Dynamic Runtime in AI Skill Design

Most AI skills are static artifacts. A markdown file with instructions. A set of tools with fixed capabilities. A prompt template with predefined variables. They work well for predictable tasks but struggle when the environment varies, when requirements emerge during execution, or when the skill needs capabilities that weren't anticipated at design time.

Dynamic runtime techniques transform static skills into adaptive ones. A skill that discovers available tools at runtime and adjusts its behavior based on what's available. A skill that loads additional context when it encounters an unfamiliar code pattern. A skill that modifies its own instructions based on feedback from previous executions.

These techniques are at the frontier of AI skill design. They're more complex to build, harder to debug, and require more trust from the user. But they enable skill capabilities that static designs cannot match.

Key Takeaways

Dynamic dispatch lets skills choose tools and strategies at runtime based on the current environment
Reflection enables skills to inspect their own capabilities and adjust behavior accordingly
Hot-reloading allows skill modifications without restarting the agent session
Adaptive prompting modifies skill instructions based on task characteristics and past performance
Capability negotiation between skills enables composition that wasn't designed at build time

Static vs. Dynamic Skills

A static skill's behavior is determined entirely at design time. The instructions are written once. The tool list is fixed. The prompt templates are predefined. Every execution follows the same pattern, regardless of context.

A dynamic skill's behavior is influenced by runtime context. It might:

Check which tools are available and adjust its approach accordingly
Load additional domain knowledge when it encounters unfamiliar patterns
Modify its verbosity based on the user's apparent expertise level
Select different strategies based on the size and complexity of the input
Learn from previous executions and avoid approaches that previously failed

The tradeoff is clear. Static skills are predictable, debuggable, and easy to understand. Dynamic skills are flexible, powerful, and harder to reason about. Most production skills should be static with selective dynamic elements, not fully dynamic.

Dynamic Dispatch Patterns

Dynamic dispatch in AI skills means choosing the execution strategy at runtime rather than at design time.

Tool-Based Dispatch

A skill that performs code analysis might support multiple languages. Instead of hardcoding language-specific logic, it dynamically dispatches based on the file type:

For each file:
  1. Detect language from extension and content
  2. Load the language-specific analysis prompt
  3. Apply language-specific quality criteria
  4. Format output using language-specific conventions

The skill's core logic (analyze code quality) remains constant. The language-specific details are loaded dynamically. This enables the skill to support new languages by adding new language modules without modifying the core skill.

Strategy-Based Dispatch

A skill that handles various task sizes might switch strategies:

For small inputs (< 100 lines): analyze directly in a single pass
For medium inputs (100-1000 lines): break into sections and analyze each
For large inputs (> 1000 lines): use sampling-based analysis

The strategy selection happens at runtime based on the input. The skill is a single artifact that adapts its approach to the task at hand.

Environment-Based Dispatch

A skill that runs across different platforms might adjust its tool usage:

If git is available: use git-based file analysis
If git is not available: use direct file reading
If running in CI: optimize for speed and structured output
If running interactively: optimize for detailed explanations

The skill probes its environment and adapts. This makes it portable across different setups without requiring separate skill versions for each environment.

Reflection Techniques

Reflection means a skill can inspect its own state and capabilities. In AI skill design, reflection enables:

Self-assessment. The skill evaluates whether it's the right tool for the current task. A code review skill might reflect: "This appears to be a configuration file, not source code. My review criteria are designed for source code. I should note this limitation."

Capability reporting. The skill describes what it can and cannot do based on its current configuration. This is useful for skill composition: another skill can query this one's capabilities before delegating work.

Performance tracking. The skill monitors its own execution metrics (time per analysis, error rate, correction frequency) and adjusts behavior to improve. A skill that notices it frequently triggers false positives on a specific pattern can learn to suppress that pattern.

Hot-Reloading

Hot-reloading means modifying a skill while it's in use without restarting the agent session. This is valuable during skill development: you edit the skill file, and the next invocation uses the updated version.

For Claude Code skills stored as SKILL.md files, hot-reloading is straightforward because the skill is reloaded from disk on each invocation. File-based skills are inherently hot-reloadable.

