progressive disclosure—loading skill content only when needed:
Only skill names and descriptions are loaded at startup (tiny footprint)
Full skill content is loaded only when the model detects relevance
Skill instructions apply only to the relevant portion of the task
Key Takeaways
- Always review skills before installing—check the safety score and permissions
- Test skills in the Playground before deploying to production
- Prefer skills from verified publishers
- Keep skill descriptions focused—good descriptions enable better progressive disclosure