| name | confidence-aware-planning |
| description | Plan differently when the agent has low certainty — ask for clarification or narrow scope instead of pretending full understanding. |
| origin | FlowDeck |
Confidence-Aware Planning
Not every task comes with complete information. This skill enforces honest uncertainty signaling and adaptive planning when confidence is low.
Confidence Levels
| Level | Meaning | Action |
|---|
| HIGH (≥80%) | Well-understood scope, clear precedent in codebase | Proceed to plan normally |
| MEDIUM (40–79%) | Partial understanding, some unknowns | Surface assumptions, narrow scope, flag for review |
| LOW (<40%) | Significant unknowns, no clear precedent | Ask clarifying questions first, do not plan until answered |
Signals That Lower Confidence
- Codebase section not covered in
.codebase/ARCHITECTURE.md
- No prior task.md for this feature area
- Request touches 5+ files with unclear dependencies
- Request uses domain jargon that doesn't appear in codebase
- No test coverage in the affected area (no test files found)
Workflow
Before planning ANY task:
- Read relevant codebase docs (ARCHITECTURE.md, STACK.md, CONVENTIONS.md)
- Scan affected files for context
- Estimate confidence level
- Act based on level:
- HIGH: proceed to
/fd-review
- MEDIUM: write explicit assumptions at the top of PLAN.md, flag 3 highest risks
- LOW: stop, ask clarifying questions, do not write PLAN.md until answered
Clarifying Question Format
When confidence is LOW, ask in this format:
Before I can plan this, I need to understand:
1. [Question about scope/behavior]
2. [Question about constraint or requirement]
3. [Question about existing system behavior]
I have LOW confidence because: [specific reason]
Assumption Declaration Format
When confidence is MEDIUM, include at the top of every plan:
## Assumptions (MEDIUM confidence)
- A1: [assumption] — if wrong, [consequence]
- A2: [assumption] — if wrong, [consequence]
## Risks
1. [risk]: [mitigation]
Non-negotiable
Never write a plan that pretends HIGH confidence when the agent actually has LOW confidence. False certainty leads to wrong implementations and wasted effort.
