Use this skill when you are improving how an agent plans, calls tools, recovers from errors, and converges on completion.
Core Model
Agent output quality is constrained by:
Action space quality
Observation quality
Recovery quality
Context budget quality
Action Space Design
Use stable, explicit tool names.
Keep inputs schema-first and narrow.
Return deterministic output shapes.
Avoid catch-all tools unless isolation is impossible.
Granularity Rules
Use micro-tools for high-risk operations (deploy, migration, permissions).
Use medium tools for common edit/read/search loops.
Use macro-tools only when round-trip overhead is the dominant cost.
Observation Design
Every tool response should include:
status
success|warning|error
summary
one-line result
next_actions
actionable follow-ups
artifacts
file paths / IDs
Error Recovery Contract
For every error path, include:
root cause hint
safe retry instruction
explicit stop condition
Context Budgeting
Keep system prompt minimal and invariant.
Move large guidance into skills loaded on demand.
Prefer references to files over inlining long documents.
Compact at phase boundaries, not arbitrary token thresholds.
Architecture Pattern Guidance
ReAct: best for exploratory tasks with uncertain path.
Function-calling: best for structured deterministic flows.
Hybrid (recommended): ReAct planning + typed tool execution.
Benchmarking
Track:
completion rate
retries per task
pass@1 and pass@3
cost per successful task
Anti-Patterns
Too many tools with overlapping semantics.
Opaque tool output with no recovery hints.
Error-only output without next steps.
Context overloading with irrelevant references.