fa7e166f3e
- Add reminders page with navigation support - Upgrade BMad builder module to skills-based architecture - Refactor MCP server: extract tools and auth into separate modules - Add connections cache, custom AI provider support - Update prisma schema and generated client - Various UI/UX improvements and i18n updates - Add service worker for PWA support Made-with: Cursor
54 lines
3.0 KiB
Markdown
54 lines
3.0 KiB
Markdown
# Quality Dimensions — Quick Reference
|
|
|
|
Seven dimensions to keep in mind when building skills. The quality scanners check these automatically during quality analysis — this is a mental checklist for the build phase.
|
|
|
|
## 1. Outcome-Driven Design
|
|
|
|
Describe what to achieve, not how to get there step by step. Only add procedural detail when the LLM would genuinely fail without it.
|
|
|
|
- **The test:** Would removing this instruction cause the LLM to produce a worse outcome? If the LLM would do it anyway, the instruction is noise.
|
|
- **Pruning:** If a block teaches the LLM something it already knows — scoring algorithms for subjective judgment, calibration tables for reading the room, weighted formulas for picking relevant participants — cut it. These are things LLMs do naturally.
|
|
- **When procedure IS value:** Exact script invocations, specific file paths, API calls with precise parameters, security-critical operations. These need low freedom because there's one right way.
|
|
|
|
## 2. Informed Autonomy
|
|
|
|
The executing agent needs enough context to make judgment calls when situations don't match the script. The Overview establishes this: domain framing, theory of mind, design rationale.
|
|
|
|
- Simple utilities need minimal context — input/output is self-explanatory
|
|
- Interactive/complex workflows 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
|
|
|
|
## 3. 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.
|
|
|
|
## 4. Progressive Disclosure
|
|
|
|
SKILL.md stays focused. Detail goes where it belongs.
|
|
|
|
- Stage instructions → `./references/`
|
|
- Reference data, schemas, large tables → `./references/`
|
|
- Templates, config files → `./assets/`
|
|
- Multi-branch SKILL.md under ~250 lines: fine as-is
|
|
- Single-purpose up to ~500 lines (~5000 tokens): acceptable if focused
|
|
|
|
## 5. 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.
|
|
|
|
## 6. 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.
|
|
|
|
## 7. 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 — never trade effectiveness for efficiency. A skill that works correctly but uses extra tokens is always better than one that's lean but fails edge cases.
|