For more complex skills that involve compiled code, tool registrations, or cached state, hot-reloading requires explicit support:

File watchers detect changes to skill source files
Changed components are reloaded while preserving session state
Tool registrations are updated to reflect new capabilities
Cached data is invalidated when the skill logic changes

Hot-reloading accelerates skill development cycles. Instead of edit-restart-test, the cycle becomes edit-test. For teams developing custom skills, this can save hours per day.

Adaptive Prompting

The most powerful dynamic runtime technique is adaptive prompting: the skill modifies its own instructions based on context.

Context-Based Adaptation

A skill adjusts its instructions based on what it discovers about the project:

If the project uses TypeScript: "Apply TypeScript-specific type safety patterns"
If the project has no tests: "Note the absence of tests and recommend test additions"
If the project is a library: "Focus on public API quality and backward compatibility"

The skill reads the project context (package.json, tsconfig.json, file structure) and augments its base instructions with context-specific guidance.

Feedback-Based Adaptation

A skill adjusts based on user feedback from previous executions:

If the user frequently dismisses a specific category of suggestions: reduce the weight of that category
If the user frequently requests more detail on a topic: increase detail in future analyses
If the user corrects the skill's output: incorporate the correction into future prompts

This creates a skill that improves with use. The improvement is per-user, adapting to individual preferences and workflows. For more on how skills evolve, see Hidden Commands and Opinionated Workflows.

Complexity-Based Adaptation

A skill adjusts its thoroughness based on input complexity:

Simple inputs get concise responses
Complex inputs get detailed analysis with step-by-step reasoning
Ambiguous inputs trigger clarifying questions before proceeding

The skill estimates complexity from input characteristics (length, structure, domain vocabulary) and adjusts its approach accordingly.

Capability Negotiation

When skills compose (one skill delegates to another), dynamic runtime enables capability negotiation. The delegating skill queries the target skill's capabilities before delegating:

"Can you analyze Python code? Do you support async patterns? Can you produce output in SARIF format?"

Based on the answers, the delegating skill either proceeds with delegation, chooses a different target, or adjusts its request to match the target's capabilities.

This negotiation enables loose coupling between skills. Skills don't need to know about each other at design time. They discover each other's capabilities at runtime and compose dynamically.

The skill composability patterns article covers static composition. Dynamic composition through capability negotiation is the runtime extension of those patterns. Together with the method interception patterns, these techniques form the foundation of advanced skill architectures.

Debugging Dynamic Skills

Dynamic behavior is harder to debug than static behavior because the execution path varies between runs. Strategies for debugging dynamic skills:

Comprehensive logging. Log every dynamic decision: which strategy was selected, which capabilities were detected, which adaptations were applied. When behavior is unexpected, the logs reveal which decision produced it. See Unified Logging for AI Workflows for structured logging approaches.

Decision replay. Save the context that drove each dynamic decision. Replay the decision with the same context to verify it produces the expected outcome. This enables deterministic debugging of non-deterministic behavior.

Fallback to static. Provide a flag that disables all dynamic behavior and runs the skill in static mode. If the static mode works correctly, the bug is in the dynamic logic. If it also fails, the bug is in the core skill.

FAQ

Are dynamic skills harder to review for security?

Yes. Dynamic behavior means the skill's actions depend on runtime context, making static analysis insufficient. Security review requires understanding the range of possible behaviors, not just the literal code. For security-sensitive applications, constrain dynamic behavior to safe bounds.

How do dynamic skills interact with Claude Code's permission model?

Dynamic skills must request permissions for all possible tool uses, not just the tools they plan to use in the current execution. Since the tool selection happens at runtime, the permission must cover the full range. This is an area where the Claude Code permission model intersects with dynamic skill design.

Should all skills be dynamic?

No. Most skills should be static. Dynamic behavior adds complexity that is only justified when the skill truly needs to adapt to varying contexts. A code formatting skill that always applies the same rules should be static. A code analysis skill that handles multiple languages and project types benefits from dynamic dispatch.

Can dynamic skills cause infinite loops?

If a skill modifies its own behavior and the modification triggers further modification, yes. Guard against this with recursion limits, change budgets (maximum number of adaptations per execution), and explicit base cases that force static behavior.

Sources

Explore production-ready AI skills at aiskill.market/browse or submit your own skill to the marketplace.