---
title: "A Field Guide to Fable: Finding Your Unknowns"
tags:
  - wiki
  - sources
sources:
  - "[[A Field Guide to Fable: Finding Your Unknowns]]"
domains: [agentic, skills]
date: "2026-07-17T06:22:35.073Z"
created: "2026-07-17T06:22:35.073Z"
includeInRss: false
---

# A Field Guide to Fable: Finding Your Unknowns

## Takeaways
- Prompts/skills/context are the **map**; the codebase and real constraints are the **territory**—gaps between them are **unknowns** where the agent guesses.
- With Fable-class models, output quality is often bottlenecked by how well you surface and resolve unknowns, not raw model capability.
- Taxonomy: known knowns (in prompt), known unknowns (aware gaps), unknown knowns (tacit taste), unknown unknowns (blind spots).
- Unknowns appear before, during, and after implementation; upfront planning alone does not eliminate them.
- Techniques include blind-spot passes, HTML prototypes for taste, structured interviews, and post-implementation retros—pick by unknown type.
- Top agentic coders reduce unknowns by staying in sync with both codebase and model behavior; over-specifying vs. vagueness both fail when unknowns are unexamined.

## Notes
Directly applicable to vault agent work: clarifying unknowns in routing rules, skill boundaries, and "what good looks like" is the leverage point for reliable `/kb-triage`, `/clip`, and coding sessions—not more prompt volume.

## Open questions
- Which unknown-discovery patterns belong as first-class vault skills vs. one-off session prompts?