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Quality Dimensions — Quick Reference
Six dimensions to keep in mind when building agent skills. The quality scanners check these automatically during optimization — this is a mental checklist for the build phase.
1. Informed Autonomy
The executing agent needs enough context to make judgment calls when situations don't match the script. The Overview section establishes this: domain framing, theory of mind, design rationale.
- Simple agents with 1-2 capabilities need minimal context
- Agents with memory, autonomous mode, or complex capabilities need domain understanding, user perspective, and rationale for non-obvious choices
- When in doubt, explain why — an agent that understands the mission improvises better than one following blind steps
2. Intelligence Placement
Scripts handle plumbing (fetch, transform, validate). Prompts handle judgment (interpret, classify, decide).
Test: If a script contains an if that decides what content means, intelligence has leaked.
Reverse test: If a prompt validates structure, counts items, parses known formats, compares against schemas, or checks file existence — determinism has leaked into the LLM. That work belongs in a script. Scripts have access to full bash, Python with standard library plus PEP 723 dependencies, and system tools — think broadly about what can be offloaded.
3. Progressive Disclosure
SKILL.md stays focused. Detail goes where it belongs.
- Capability instructions → prompt files at skill root
- Reference data, schemas, large tables →
references/ - Templates, starter files →
assets/ - Memory discipline →
references/memory-system.md - Multi-capability SKILL.md under ~250 lines: fine as-is
- Single-purpose up to ~500 lines: acceptable if focused
4. Description Format
Two parts: [5-8 word summary]. [Use when user says 'X' or 'Y'.]
Default to conservative triggering. See references/standard-fields.md for full format and examples.
5. Path Construction
Only use {project-root} for _bmad paths. Config variables used directly — they already contain {project-root}.
See references/standard-fields.md for correct/incorrect patterns.
6. Token Efficiency
Remove genuine waste (repetition, defensive padding, meta-explanation). Preserve context that enables judgment (domain framing, theory of mind, design rationale). These are different things — the prompt-craft scanner distinguishes between them.