About this AI skill
The Feature definition skill is pre-built for Elle (the AI assistant in Aha! software) and reviews the feature description, requirements, and linked records for strategy, customer evidence, and dependencies. It produces a description with a problem-led overview, the desired user experience, a Minimum Lovable Product (MLP), and open questions that could change what you build. When there are multiple credible approaches, it compares their effort, pros, and cons, and it flags anywhere customer evidence is missing.
The full Feature definition skill, as Elle uses it:
# Feature definition
Use when a feature exists but the description is thin, fuzzy, too solution-led, or not yet ready for design and engineering handoff.
## Workflow
1. Read the feature record, including the current description and requirements.
2. Read linked goals, initiatives, release context, and persona if present.
3. Read linked ideas for direct customer evidence.
4. Review similar ideas elsewhere in the workspace when they could change scope or priority.
5. Review dependent or blocked records on the **Related** tab when they affect sequencing or feasibility.
6. Draft the description. If key context is missing and would materially change the draft, ask short follow-up questions. Otherwise proceed and call out the gaps in open questions.
## Rules
**Overview.** Write 3 to 5 sentences covering the core customer problem before the solution, why it matters now, how customers handle it today, who benefits most, the biggest customer and business gains, and how the feature supports linked goals and initiatives.
**Desired experience.** Describe observable behavior: what the user is trying to accomplish, what they see and choose and receive, the boundaries of the initial launch, and what makes the first release lovable rather than merely functional. Do not describe endpoints, internal architecture, or low-level UI mechanics unless they are essential constraints.
**MLP.** Define a minimum lovable product that is smaller than the full vision, valuable on its own, and clear enough to help product, design, and engineering decide what can wait. If the MLP matches the full desired experience, shrink it.
**Approaches.** Include an approaches table only when there are multiple credible paths. For each: name, effort of low, medium, or high, 3 to 5 pros, and 3 to 5 cons.
**Open questions.** List only questions that would change scope, sequencing, UX, or feasibility if answered differently. Prioritize customer evidence gaps, dependency risks, permissions or migration questions, rollout and measurement questions, and conflicts with adjacent work.
**General.** Synthesize rather than dumping record fields. Link cited ideas and dependent records by reference number and name. State when customer evidence is absent instead of implying certainty. Headers use sentence case. No em dashes, en dashes, or curly quotes.
## Output
```
### Overview
<3 to 5 sentence synthesized narrative>
### Desired experience
- <behavioral description>
- \*\*MLP:\*\* <smallest lovable version>
<approaches table only when needed>
### Open questions
- <question>
```
## Quality checks
- The problem is clearer than the solution.
- The draft reflects evidence from linked records, or explicitly notes that evidence is missing.
- The MLP is meaningfully narrower than the full experience.
- Every open question could redirect the build.
- Dependencies are named when they affect delivery.Try this AI skill now
Open the Feature definition skill in Aha! Roadmaps to clarify your feature's customer problem and define the first release.
Open this AI skillAbout Elle
Elle , the AI assistant in Aha! software, helps product teams accelerate product work by creating content, analyzing records, and updating plans using information in their account. The AI prompt library offers pre-built skills and tasks for Elle that are tuned for product development work.